Journal ArticleDOI
Evaluation of Recurrent Neural Network and its Variants for Intrusion Detection System (IDS)
TLDR
This article describes how howSequencesareﻷ attributedﻴtemporal�characteristicsﻵeitherﻰ�explicitlyﻡ�orﻢimplicitly £2.5m Cybersecurity.Abstract:
ThisarticledescribeshowsequentialdatamodelingisarelevanttaskinCybersecurity.Sequencesare attributedtemporalcharacteristicseitherexplicitlyorimplicitly.Recurrentneuralnetworks(RNNs)are asubsetofartificialneuralnetworks(ANNs)whichhaveappearedasapowerful,principleapproach tolearndynamictemporalbehaviorsinanarbitrarylengthoflarge-scalesequencedata.Furthermore, stackedrecurrentneuralnetworks(S-RNNs)havethepotentialtolearncomplextemporalbehaviors quickly,includingsparserepresentations.Toleveragethis,theauthorsmodelnetworktrafficasatime series,particularlytransmissioncontrolprotocol/internetprotocol(TCP/IP)packetsinapredefined time range with a supervised learning method, using millions of known good and bad network connections.Tofindoutthebestarchitecture,theauthorscompleteacomprehensivereviewofvarious RNNarchitectureswithitsnetworkparametersandnetworkstructures.Ideally,asatestbed,they usetheexistingbenchmarkDefenseAdvancedResearchProjectsAgency/KnowledgeDiscovery andDataMining(DARPA)/(KDD)Cup‘99’intrusiondetection(ID)contestdatasettoshowthe efficacyofthesevariousRNNarchitectures.Alltheexperimentsofdeeplearningarchitecturesare runupto1000epochswithalearningrateintherange[0.01-0.5]onaGPU-enabledTensorFlowand experimentsoftraditionalmachinelearningalgorithmsaredoneusingScikit-learn.Experimentsof familiesofRNNarchitectureachievedalowfalsepositiverateincomparisontothetraditionalmachine learningclassifiers.TheprimaryreasonisthatRNNarchitecturesareabletostoreinformationfor long-termdependenciesovertime-lagsandtoadjustwithsuccessiveconnectionsequenceinformation. Inaddition,theeffectivenessofRNNarchitecturesareshownfortheUNSW-NB15dataset. KEywoRDS Deep Learning (DL) Approaches, Gated Recurrent Unit (GRU), Intrusion Detection (ID) Data Sets, KDDCup ’99’, Long Short-Term Memory (LSTM), Machine Learning (ML), Recurrent Neural Network (RNN), UNSW-NB15read more
Citations
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Proceedings ArticleDOI
Evaluating Shallow and Deep Neural Networks for Network Intrusion Detection Systems in Cyber Security
TL;DR: DNNs have been utilized to predict the attacks on Network Intrusion Detection System (N-IDS) and it is concluded that a DNN of 3 layers has superior performance over all the other classical machine learning algorithms.
Journal ArticleDOI
SDN-Enabled Hybrid DL-Driven Framework for the Detection of Emerging Cyber Threats in IoT
TL;DR: This work presents an SDN-enabled architecture leveraging hybrid deep learning detection algorithms for the efficient detection of cyber threats and attacks while considering the resource-constrained IoT devices so that no burden is placed on them.
Journal ArticleDOI
GAN augmentation to deal with imbalance in imaging-based intrusion detection
TL;DR: A deep learning methodology for the binary classification of the network traffic that leads to better predictive accuracy when compared to competitive intrusion detection architectures on four benchmark datasets is illustrated.
Journal ArticleDOI
Intrusion Detection System Using Voting-Based Neural Network
Mohammad Hashem Haghighat,Jun Li +1 more
TL;DR: Experimental results over KDDCUP'99 and CTU-13, as two well known and more widely employed datasets, revealed the voting procedure was highly effective to increase the system performance, where the false alarms were reduced up to 75% in comparison with the original deep learning models.
Journal ArticleDOI
Intrusion detection systems using classical machine learning techniques vs integrated unsupervised feature learning and deep neural network
TL;DR: A performance comparison of classical machine learning approaches that require vast feature engineering, vs integrated unsupervised feature learning and deep neural networks on the NSL‐KDD dataset, finds the DNN using 15 features extracