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-Supervised Feature Selection Techniques in
-Network Intrusion Detection: a Critical Review
-M. Di Mauroa,<, G. Galatrob, G. Fortinocand A. Liottad
-aDepartment of Information and Electrical Engineering and Applied Mathematics (DIEM), University of Salerno, 84084, Fisciano, Italy
-bAmazon AWS, Belgard Retail Park, Tallaght, Dublin, Ireland
-cDepartment of Informatics, Modeling, Electronics and Systems, University of Calabria, Italy
-dFaculty of Computer Science, Free University of Bozen-Bolzano, Italy
-ARTICLE INFO
-Keywords :
-Feature Selection
-Machine Learning
-Network Intrusion Detection
-Network PerformanceABSTRACT
-Machine Learning (ML) techniques are becoming an invaluable support for network intrusion de-
-tection, especially in revealing anomalous flows, which often hide cyber-threats. Typically, ML al-
-gorithms are exploited to classify/recognize data traffic on the basis of statistical features such as
-inter-arrival times, packets length distribution, mean number of flows, etc. Dealing with the vast
-diversity and number of features that typically characterize data traffic is a hard problem. This re-
-sults in the following issues: i)the presence of so many features leads to lengthy training processes
-(particularly when features are highly correlated), while prediction accuracy does not proportionally
-improve; ii)some of the features may introduce bias during the classification process, particularly
-those that have scarce relation with the data traffic to be classified. To this end, by reducing the fea-
-ture space and retaining only the most significant features, Feature Selection (FS) becomes a crucial
-pre-processing step in network management and, specifically, for the purposes of network intrusion
-detection. In this review paper, we complement other surveys in multiple ways: i)evaluating more
-recentdatasets(updatedw.r.t. obsoleteKDD 99)bymeansofadesigned-from-scratchPython-based
-procedure; ii)providingasynopsisofmostcreditedFSapproachesinthefieldofintrusiondetection,
-includingMulti-ObjectiveEvolutionarytechniques; iii)assessingvariousexperimentalanalysessuch
-as feature correlation, time complexity, and performance. Our comparisons offer useful guidelines
-to network/security managers who are considering the incorporation of ML concepts into network
-intrusion detection, where trade-offs between performance and resource consumption are crucial.
-1. Introduction
-With the rapid growth of digital technology and com-
-munications, we are overwhelmed by network data traffic,
-whicharediverseformediatype(e.g. video,voice,text,sen-
-sory,etc.),andoriginatefrom(andaretransportedthrough)
-abroadrangeofsources(e.g. mobilenetworks,cloudinfras-
-tructures,InternetofThings,etc.). Consequently,wehandle
-high-dimensionality data, calling for increasingly more so-
-phisticated classification methods [1, 2].
-Typically,werefertohighdimensionalitywhenwedeal
-with data whereby a large number of features may be ex-
-tracted, to the point that the features may even exceed the
-numberofobservations. Thisleadstomajorissues,particu-
-larly the massive increase in training times.
-To this end, Feature Selection (FS) is a promising re-
-searchdirection,lookingatwaystoreducethefeaturespace
-in order to pinpoint only the most significant features. As
-a fundamental pre-processing step in machine learning, FS
-is gaining prominence in network management and, specif-
-ically, for the purposes of network intrusion detection and
-network traffic classification problems [3, 4, 5, 6].
-Moregenerally,FSfindsanevenmuchbroaderapplica-
-bilityinfieldasdiverseasbioinformatics[7,8,9,10],image
-recognition/retrieval [11, 12, 13, 14, 15, 16, 17], fault diag-
-<Corresponding author
-mdimauro@unisa.it (M. Di Mauro); galatrogiovanni@gmail.com (G.
-Galatro); g.fortino@unical.it (G. Fortino); antonio.liotta@unibz.it (A.
-Liotta)nosis[18,19],textmining[20,21,22]and,interestingly, in
-network traffic analysis/classification, whose pertinent bib-
-liography will be covered more closely in the following.
-Machinelearningenginescanbeeasilyembeddedinnet-
-work intrusion detection systems (IDS), which represent an
-essential part of network infrastructures to guarantee secu-
-rity [23, 24, 25] and availability [26, 27, 28, 29, 30, 31, 32,
-33, 34]. Specifically, modern NIDS can be equipped with
-softwareprobesinchargeofanalyzingnetworktrafficonthe
-basis of some characterizing features such as: distribution
-of inter-arrival times, distribution of packet sizes, presence
-of specific TCP/IP flags, percentage of forward/backward
-flows. Suchstatisticalinformationisinstrumentaltoreveal-
-inganomaloustraffic,whichisoftenbehindDistributedDe-
-nial of Service (DDoS) attacks [35, 36], covert Voice-over-
-IPsessions[37],threatdiffusion[38]andPeer-2-Peertraffic
-[39, 40, 41]. In many cases, these flows would pass unob-
-servedthroughotherconventionalsignature-basedanalyses.
-In principle, a larger number of features would allow to
-perform more granular analyses; yet, two main drawbacks
-emerge. First, a proliferation of correlated features leads to
-levels of redundancy that are useless, while resulting in in-
-creasingly longer training times. Also, not all features are
-valuable in characterizing traffic, incurring bias during the
-classification step.
-Ifweconsider,asanexample,somenovelprobessuchas
-ISCXFlowMeter[42],thesecanactuallygeneratemorethan
-80features to characterize data traffic, which makes it ex-
-Di Mauro et al.: Preprint submitted to Elsevier Page 1 of 19arXiv:2104.04958v1  [cs.CR]  11 Apr 2021Supervised Feature Selection Techniques in Network Intrusion Detection: a Critical Review
-tremelydifficultforthenetworkanalysttodealwiththisvast
-amount of information. This is where an FS pre-processing
-step becomes invaluable.
-Across the scientific literature, many works are devoted
-to surveying machine learning algorithms with applications
-tointernettrafficclassification. Yet,fewattemptshavebeen
-made in the field of FS techniques, where two main short-
-comings emerge: i)many works analyze and compare FS
-algorithms applied to non-specific datasets [43, 44, 45]; ii)
-areeitherbasedonoutdateddatasets,orlacktheexperimen-
-taldimension. Overall,notmuchnovelalgorithmshavebeen
-compared(refertodetailsprovidedinTable 1,discussedfur-
-ther ahead). Aimed at filling these gaps, our paper provides
-the main following contributions:
-•We perform an experimental analysis of more re-
-centdatasets(classicliteraturefocusesonthe 20-year
-old KDD 99dataset) by means of our designed-from-
-scratchPythonbasedroutine. Thisallowsusto i)per-
-form cleaning, re-balancing, and data mixing opera-
-tions,and ii)automaticallyinteractwithspecificML-
-based engines.
-•We present a variety of experimental results (in-
-cluding: feature correlation, time complexity, perfor-
-mance) aimed at critically comparing selected fam-
-ilies of FS algorithms, ranging from classic ones
-(rank search, linear forwarding selection) to modern
-bio-inspired algorithms (genetic search, ant colonies,
-multi-objectiveevolutionary),thusgoingwellbeyond
-a conventional literature survey.
-Our experimental-based, comparative assessment finds
-fruitfulapplicationsinthefieldofnetwork/securitymanage-
-ment, where machine learning techniques are proved to of-
-feraprecioussupporttointrusiondetectiontasks,andwhere
-criticaltrade-offsbetweenperformanceclassificationandre-
-source consumption arise.
-Thepaperisorganizedasfollows: Section 2offersagen-
-eralperspectiveonFSmethods,inlinewithotheraccredited
-taxonomies. In Section 3we analyze how diverse FS tech-
-niqueshavebeenappliedintheliterature,oftenbasedonthe
-oldKDD 99dataset. Section 4proposesan excursusofpop-
-ular FS algorithms (classic and modern) along with some
-necessary technical details. In Section 5we analyze the ex-
-ploitednoveldatasetsbygroupingthemostrelevantfeatures
-in families. In Section 6we perform an experimental-based
-comparative analysis of prominent FS algorithms, provid-
-ing performance, feature correlation, and time-complexity
-figures. Finally, Section 7draws conclusions and provides
-future-direction indications.
-2. Overview of Feature Selection
-Feature Selection refers to that set of techniques and
-strategies that allow to optimize the feature space, namely,
-ann-dimensionalspacewhereeachsampleisrepresentedas
-a point. When dealing with large feature spaces, the anal-
-ysis of data, which starts from their representative features,can be tremendously time and resource consuming. Hence,
-the need to devise suitable FS strategies aimed at eliminat-
-ingi/irrelevant features, namely, those features that are not
-actually needed to build an optimal feature subset; and ii/
-redundant features, namely, those features that strongly de-
-pend on other features [46].
-Feature selection can be considered as a special case of
-feature extraction methods [47]. The latter refer to a set of
-techniques (e.g. PCA, Single Value Decomposition, Linear
-Discriminant Analysis and others) useful to transform the
-original feature space in a new one aimed at alleviating the
-effectsofthenotorious curseofdimensionality problem[48,
-49]. Itisimportanttonotethatfeatureextractioncomeswith
-thecriticalriskofobtainingatransformedfeaturespacethat
-couldloseitsoriginalphysicalmeaning,whereasclassicFS
-aims at preserving it [50, 51].
-A more formal definition of the FS problem follows.
-Given a feature set X= ^xi:i= 1;§;N`, find a subset
-SK= ^xi1;xi2;§;xiK`withK < N, where an objective
-function Y() is optimized, namely:
-^xi1;xi2;§;xiK` = arg max
-K;ik[Y^xi:i= 1;§;N`]:(1)
-Unlessotherwisestated,theproblemistoselecttheoptimal
-subsetoffeatures(accordingtoaspecificcriterion)fromthe
-initialset,wheretwostepsaretypicallyperformed: thefirst
-oneinvolvesasearchstrategytopinpointcandidatesubsets;
-thesecondoneinvolvesanobjectivefunctiontoevaluatethe
-selected candidate subsets. The latter can be split in two
-types [52, 53, 54, 55]: i/filters, referring to objective func-
-tions that rely on properties of the data by evaluating the
-information content (e.g. correlation measures, inter-class
-distance);ii/wrappers, referring to objective functions that
-exploittrainingmodelsbystartingfromasubsetoffeatures
-and,then,addingorremovingfeaturesbasedontheprevious
-model.
-A commonly accepted taxonomy of FS methods is pre-
-sented in [56], where three classic approaches have been
-identifiedas supervised ,unsupervised ,andsemi-supervised .
-According to the supervised approach, labeled data are ex-
-ploitedtosingleoutafeaturesubset,consideringspecificcri-
-teriaformeasuringthefeaturesimportance. Conversely,un-
-supervisedtechniquesseektounveiltheintrinsicdatastruc-
-turetoselectthemostsignificantfeatures,withoutassuming
-anya prioriknowledge [57].
-Finally, the semi-supervised approach is based on a
-mixedstrategy,strivingtoenrichanunlabeledsetwithsome
-labeled data, so as to improve the FS phase. Both the un-
-supervised and the semi-supervised approaches exhibit the
-drawback of neglecting potential correlations among fea-
-tures, resulting in the analysis of sub-optimal sets. This
-may prove critical when dealing with traffic analysis, where
-we need to take into account statistical-based features (e.g.
-inter-arrivaltimesvariance,averagepacketlength,etc.) and
-deterministic ones (e.g. IP addresses, port numbers, etc.).
-Let us consider, for example, a particular kind of traffic di-
-rected towards a fixed destination port for a certain period
-Di Mauro et al.: Preprint submitted to Elsevier Page 2 of 19Supervised Feature Selection Techniques in Network Intrusion Detection: a Critical Review
-Table 1
-Prominent related work surveying FS techniques applied to Network Intrusion Detection.
-Authors Experiments Single/Multi Class Description
-Wang et al. [58] Performance analysis Single Class Feature Selection performed on the KDD99
-dataset, by applying C4.5 and Bayesian Net-
-work algorithms
-Janarthanan et al.
-[59]Performance analysis Single/Multi Class Empirical selection of features by performing
-tests on KDD99 and UNSW-NB15 datasets
-by means of Random Forest algorithm
-El-Khatib [60] Performance analysis Single/Multi Class Feature selection combining Filter/Wrapper
-methods for Wireless IDS. Tests have been
-performed on a WLAN environment
-Chen et al. [61] Performance analysis, Time
-analysisSingle Class Correlation-based Feature selection using the
-KDD99 dataset
-Nisioti et al. [62] N/A N/A Classic survey on ML-based techniques (in-
-cludingFS)withpointerstodetailedsources,
-but with no experiments
-Iglesias et al. [63] Performance analysis Single/Multi Class Comparison among 4FS algorithms on the
-NSL-KDD dataset
-Singh et al. [64] Performance analysis, Time
-analysisSingle Class Comparison among various FS algorithms on
-the KDD99 dataset
-Bahrololum et al.
-[65]Performance analysis Single/Multi Class Comparison among PSO, Decision Tree,
-FlexibleneuraltreealgorithmsontheKDD99
-dataset
-Dhote et al. [66] N/A N/A Limited survey of FS techniques applied to
-network traffic, with pointers to detailed
-sources, but with no experiments
-This work Performanceanalysis, Feature
-Correlation analysis, Time
-analysisSingle/Multi Class Feature Selection performed on the CIC-IDS-
-2017/2018dataset, byconsidering 9FSalgo-
-rithms from classic (Rank, Scatter) to mod-
-ern (Genetic, Multi-Objective Evolutionary)
-oftime. Anunsupervisedapproachcouldleadtoasubsetof
-features which does not include the destination port. Thus,
-acrucial(aswellasdeterministic)pieceofinformationgets
-lost.
-Ontheotherhand,asupervisedapproachcanofferopti-
-malresults,providedthatthedataarecorrectlylabeled. This
-case typically occurs in a controlled network environment,
-where, with the help of network analyzers, it is possible to
-automatically label the type of passing data traffic.
-Because of the great potential of supervised methods,
-andthankstotheavailabilityofsuitablelabeleddatasets,we
-have decided to focus our experimental-based comparative
-evaluation on supervised FS methods.
-3. Related Work on Feature Selection applied
-to ML-based Intrusion Detection
-Most scientific literature involving ML approaches is
-typically focused on the proposal of algorithms or tech-
-niquesforclassification/detectionofspecificnetworktraffic
-([67,68,69,70,71,72,73,74]). Unfortunately,thestraight
-applicationofmachinelearningtonetworktrafficanalysisis
-not always feasible, due to the broad variety of traffic types,
-which leads to unmanageable feature spaces. Intuitively, in
-fact, a highly diversified traffic (multimedia, asynchronous,bursty, etc.) requires a large set of features able to capture
-the variegated “nature" of the different flows. That is why
-appropriatepre-processingsteps(i.e. FS)playacrucialrole,
-thus a significant portion of ML-based literature has shown
-interest in FS techniques.
-Forinstance,toimprovetheperformanceofIDSframe-
-works, the authors of [75] propose a mixed strategy in-
-volvingPrincipalComponentAnalysisandfuzzyclustering
-withKNN-basedFStechniques. Acorrelation-basedFSap-
-proachcoupledwithaSupportVectorMachine(SVM)clas-
-sifierisproposedin[76]tobuildacloud-basedIDS.AnIDS
-based on deep learning methods, along with a filter-based
-FS algorithm, is introduced in [77]. Similarly, a Convolu-
-tional Neural Network (CNN) approach is exploited in [78]
-to select traffic features from raw data sets, improving the
-accuracy of an intrusion detector. Yu and Liu propose a
-mutualinformation-basedalgorithmthatcananalyticallyse-
-lect optimal features for classification, by handling linearly
-andnon-linearlydependentdata[79]. Again,FSisexploited
-jointlywithArtificialNeuralNetworks[80]andDeepNeural
-Networks [81], respectively, to improve IDS performance.
-Furthermore,authors in[82]embedin anIDStwoFSalgo-
-rithms that are compared against mutual information-based
-methods.
-A major shortcoming of these works is that methods
-Di Mauro et al.: Preprint submitted to Elsevier Page 3 of 19Supervised Feature Selection Techniques in Network Intrusion Detection: a Critical Review
-are validated and compared through the outdated KDD 99
-dataset. This contains information about older network at-
-tacks (that have been mitigated by now) or, in some cases,
-adopts an updated version of KDD 99, namely NSL-KDD
-[83]. Yet, although NSL-KDD adds some improvements
-onto KDD 99(e.g. no redundant records, better balancing
-between training and test set, etc.), it does not take into ac-
-count features that characterize novel cyber attacks. Other
-works in the field of machine learning applied to intrusion
-detectionrelyonmorerecentdatasetssuchasUNSW-NB15
-[84, 85, 86, 87]. Although quite recent, such a dataset has
-two limitations: first, the traffic has been collected in a re-
-duced testbed; and secondly, the number of features is lim-
-ited to 49, which is too small to appreciate the effectiveness
-of feature selection techniques. In contrast, in our work we
-relyonanup-to-datedatasetfromtheCanadianInstitutefor
-Cybersecurity [88] which has the following benefits: i/it
-containsdatatrafficgatheredoveravastnetworkarea;and ii/
-itaccountsforabout 80features,allowingtoextensivelytest
-the feature selection algorithms. Additional details about
-this dataset are provided in Section 5.
-Going more specifically into the set of works that share
-withthispapertheaimtocompareorsurveyFSmethodsfor
-intrusiondetection,wehavecollectedthesignificantpapers
-in Table 1. In it, we have identified the material covered in
-the literature, which helps appreciating the contributions of
-our paper. The first column of Table 1points to the source;
-thesecondcolumnhighlightsthetypeofexperimentalanal-
-ysis (if any); the third column pinpoints the type of datasets
-utilized (single or multi-class); the last column provides a
-concise description of the surveyed material.
-4. Review of Feature Selection Algorithms
-under Scrutiny
-In this section, we briefly describe the algorithms under
-scrutiny,whichbelongtodifferentfamiliesofFStechniques,
-rangingfromclassicrank-guided(Rank,LinearForwardSe-
-lection) and meta-heuristic (Tabu, Scatter, Particle Swarm)
-ones, to nature-inspired algorithms (Ant, Cuckoo), and up
-to modern techniques (Genetic, Multi-Objective Evolution-
-ary).
-For each algorithm, we provide a brief recap along with
-itspertinentapplicationinnetworktrafficanalysisandsecu-
-rity in literature.
-4.1. Rank-based Feature Selection
-Algorithms belonging to this family follow an approach
-based on two macro-steps: in the first one, the features are
-rankedaccordingtoacertainstatisticalmeasure,whereasin
-thesecondstepthealgorithmchoosesthetoprankedfeatures
-(eventually partitioned in clusters). We investigate and put
-to test two representative algorithms of this family: Rank
-Search and Linear Forward Selection.
-4.1.1. Rank Search
-TheRankSearchtechniquerefersnotonlytoasingleal-
-gorithm,but,toanumbrellaofmethodsabletoproducealistof attributes ranked with some criteria. One of the most ap-
-plied rank-based techniques in FS relies on the Information
-Gain (IG) concept [89]. Given an attribute Aand a class C,
-theentropyof the class without and with prior observation
-of the attribute are, respectively:
-H.C/ = *É
-cËCp.c/log2p.c/; (2)
-and
-H.CðA/ = *É
-aËAp.a/É
-cËCp.cða/log2p.cða/:(3)
-TheIGisgivenby H.C/ *H.CðAi/,andrepresentsthe
-amount of the class entropy decreasing due to the a priori
-knowledge introduced by i-th attribute.
-Over network traffic analysis literature, the rank search
-methodhasbeenwidelyexploitedinconjunctionwithstan-
-dard machine learning algorithms typically used during the
-classificationstep. In[90],havingtheNSL-KDDdatasetas
-input, the proposed algorithm i/evaluates the information
-gain value,ii/applies fuzzy rules to remove unwanted fea-
-tures,iii/calculatesthemeanvalueofIGacrossthenewsub-
-setoffeatures, iv/refinesthefeaturesubsetsbyapplyingan
-algorithm based on conditional probability evaluation. Au-
-thors in [91] propose a detection model able to cope with
-network attacks based on the feature IG ranking; once ob-
-tained a satisfying feature set, a triangle area based KNN,
-combiningbothSVMandgreedytechniques,isexploitedto
-single out even more discriminative and useful features. A
-combination between an IG-based feature selection method
-andaC4.5-basedclassifierisadvancedin[92],wherethere-
-sultingalgorithmhasbeenoptimized(intermsofpowercon-
-sumption) to reveal DoS attacks in ad hoc networks. Simi-
-larensemblehasbeenexploitedin[93],whereauthorscon-
-siderabroadersetofclassifiers(C4.5,RandomForest,Naive
-Bayes, etc.) and where the analysis focuses on generic mal-
-ware detection. Again, a mixed Genetic/KNN approach for
-intrusion detection presented in [94] benefits from a feature
-reductionprocedureobtainedapplyingID3algorithmtofind
-higher IG.
-4.1.2. Linear Forward Selection
-Suchatechniquetoreducethefeaturespacedimension-
-ality has been presented in [95]. Linear Forward Selection
-(LFS) can be considered as an improvement of Sequential
-ForwardSelection(SFS)methodwhichstartswithanempty
-set of features and sequentially adds one feature at a time.
-In order to face the O.N2/complexity of SFS, in LFS the
-number of considered features at each step does not exceed
-a certain user-specified constant, thus, the resulting perfor-
-mance is impressively ameliorated. Two methods for limit-
-ing the number of features have been implemented in LFS
-algorithm:i/FixedSet,whereascoreobtainedbymeansof
-a wrapper evaluator is used to choose the top- kranked at-
-tributes, thus, the maximum number of evaluations reduces
-tok_2.k+ 1/;ii/Fixed Width , where, at each forward
-Di Mauro et al.: Preprint submitted to Elsevier Page 4 of 19Supervised Feature Selection Techniques in Network Intrusion Detection: a Critical Review
-selection step, the number of features is increased by one,
-suchthatthesetofcandidateexpansionsconsistsofthebest
-kfeatures not been selected so far during the search. In
-such case, the maximum number of evaluations amounts to
-Nk*k_2.k+1/. ForwardSelection-basedtechniqueshave
-beenexploitedacrossthetrafficclassificationdomainforde-
-tection of malicious application on Android [96], detection
-ofzero dayattacks [97], designing novel anomaly detection
-systems [98].
-4.2. Meta-heuristic Feature Selection
-Techniquesbelongingtothisfamilyrelyontheprinciple
-that it is possible to select heuristics , namely, approximate
-algorithms searching for a sufficiently good solution to an
-optimization problem, useful when some constraints arise
-(e.g. limited computational resources, incomplete informa-
-tion). Weselectthreerepresentativealgorithmsofthisfam-
-ily: Tabu Search, Scatter Search, and Particle Swarm Opti-
-mization.
-4.2.1. Tabu Search
-Tabu Search (TS) algorithm has been originally pro-
-posed in [99], and, then, it has been extended and exploited
-to solve practical optimization problems [100, 101, 102].
-TS is based on a metaheuristic method able to pilot a lo-
-cal heuristic search in exploring the space of solutions be-
-yond the local optimality. Two main features characterize
-TS:adaptive memory andresponsive exploration . The for-
-mer allows to perform local choices guided by information
-collected during the search, whereas, the latter allows to
-make strategic choices. In other words, TS exploits a local
-search procedure combined with memory-based strategies,
-thus,theissueofgettingtrappedinlocaloptimalsolutionsis
-avoided. To implement the memory-based mechanism, TS
-builds a map of recently visited solutions called Tabu List
-(TL). A simplified TS algorithm is illustrated below:
-Algorithm 1: Tabu Search
-1. Given a function f.x/to be optimized over a set
-X, start from initial solution x0ËX, initialize
-Tabu List (TL), and initialize a counter i= 0.
-2. GivenN.xi/ÏXtheneighborhood ofxi,N.xi/
-can be reached by xiby means of a move
-operation. Thus, generate a neighborhood move
-listM.Xi).
-3. Givenxi+1the best solution in M.Xi), update
-TL.
-4. In case stopping conditions are met, terminate.
-Otherwise, repeat Step 2.
-It is interesting to notice that, the memory structures char-
-acterizing TS method operate according four dimensions
-[100]:
-•Recency: concerns the ability of keeping track of so-
-lutions attributes that have changed across the recent
-past.•Frequency : involves the mechanism exploited to
-broad the foundation for selecting preferred moves.
-•Quality: pertainstothecapacityofdiscriminatingthe
-meritofsolutionsvisitedduringthesearchoperation.
-•Influence: takes into account the impact of choices
-performed during the search.
-Inthefieldofnetworktrafficclassification,TShasbeen
-exploitedin[103]jointlywithfuzzytechniquesaimedatop-
-timizing the exploration of feature search space in intrusion
-detection problems. A combination of TS technique and
-KNNispresentedin[104],whereKNNisinitiallyexploited
-to generate a subset of non-redundant features, whereas, TS
-isusedtorefinetheobtainedsubset. Again,authorsin[105]
-propose a wrapperFS algorithm, dubbed GATS-C4.5, that
-embedsanhybridGeneticandTabu-basedmethodasfeature
-selection strategy and a supervised ML algorithm (C4.5) as
-the evaluation function. Similar combinations between TS
-andGenetictechniqueshavebeenusedin[106]wheresome
-tests (vs pure Genetic algorithms) across DARPA dataset
-have been carried out, and in [107] where SVM has been
-adopted as a classification criterion.
-4.2.2. Scatter Search
-Scatter Search is a metaheuristic algorithm involving
-memory-based mechanisms similar to those exploited in
-Tabu Search [108]. The main strategy relies on an iterative
-process that organizes high-quality optimal solutions into
-subsets, and where five “methods" emerge: i/Diversifica-
-tion,tocreateasetofdifferenttrialsolutionsbyexploitinga
-seed solution; ii/Improvement , to convert a trial solution in
-one or more improved solutions; iii/Reference Set Update ,
-to create and keep update a reference set of best solutions;
-iv/SubsetGeneration ,tomanipulatethereferencesetaimed
-at deriving a subset of solutions; v/Solution Combination ,
-to linearly re-combine the solutions on the basis of subsets
-obtained with the previous methods.
-As an effective FS procedure, Scatter Search has been
-exploitedtogetherwithNLPsolversforglobaloptimization
-[109],withroughsetsfortheimplementationofcreditscor-
-ing mechanisms [110], or in a parallelized fashion to im-
-prove the feature subset selection problem [111]. Again,
-ScatterSearchhasbeenprofitablyexploitedinissuesinvolv-
-ingcreditcardsfrauddetection[112],andsoftwaresecurity
-characterization [113].
-4.2.3. PSO Search
-The Particle Swarm Optimization (PSO) algorithm has
-beenoriginallydiscoveredin[114],throughsimulationscar-
-riedoutacrossasimplifiedsocialmodelaimedatreproduc-
-ing the behavior of birds flocking.
-Then,thepopulationofagentsbecamemoresimilartoa
-swarm than flock, and single individuals were named parti-
-clesthat represent the candidate solutions. In a mathemat-
-ical form, given AÏRnthe search space, and f:A, , ™
-Y Ó R, the swarm is defined by a set S= ^x1;x2;§;xN`
-ofNparticles, where xi= .xi1;xi2;§;xin/TËAwith
-Di Mauro et al.: Preprint submitted to Elsevier Page 5 of 19Supervised Feature Selection Techniques in Network Intrusion Detection: a Critical Review
-i= 1;2;§;N. It is assumed that particles are able to it-
-eratively move within search space Aby means of a ve-
-locity parameter defined as vi= .vi1;vi2;§;vin/Twith
-i= 1;2;§;N. In PSO method, it is also defined a mem-
-ory set P= ^p1;p2;§;pN`containing the best positions
-pi= .pi1;pi2;§;pin/T(withi= 1;2;§;N) visited by
-each particle. Given tthe time counter, the current position
-and velocity for particle iare, respectively, xi.t/andvi.t/,
-whereaspi.t/ = arg mintfi.t/. Accordingly, PSO is defined
-by the following equations:
-vij.t+ 1/ =vij.t/ +
1r1[pij.t/ *xij.t/](4)
-+
2r2[g.t/ *xij.t/];
-xij.t+ 1/ =xij.t/ +vij.t+ 1/; (5)
-where:r1andr2denote random variables uniformly dis-
-tributed in [0,1], 
1is thecognitive parameter which affects
-thestepsizethattheparticletakestowardsitsbestcandidate
-solutionpij.t/, and
2is thesocialparameter which affects
-thestepsizethattheparticletakestowardstheswarm’sbest
-solutiong.t/. At each iteration, best positions pi.t+ 1/are
-updated as well, namely
-pi.t+1/ =h
-n
-l
-njxi.t+ 1/iff.xi.t+ 1//ff.pi.t//;
-pi.t/otherwise.(6)
-InthefieldofFSappliedtonetworktrafficanalysis,PSO
-hasbeenprofitablyexploitedjointlywithclassificationtech-
-niques. It is the case of [115, 116], where a particle swarm
-selectionmethodhasbeenexploitedwithSVM-basedclassi-
-fiers. Again,ahybridFSmodelbasedonPSOandRandom
-Foresthasbeenexploitedin[117],whereindependentmea-
-suresandalearningalgorithmareexploitedtoevaluatefea-
-ture subsets. Interesting is also an evolution of classic PSO
-advanced in [118], and dubbed Accelerated PSO, amenable
-to deal with FS on Big Data streams.
-4.3. Nature-inspired Feature Selection
-Although relying on meta-heuristic concepts, this fam-
-ily of algorithms takes inspiration from the nature ecosys-
-tem, where many animal species exhibit impressive behav-
-iorsaimedatoptimizingtheirlifecycle. WeassessAntOp-
-timization and Cuckoo search as representative algorithms
-of such family.
-4.3.1. Ant Search
-This technique, originally proposed in [119], is inspired
-byantscoloniesbehavior,wheretheoptimizationproblemis
-solvedbya“colony"ofcooperatingagents. Analysescarried
-out by ethologists showed that, following pheromone trails,
-eachantisabletofollowaprecedingantwhichreleasessuch
-substance, thus, a whole ant colony is able to self-organize
-itself. The emerging collective behavior relies on a positive
-feedback loop: the probability which an ant chooses a cer-
-tainpathincreaseswiththenumberofantsthatchoosingthesamepath,sincethetrailiscontinuouslyreinforcedwithnew
-pheromone. The Ant algorithm can be profitably exploited
-in feature selection problems by evaluating the probability
-pk
-ijthat thek-th “ant" could arrive to feature jby starting
-from feature i, namely,
-pk
-ij=h
-n
-l
-nj[ij][ij]
-³
-kËUk[ik][ik]ifjËUk;
-0otherwise,(7)
-where,Ukisthesetoffeasibleattributes(notvisitedyet), ij
-istheamountofpheromoneacrossthe ijpath,ijrepresents
-theheuristicinformationfortheselectedattribute j,and
-areparametersinchargeofcontrollingpheromonetrialsand
-heuristic information, respectively.
-As regards the FS problem in network traffic analysis,
-Ant-based methods have been used in: [120] where a SVM
-classifier is adopted on KDD99 dataset, after a feature re-
-duction obtained through ACO (Ant Colony Optimization)
-technique; in [121] where an ACO-based feature selection
-method allows to deal with big streamed data; [122] where
-animprovedACO-basedalgorithm(namedFACO)hasbeen
-designed and tested across the classic KDD99 dataset.
-4.3.2. Cuckoo Search
-This technique is inspired to the brood parasitism strat-
-egycharacterizingsomecuckoosspecies. Inparticular,such
-specieslaytheireggsinthenestsofotherbirds(hosts). Since
-hostbirdscanengageaconflictastheyrecognizealieneggs,
-particularcuckoospecieshaveevolvedinsuchawaythatfe-
-malesareableinmimickingcolourandsizeofeggsofsome
-host species, thus the hosts are cheated, and the probabil-
-ity of cuckoos reproductivity grows. Considering an egg in
-a nest as a solution, three idealized rules emerge in Cuckoo
-Searchprocedure[123]: i/eachcuckoolaysoneeggattime,
-in a randomly chosen nest; ii/the bests nests (having high-
-quality eggs) are candidate to carry over next generations;
-iii/thenumberofnestsisfixedand,asahostbirddiscovers
-alien(cuckoo)eggswithaprobability pd,itgetsridofit. The
-aimofthealgorithmistoexploitnew(andeventuallybetter)
-solutions in place of not-so-good solutions in the nest. New
-solutions xt+1
-ifor cuckooiare obtained through the follow-
-ing expression which represents the stochastic equation for
-random walk, namely,
-xt+1
-i=xt
-i+äL./; (8)
-where, > 0is a scale factor, the product ärefers to en-
-trywise multiplications, whereas Lis the Lévy distribution
-with ( 1<f3).
-Cuckoo search method has been exploited in network
-trafficanalysispairedwithvarioustechniquesandtechnolo-
-gies. In [124], authors propose an algorithm that uses PCA
-and Cuckoo Search to reduce the feature space and to op-
-timize the clustering center selection. A Cuckoo-based FS
-algorithm is proposed in [125] to preprocess network data
-Di Mauro et al.: Preprint submitted to Elsevier Page 6 of 19Supervised Feature Selection Techniques in Network Intrusion Detection: a Critical Review
-aimedatimprovingtheIDSdetectionaccuracyinclouden-
-vironments. A Cuckoo search strategy has been also used
-in [126] to optimize Artificial Neural Networks when deal-
-ing with traffic anomaly detection issues. Again, coupled
-with SVM, Cuckoo search has been adopted in FS to deal
-with problem of phishing mail detection [127]. Recently,
-extended versions of Cuckoo Search algorithm have been
-advanced to cope with classification of tweetsin sentiment
-analysis[128],ortodefeatattacksinSoftwareDefinedNet-
-work infrastructures [129].
-4.4. Evolutionary Feature Selection
-Suchfamilyofalgorithmsisinspiredbynaturalselection
-theory, claiming that living organisms survived across mil-
-lions of years thanks to an adaptation process. In a similar
-way,thisaptitudecanbetranslatedinsearchforoptimalso-
-lutions to a problem. Two exemplary tested algorithms are:
-Genetic search and Multi Objective Evolutionary search.
-4.4.1. Genetic Search
-Genetic Algorithms (GAs) have been designed around
-themid-1950s,whenbiologistsstartedtoperformcomputer-
-based simulations aimed at analyzing more in deep the
-evolution of genetic processes [130]. Then, GAs have
-been extended to face problems ranging from neural net-
-works weight estimation [131] to inequalities-based prob-
-lems[132]. Apioneeringworkinthisfieldhasbeencarried
-outbyHolland[133,134],and,today,manyvariantsofGAs
-exist [135] and are applied in economy, computer science,
-sociology.
-The basic skeleton of a GA includes three operators
-[136]:Reproduction, Crossover andMutation.
-Reproduction refers to a process in charge of evaluating
-theabilityofanindividualtobeselected(amongothers)for
-reproduction, on the basis of a fitnessscore.
-Crossover concerns the capability of a genetic operator
-in recombining information to create new offspring. Typi-
-cally, offspring is generated by exchanging genes of parents
-until acrossover point is reached.
-Mutation pertains to the probability that some offspring
-genes could be modified or altered.
-Genetic-based feature selection in network traffic analy-
-sishasbeenusedinconjunctionwithmanyML-basedmeth-
-ods. Authors in [137] exploit a GA-based FS approach to
-optimize network traffic data before applying an artificial
-neuralnetworktoperformattacksdetectionacrosscloudin-
-frastructures. A combination of a genetic FS method and a
-supervised classifier based on J48 algorithm is proposed in
-[138]. More frequent across the scientific literature is the
-couplingbetweengeneticFSandSVMclassifiersappliedto
-networktrafficclassificationproblems(see[139,140,141]).
-When dealing with FS problems, GAs allow to explore
-the solution space by selecting the most promising regions,
-thus,avoidingacostlyexhaustivesearch. Inourdomain,the
-initial population is represented by the whole feature space
-and the fitness function relies on the correlation among fea-tures and expressed by means of a meritindicator defined
-further ahead in eq. (12).
-Once entered the cycle represented in Fig. 1, the algo-
-rithm calculates the fitness of each candidate solution per
-iteration, selects individuals to reproduce, and generates a
-newpopulationbytakingintoaccountcrossover(featurere-
-combination with a certain probability), and mutation (one
-featurecanbeturnedintoanotherfeaturewithacertainprob-
-ability).
-!"#$#%&'()*&%$#(" 
-!"#"$%&"'%#'(#(&(%)
-*+*,)%&(+#+-'(#.(/(.,%)0 +,%&*%$#(" 
-1/%),%&"&2"'-(&#"00'
--,#3&(+#4+567'-,#3&78 -#$".//01%&*./ 
-900(:#&2"'%**$+*$(%&"'
--(&#"00'/%),"0 
-2.&.3$#(" 
-;(#:)"'+,&'&2"'(#.(/(.,%)0 
--+$'$"*$+.,3&(+# 4.)5(6*3$#(" 
-!"#"$%&"'#"<'(#.(/(.,%)0 
-43$+00+/"$='>,&%&(+#8'
-Figure 1: Genetic Algorithms life cycle.
-4.4.2. Multi-Objective Evolutionary Search
-The family of solutions concerning a multiobjective op-
-timization problem (MO) includes all the elements of the
-search space whose objective vectors cannot be simultane-
-ously improved (Pareto optimality concept) [142]. The set
-of such objective vectors is said non-dominated.
-Moreformally,aMOproblemcanbeformulatedasfol-
-lows: given a vector of nobjective functions fof a vector
-variable xin a domain Ddefined as
-f.x/ = .f1.x/;f2.x/;§;fn.x/; (9)
-a decision vector xhËDis Pareto-optimal iffthere is no
-xkËDsuch that:
-h
-n
-n
-l
-n
-njÅiË ^1;§;n`;kifhi
-á
-ÇiË ^1;§;n` :ki<hi:(10)
-On the other hand, Evolutionary Algortihms (EAs)
-can be profitably exploited in MO-based problems since
-many “individuals" can search in parallel for multiple so-
-lutions, with the possibility of taking advantage of similari-
-tiesamongsolutionsbelongingtothesamefamily. Possible
-implementationsofMO-EAtechniquesareENORA(Evolu-
-tionary NOn-dominated Radial slots based Algorithm), and
-NSGA (Non-dominated Sorted Genetic Algorithm) com-
-pared in [143].
-Applied to the FS problem, the purpose of a multi-
-objective search algorithm is to discover a subset of fea-
-tures (a family of solutions) being a good approximation of
-the Pareto front. The MO-EA approach has been exploited
-Di Mauro et al.: Preprint submitted to Elsevier Page 7 of 19Supervised Feature Selection Techniques in Network Intrusion Detection: a Critical Review
-in network traffic classification jointly with ensemble ML-
-based methods [144], where some objectives such as max-
-imizing true positive rate, maximizing classification accu-
-racy,minimizingfeaturenumber,andminimizingfalsepos-
-itiverate,aresatisfiedsimultaneouslyandwithnoconflicts.
-An NSGA-based approach to ameliorate the performance
-classificationofIDSplatformshasbeenadoptedin[145]and
-in[146]. Again,aMO-EAtechniquejointlywithfuzzyclas-
-sifiers for coping with the traffic classification problem has
-been introduced in [147].
-5. The considered Datasets
-One of the great issues when dealing with supervised
-FSapproachesintrafficanalysis,isfindingtrainingsetsthat
-are both recent and labeled. As remarked in Sect. 3, many
-works rely on the obsolete KDD 99dataset [148]. Created
-about 20years ago, KDD 99has been broadly employed to
-validate machine learning algorithms, particularly to differ-
-entiatemaliciousdatatrafficfrombenignone. However,the
-KDD 99dataset does no longer reflect the characteristics of
-moderndatatraffic,whichhassensiblychangedacrosstime.
-Thisisverymuchthecaseofmultimediatraffic(e.g. voice,
-video), being the principle revenue-making stream for ser-
-vice providers but also the vehicle of covert malicious data.
-Percontra ,inthisworkweconsidermorerecentdatasetsob-
-tainedbymeansofCICFlowMeter[42,149],anopen-source
-enginethatcanbothgatherandlabelnetworktrafficinacon-
-trolled environment. Each dataset contains records labeled
-eitherasBenignorMalicious(malicioustrafficisoftensplit
-insub-labeledtrafficrepresentingdifferentkindsofattacks).
-Specifically, we consider the following datasets:
-•DDoSwhichcontainstrafficrelatingtodistributedde-
-nialofserviceattacks,aimedatsaturatingthenetwork
-resources of specific targets;
-•Portscan which contains traffic relating to Portscan
-attacks, aimed at discovering open, network device
-ports;
-•Webattack , including malicious traffic which imple-
-mentsvariousweb-basedattackssuchasBruteForce,
-Cross-Site Scripting, and Sql Injection;
-•TORwhichincludestrafficpassingovertheTORnet-
-work,ananonymousandprivatedatacircuitoftenex-
-ploited to carry dangerous information or malicious
-encrypted traffic;
-•Androidwhich embeds various families of Android-
-based threats (adwares, ransomwares, etc.).
-We want to notice that the first four datasets are exploited
-into the single class analysis, whereas the Androiddataset
-is exploited in the forthcoming multi-class analysis. In this
-latter analysis, we build a new dataset called MultiAndroid
-(see Sect. 6:2), obtained by selecting the most relevant mo-
-bile threats from the Androiddataset mixed with some be-
-nign traffic. In order to avoid the issue of bias that typi-
-cally arises during classification in imbalanced datasets, wehave first re-arranged the datasets. We have achieved well-
-balanced benign/malicious features, spanning across about
-50kinstances for each dataset. Each one contains up to 78
-features, except for the TOR dataset including 30 features.
-Forthesakeofconvenience,wefounditusefultogroupthe
-featuresin 5macro-classes(thecompletelistoffeaturescan
-be found in [150]):
-•Time-based features: Forward/Backward inter-
-arrivaltimes(IAT)betweentwoflows,durationofac-
-tive flow (min, max, mean, std), duration of idle flow
-(min, max, mean, std), etc.;
-•Byte-based features: Forward/Backward number of
-bytes in a flow, Forward/Backward number of bytes
-used for headers, etc.;
-•Packet-based features: Forward/Backward number
-of packets in a flow, Forward/Backward length of
-packets in a flow (min, max, mean, std), etc.;
-•Flow-based features: Length of a flow (mean, max,
-etc.);
-•Flag-based features: Number of packets with active
-TCP/IP flags (FIN, SYN, RST, PUSH, URG, etc.).
-6. Experimental Results
-Herein we describe our analytical study, which required
-the development of a dedicated Python routine, to normal-
-ize/balance the datasets and to automate the comparison
-among FS algorithms. We have validated the scrutinized
-search algorithms using the Correlation-based Feature Se-
-lector (CFS) as objective function [151, 152].
-The main idea behind CFS is that a good feature sub-
-setincludesthosefeaturesthatarehighlycorrelatedwiththe
-class,whilebeingstronglyuncorrelatedamongthem. Afor-
-mal definition is offered in [153]: a feature Xiis relevant iff
-there is some xiandyfor whichp.Xi=xi/>0such that
-p.Y=yðXi=xi/‘p.Y=y/: (11)
-Namely,Xiis relevant if Yis conditionally dependent on
-Xi. Thus, CFS is a filter algorithm that can rank feature
-subsets according to a correlation-based heuristic function.
-Precisely,givenasubset Sincludingkfeatures,theheuristic
-meritMS;kis defined as:
-MS;k=krfct
-k+k.k* 1/rff; (12)
-whererfcis the average value of feature/class correlations,
-andrffis the average value of feature/feature correlations.
-The numerator of (12) may be seen as an indicator of how
-far a set of features is predictive of a class; whereas, the
-denominator contains information about how much redun-
-dancy there is among features.
-Di Mauro et al.: Preprint submitted to Elsevier Page 8 of 19Supervised Feature Selection Techniques in Network Intrusion Detection: a Critical Review
-(a) Ant (21 fts)
- (b) Scatter (4 fts)
- (c) MO-EA (5 fts)
-(d) Ranking (10 fts)
- (e) Cuckoo (7 fts)
- (f) Tabu/LFS (6 fts)
-(g) Genetic (27 fts)
- (h) PSO (18 fts)
-Figure 2: Correlation maps for different algorithms - DDoS dataset. In parenthesis is
-reported the number of features surviving after the FS process.
-Our assessment is split into two parts: the first one con-
-cernsasingleclass analysis,whereweevaluatedatasetsex-
-hibitingdichotomous information (malign/benign); the sec-
-ond one is focused on multi class problems, where we eval-
-uate the effectiveness of FS in the presence of multiple
-classes.
-6.1. Single Class Analysis
-Let us consider the Distributed Denial of Service
-(DDoS) attack which, recently, is also affecting modern
-SDN-basednetworks[154,155]. DDoSattacksaredesignedto overwhelm the target network resources by means of a
-botnet, namely, a network composed of a large number of
-malicious nodes sending tiny packets towards the target, ul-
-timately coordinated by a botmaster .
-Let us now analyze the results obtained by pre-
-processing the DDoS dataset through the set of FS algo-
-rithms introduced above. In Fig. 2 we report, for each al-
-gorithm, the correlation map corresponding to a graphical
-representation of covariance matrices. This representation
-embedsthreeimportantpiecesofinformation: i)thenumber
-offeaturessurvivingaftertheFSprocessingstep; ii)thetype
-Di Mauro et al.: Preprint submitted to Elsevier Page 9 of 19Supervised Feature Selection Techniques in Network Intrusion Detection: a Critical Review
-0 1 2 3 4 5
-Training Size ×104100101102Feat. Sel. Time (sec)MO-EA
-Rank
-Ant
-Tabu
-Genetic
-Particle Swarm
-Cuckoo
-Lin.Fwd.Sel.
-Scatter
-0 1 2 3 4 5
-Training Size ×104100101102Training Time (sec)NO Feat. Sel.
-MO-EA
-Rank
-Ant
-Tabu
-Genetic
-Particle Swarm
-Cuckoo
-Lin.Fwd.Sel.
-Scatter
-Figure 3: FS times - DDoS dataset (a); Training times - DDoS dataset (b).
-offeatures;and iii)therelationshipexistingamongsurviving
-features. Thelatteristakenintoaccountbymeansofagray
-scale, in which darker shades indicate higher levels of cor-
-relation. Thus, each .i;j/“pixel" gives the correlation level
-between feature iand featurej. Accordingly, the pixels on
-the main diagonal are always black (maximum correlation,
-corr= 1), due to the self-correlation. As was to be expected,
-highercorrelationarefoundamongthosefeaturesbelonging
-to the same family (Time-based, Flow-based, etc.).
-Some interesting considerations about the various cor-
-relation maps arise. First, the number of features retained
-by different algorithms may significantly diverge, which is
-due to the specific approaches adopted by each algorithm.
-TheGeneticalgorithmistheoneretainingthemostfeatures.
-This is to be ascribed to the particular strategy of this al-
-gorithm, which strives to escape local optima by applying
-themutationoperator,thusallowingtoconsidermorepaths,
-namely, more features. Second, some common features re-
-tained by all the algorithms can be recognized. For in-
-stance,thedestinationportfeatureisalwayspresentsince,in
-aDDoSattack, atargetvictimis typicallyreachedonapar-
-ticular exposed TCP/UDP port. Moreover, since DDoS at-
-tacksarecharacterizedbyalargeamountofsmall-sizepack-
-ets, features embodying information about packet lengths
-are retained. The difference is that, some algorithms (e.g.
-Scatter, MO-EA, Cuckoo, Tabu, LFS) just keep the essen-
-tialfeaturesrelatedtopacketlength(e.g. totalpacketlength,
-total number of bytes sent in initial window); whereas,
-other algorithms (e.g. Ranking, Genetic, PSO, Ant) pre-
-fer to retain more features belonging to the same family.
-DDoSisalsocharacterizedbysomekindofsynchronization
-amongthebots,whicharecoordinatedtolaunchanalmost-
-simultaneous attack. This means that time-related features
-willoftenprovideusefulinformationtodetectDDoS.Inter-
-estingly, the Genetic algorithm retains 5features relating to
-the inter-arrival flow times, resulting in a dark gray cluster
-at the center of the correlation map (Fig. 2(g)).ItisalsopossibleforDDoSattackstobeevenmoreeffec-
-tivethroughthemodificationoftheIPflags(e.g. SYN/RST
-flooding). Accordingly, features embodying information
-aboutIPflags(e.g. RST-SYN-URGflagcount)areretained
-by algorithms such as Ant (Fig. 2(a)), MO-EA (Fig. 2(c)),
-Cuckoo (Fig. 2(e)), Genetic (Fig. 2(g)), and PSO (Fig.
-2(h)). Let us note that many algorithms opt for selecting
-featuresthatareuncorrelatedamongthem(fewdarkgrayor
-blackclustersarepresent)sincetheyconveymorevariegated
-information.
-Let us now analyze some findings obtained from the
-time-complexity evaluation. To this aim, we use a PC
-equipped with Intel CoreTMi5-7200U CPU@ 2.50GHz
-CPUand16GBofRAM.InFig. 3(a),weshowhowtheFS
-timevarieswithtrainingsize,fortheDDoSdataset. Nodra-
-maticdifferencesareobservedacrossthevariousalgorithms,
-even more significantly as the training size grows. Consid-
-ering a relatively large training size (with 5104training
-instances), FS times range from about 10seconds (Scatter
-algorithm) to almost 26seconds (MO-EA algorithm). Sur-
-prisingly, the FS times are rather uniform, in spite of the
-broadvariationinnumberofretainedfeatures(byeachofthe
-algorithms). For instance, remaining in the case of 5104
-training instances, Scatter retains the minimum number of
-features (4), while Genetic retains the maximum number of
-features(27);yetFStimesarecomparable( 16:19and10:18
-seconds, respectively). Although it is legitimate to expect
-that higher FS time could be justified to produce a more re-
-ducedfeaturespace,thescarcecorrelationbetweensuchob-
-servables is due to the particular logic implemented in each
-FS algorithm.
-On the other hand, Fig. 3(b) provides the training times
-obtained by applying the J48 benchmark algorithm, down-
-stream of the FS processing step. Here, the black line (with
-emptycircles)givesthetrainingtimesobtainedwhennoFS
-processing is employed. We can observe how FS leads to
-significantimprovements,intermsofbothtimesandtrends.
-Di Mauro et al.: Preprint submitted to Elsevier Page 10 of 19Supervised Feature Selection Techniques in Network Intrusion Detection: a Critical Review
-  NO F.S.       MO-EA         Rank          Ant         Tabu        Genetic        PSO        Cuckoo         LFS        Scatter   0.970.9750.980.9850.990.99511.0051.01DDoS Dataset
-Accuracy (DDoS)
-F-Measure (DDoS)
-Accuracy (Benign)
-F-Measure (Benign)
-(a)
-  NO F.S.       MO-EA         Rank          Ant         Tabu        Genetic        PSO        Cuckoo         LFS        Scatter   0.970.9750.980.9850.990.99511.0051.01Portscan Dataset
-Accuracy (Portscan)
-F-Measure (Portscan)
-Accuracy (Benign)
-F-Measure (Benign) (b)
-  NO F.S.       MO-EA         Rank          Ant         Tabu        Genetic        PSO        Cuckoo         LFS        Scatter   0.970.9750.980.9850.990.99511.005WebAttack Dataset
-Accuracy (WebAttack)
-F-Measure (WebAttack)
-Accuracy (Benign)
-F-Measure (Benign)
-(c)
-  NO F.S.       MO-EA         Rank          Ant         Tabu        Genetic        PSO        Cuckoo         LFS        Scatter   0.970.9750.980.9850.990.99511.005TOR Dataset
-Accuracy (TOR)
-F-Measure (TOR)
-Accuracy (Non TOR)
-F-Measure (Non TOR) (d)
-Figure 4: Performance in terms of Accuracy/F-Measures for different single class datasets:
-DDoS (a), Portscan (b), WebAttack (c), TOR (d).
-The black (benchmark) line grows rapidly to almost 80 sec-
-onds,whilemostalgorithmspeaktoalmost5seconds,with
-theexceptionoftheGeneticalgorithm(yellowline)andthe
-Particle Swarm algorithm (light blue line) that take over 10
-seconds to complete. This indicates that the FS process, on
-the whole, brings gains in the range of about one order of
-magnitude,whichmaybecomeevenmoresignificantasthe
-dataset grows.
-Let us now analyze the performance of the proposed FS
-algorithmsintermsofAccuracyandF-Measure. Thesetwo
-metrics,widelyusedinthefieldoftrafficclassification[156,
-157], are defined as follows:
-•Accuracy : the ratio of the correctly predicted obser-
-vations to the total observations. This is the most in-
-tuitive indicator.
-•F-Measure : the weighted average of precision (ratio
-ofcorrectlyclassifiedflowsoverallpredictedflowsina class) and recall (ratio of correctly classified flows
-overallgroundtruthflowsinaclass). Thisisanindi-
-cator of a per-class performance.
-To verify that the effectiveness of the FS algorithms is
-notlinkedtospecificdatasets,wehaveconsideredthe 4dif-
-ferent datasets introduced in Sect. 5(DDoS, Portscan, We-
-bAttack, and TOR), reporting our findings in Fig. 4. Just
-like for the previous experiments, we have used the tree-
-based J 48algorithm as a benchmark. We have adopted a
-10-foldcross-validationwhichistypicalinappliedML,and
-offersagoodtrade-offbetweentrainingtimeandrobustness.
-Noticeably,allFSalgorithmsperformsatisfactorily(bothin
-accuracy and F-measure) in comparison to the benchmark
-(firstbarsinallthehistograms,labeledas“NOF.S.”)forthe
-four datasets.
-InsomeinstancestheFSalgorithmsperformedevenbet-
-ter than the benchmark (e.g., Rank and Genetic algorithms
-in the WebAttack dataset). This can be explained by a phe-
-Di Mauro et al.: Preprint submitted to Elsevier Page 11 of 19Supervised Feature Selection Techniques in Network Intrusion Detection: a Critical Review
-(a) Ant (22 fts)
- (b) Scatter/Tabu (9 fts)
-Tot Lenof BwdPktsFwdPktLenStdBwdPktLenMeanInit_Win_bytes_FwdInit_Win_bytes_BwdFlow Pkt/sTot Lenof BwdPktsFwdPktLenStdBwdPktLenMeanInit_Win_bytes_FwdInit_Win_bytes_BwdFlow Pkt/s (c) MO-EA (6 fts)
-(d) Ranking (28 fts)
- (e) Cuckoo (17 fts)
-Tot Lenof BwdPktsFwdPktLenStdAvgBwdSegmentSizeInit_Win_bytes_FwdInit_Win_bytes_BwdFlow IAT MaxFwdPktLenMaxBwdPktLenStdFlow IAT MinTot Lenof BwdPktsFwdPktLenStd
-AvgBwdSegmentSizeInit_Win_bytes_FwdInit_Win_bytes_BwdFlow IAT MaxFwdPktLenMaxBwdPktLenStdFlow IAT Min (f) LFS (9 fts)
-(g) Genetic (31 fts)
- (h) PSO (23 fts)
-Figure 5: Correlation maps - MultiAndroid dataset. In parenthesis is reported the number
-of features surviving after the FS process.
-nomenon that is well-known in ML, whereby models based
-on too many features may lead to biased classification. On
-theotherhand,whenFSmanagestoretainasufficientlyhigh
-number of meaningful features, there is a positive effect on
-accuracy. This is the case of the Genetic algorithm applied
-to the TOR dataset (Fig. 4(d)) that performs better than the
-other methods.6.2. Multi Class Analysis
-Another fruitful analysis is aimed at evaluating FS al-
-gorithms when multi-instance datasets are considered. This
-turns out to be particularly useful when it is not possible to
-discerndifferenttypesofdatatrafficviasomepre-processing
-filter (e.g. IP/Port-based filtering). To assess this case, we
-consider two datasets: the MultiAndroid dataset, containing
-benigntrafficmixedupwithfivedifferenttypesofAndroid-
-based threats; and the DDoS/Portscan dataset, including a
-Di Mauro et al.: Preprint submitted to Elsevier Page 12 of 19Supervised Feature Selection Techniques in Network Intrusion Detection: a Critical Review
-0 1 2 3 4 5
-Training Size ×104100101102Feat. Sel. Time (sec)MO-EA
-Rank
-Ant
-Tabu
-Genetic
-Particle Swarm
-Cuckoo
-Lin.Fwd.Sel.
-Scatter
-(a)
-0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
-Training Size ×104100101102103Training Time (sec)NO Feat. Sel.
-MO-EA
-Rank
-Ant
-Tabu
-Genetic
-Particle Swarm
-Cuckoo
-Lin.Fwd.Sel.
-Scatter (b)
-Figure 6: FS times - MultiAndroid dataset (a); Training times - MultiAndroid dataset (b).
-mix of DDoS, Portscan, and benign traffic. The MultiAn-
-droiddataset,includesthefollowingtypesofmaligntraffic:
-•FakeApp.AL : a scareware hidden inside a fake
-Minecraft application, one of the most popular game
-applications;
-•Android Defender : a malware which, once acci-
-dentally downloaded and installed, raises some fake
-alerts;
-•Gooligam : an insidious malware that has already in-
-fected more than 1million Android-based devices,
-aimed at stealing Google accounts for Drive, Docs,
-Gmail, etc.;
-•Feiwo: belonging to the adware family, it acts by
-showingadvertisementsinthesystemnotificationbar,
-and by sending device GPS coordinates to a remote
-server;
-•Charger: a ransomware hidden in some Google Play
-applications, which gains root privileges and steals
-contacts before asking for a ransom.
-Let us analyze how FS algorithms impact on the Mul-
-tiAndroid dataset in terms of feature correlation referring
-first to the panels of Fig. 5. Comparing these results with
-the ones of Fig. 2, an interesting difference emerges: all FS
-algorithms retain more features w.r.t. the single-class case.
-Thisbehavioriscoherentwiththefactthat,todealwithdif-
-ferent types of threats (ransomware, adware, malware) we
-need more features, to be able to capture this higher vari-
-ability. This effect is even more evident in time-based fea-
-tures (mainly inter-arrival times) and in size-based features
-(mainly packet lengths).
-Looking at DDoS, we observe a difference between
-single- and multi-class analysis. In the latter, the destina-tion port is not retained as a crucial feature. This is possi-
-blybecausemalwaresexploitdifferentmechanismstocreate
-damage: rather than directly overwhelming a particular tar-
-getport,theyfirstactinthebackground(e.g. bystealingpri-
-vacy data) and then produce malicious traffic in egress. On
-the other hand, DDoS attacks generate ingress traffic from
-the infected device.
-It is worth noticing that, when applied to multi-class
-problems,allalgorithmshavepreservedtheiroriginallogic.
-For instance, with 31 surviving features, the Genetic algo-
-rithm is still the algorithm that saves more features, thanks
-totheroleplayedbythemutationoperator. Anotherexample
-istheMO-EAalgorithmthat,justlikeinthesingle-classex-
-periment,retainsthesmallestnumberoffeatures( 6). Thisis
-mainly due to the diversity-preservation mechanism, which
-forces the selection of a representative subset of the whole
-Pareto front. It optimizes conflicting objective functions,
-thus few solutions survive.
-The time-complexity evaluation is reported in Fig. 6,
-which evaluates the usual FS algorithms onto the MultiAn-
-droid dataset. FS times exhibit the same order of magni-
-tude as in single-class analysis (Figs.3(a)). For a training
-size amounting to 5104instances, the fastest algorithm is
-Scatter (FS time amounting to 9:541seconds); whereas the
-slowest one is MO-EA (FS time amounting to 24:827sec-
-onds).
-The situation changes dramatically when we consider
-training times for the J 48benchmark algorithm (Fig. 6(b)).
-Notably, multi-class algorithms are roughly one order of
-magnitudeslowerthantheirsingle-classcounterpart. Forin-
-stance,letusconsidertheGeneticalgorithm(yellowcurve).
-Fora 103trainingsize,GeneticFSreducesthetrainingtime
-to1:861seconds, growing to the following (X;Y) points:
-(104;10:731); (2 < 104;56:748); (3 < 104;133:346);
-(5 < 104;301:997). Thelongertrainingtimesarisefromthe
-process of training multiple classes. Nevertheless, signifi-
-Di Mauro et al.: Preprint submitted to Elsevier Page 13 of 19Supervised Feature Selection Techniques in Network Intrusion Detection: a Critical Review
-   NO F.S.       MO-EA        Rank         Ant          Tabu        Genetic       PSO         Cuckoo      LFS/Scat   0.20.30.40.50.60.70.80.9Multi-Class Dataset (Android threats) - Accuracy
-FakeAppal
-Andr.Defender
-Gooligan
-Feiwo
-Charger
-Benign
-(a)
-   NO F.S.       MO-EA        Rank         Ant          Tabu        Genetic       PSO         Cuckoo      LFS/Scat   0.20.250.30.350.40.450.50.550.6Multi-Class Dataset (Android threats) - F-Measure
-FakeAppal
-Andr.Defender
-Gooligan
-Feiwo
-Charger
-Benign (b)
-  NO F.S.       MO-EA         Rank          Ant         Tabu        Genetic        PSO        Cuckoo         LFS        Scatter   0.980.9850.990.99511.005Multi-Class Dataset (DDoS/Portscan) - Accuracy
-DDoS
-Portscan
-Benign
-(c)
-  NO F.S.       MO-EA         Rank          Ant         Tabu        Genetic        PSO        Cuckoo         LFS        Scatter   0.980.9850.990.99511.005Multi-Class Dataset (DDoS/Portscan) - F-Measure
-DDoS
-Portscan
-Benign (d)
-Figure 7: MultiAndroid dataset: Accuracy (a), F-Measure (b); DDoS/Portscan dataset:
-Accuracy (c), F-Measure (d);
-cant gains are still obtained by all FS algorithms compared
-to the “NO F.S.” benchmark, which peaks at 446:329secs.
-Turning now to the performance analysis, in Fig. 7
-wecomparethetwomulti-classdatasets,MultiAndroidand
-DDoS/Portscan,drawingsomeinterestingconsiderations. It
-is comparably more difficult to detect Android threats than
-DDoS/Portscan attacks - MultiAndroid accuracy is below
-0:7and F-Measure is below 0:5. However, this issue is
-not generated by the FS processes, since the “NO F.S.” per-
-formance is poor too, particularly with the “Benign” class.
-This issue arises from two facts. First, mobile network at-
-tacks are often accompanied by activities that do not di-
-rectly/immediately generate network anomalies. Examples
-are ransomware and malware, whereby the anomalies arise
-after the user has downloaded the malicious application.
-There is typically a lag between infection and anomalies,
-as the malicious program initially establishes a secret/silent
-communication with a remote server, and then graduallysteals/sends private user data. Another example is adware,
-wherethoseannoyingbannersactuallyincurverylittledata,
-thusmakingithardtodetectfromtheregulartraffic. Asec-
-ond reason for the poor MultiAndroid performance is the
-strongsimilarityamongdifferentmalignclasses(e.g.,scare-
-ware, adware, ransomware). Similar considerations hold
-trueinthecaseinwhichweconsideradatasetincludingWe-
-battackandTORtraffic(notreportedforspaceconstraints),
-whereby the high similarity between the two classes re-
-sulted in poor classification performance. We should how-
-ever stress that FS algorithms are still very beneficial, since
-thetime-complexitybenefitsidentifiedareachievedwithno
-dramatic loss in accuracy.
-By contrast, the DDoS/Portscan multi-class case
-achieves outstanding performance (Figs.7(c) and (d)). This
-is because these types of attacks are radically distinct in
-the way they exploit network vulnerabilities: DDoS falls
-under the umbrella of volumetric attacks; whereas Portscan
-Di Mauro et al.: Preprint submitted to Elsevier Page 14 of 19Supervised Feature Selection Techniques in Network Intrusion Detection: a Critical Review
-attacks employ monitoring strategies to unveil possible
-open ports. In other words, a peculiar symptom of a DDoS
-attack is the presence of an exceptionally large number
-of connections coming from different nodes and heading
-towards one network target’s port. Conversely, a symptom
-of Portscan attacks is the presence of just a single node (or
-a few nodes in case of simultaneous Portscans) opening a
-considerably large number of connections towards multiple
-ports of a certain network target. Thus it is relatively easier
-to differentiate between these two attacks.
-6.3. General Remarks
-Overall, we can observe that FS algorithms do lead to
-an effective reduction in feature space, ranging from 65~
-(Single Class, Genetic) to 95~(Single Class, Scatter) and
-from 60~(Multi Class, Genetic) to 92~(Multi Class, MO-
-EA). Such feature-space reduction translates into signifi-
-cantcomputational-timeimprovements,whichbecomeeven
-more remarked as the training size grows. For instance,
-with a training set of 50ksamples (single-class DDoS) the
-MO-EA algorithm takes 24:8secs to perform FS, while the
-trainingtimecomparedtothebenchmarkdropsfrom 72:2to
-5:13secs. Atthesametime,performanceisnotsignificantly
-degraded by the feature reduction process - accuracy drops
-from 0:9993to0:9971. Similar considerations hold for all
-other algorithms.
-The performed assessment provides invaluable guide-
-linesfornetwork/securitymanagementpractitionersdealing
-with traffic classification problems. Our evaluation frame-
-work aims at weighing the practical benefits of the vari-
-ous FS techniques in terms of time-complexity reduction
-and performance guarantees. For instance, if we aimed at
-minimizing the overall processing time (i.e., FS plus train-
-ing times), the Scatter algorithm would be the best choice.
-Thisincursatotalprocessingtimeamountingto 14:338sec-
-onds for the single-class case (FS= 10:178secs plus train-
-ing= 4:16secs),andto 219:963secondsformulti-class(FS=
-9:541secsplustraining= 210:422secs). Conversely,theGe-
-neticmethodwouldbepreferabletomaximizeperformance.
-7. Conclusion and Future Direction
-Aprominentresearchdirectionfornetworkintrusionde-
-tectionistheadoptionofmachinelearningmethods,partic-
-ularly for the detection of anomalous (and often malicious)
-network-traffic flows. Looking at the literature, we find am-
-ple examples of network classification problems. Yet, little
-attention has been turned towards feature selection, which
-is an essential classification pre-processing step. We argue
-that the main reason for this overlook is that most studies
-have been based on the obsolete KDD 99dataset, which in-
-cludesfewfeatures,thusmakingFSirrelevant. Ontheother
-hand, we consider that modern network engines generate
-much richer features (in fact, hundreds of features), which
-allowmorefineandgranularnetworktrafficanalyses. How-
-ever, this extra capability results into impractical ML train-
-ingtimes,makingitnecessarytounderstandhowFSmaybe
-realized effectively.To this end, herein we have carried out an experimental
-comparativeevaluationofprominentmethods,withtheview
-to provide insights as to how the different FS algorithms
-perform in the peculiar context of network-traffic classifica-
-tion. Our assessment shows how few, relevant features are
-retained, but also that the FS reduction process is virtually
-lossless, with a significant acceleration of the overall train-
-ing process.
-To sum up, the novelties of our work are:
-i)we carry out an experimental-based review, consider-
-ingrecentdatasets(includingDDoS,Portscan,WebAttacks,
-and Android threats), as opposed to the obsolete KDD 99
-dataset adopted in most literature;
-ii)we compare and contrast a representative number
-of alternative FS algorithm types, including classic rank-
-guided methods (LFS, Ranking), meta-heuristic methods
-(Particle Swarm, Tabu, Scatter), nature-inspired methods
-(Ant, Cuckoo), and evolutionary methods (Genetic, MO-
-EA);
-iii)we provide actual experimental results, unveiling
-trade-offs between performance (Accuracy/F-Measure) and
-computational time, at different scales (training set size).
-Ultimately,ouranalysisshowsthebenefitslinkedtoem-
-bedding the FS process into network analysis, providing a
-valuable tool for identifying the most useful features out of
-hundreds of possibilities. This will prove invaluable to the
-fieldsofnetworkmanagement,securitymanagement,intru-
-sion detection and incident response. We should note that,
-the purpose of our comparative evaluation was not to claim
-the predominance of some FS algorithms over others but,
-rather, to suggest a methodical framework to work with FS.
-As a byproduct of our investigation, some interesting
-open research directions emerge: i)extending the present
-analysisto unsupervised FStechniques,whichwouldbeuse-
-ful to deal with datasets lacking class labels, or with new
-types of (unknown) malicious traffic - this is the case of so
-called zero-day attacks that have no prior information; ii)
-consideringthecaseofstreameddataanalysis,whichisnec-
-essary when dealing with extremely time-variant streams,
-wherebytheFSprocessshouldberepeatedacrosstime(e.g.
-byusingamobiletimewindow),soastoperiodicallyupdate
-theresultingdatasetwiththefreshestfeatures; iii)designing
-routines to automatically manage the best FS strategies to
-be applied in accordance to specific criteria (e.g. accuracy
-target, latency needs, etc.). Our investigation goes into the
-direction of the 6G paradigm that, according to most net-
-workscientists,willbecharacterizedbyintelligentresource
-management,smartadjustments,andautomaticservicepro-
-visioning.
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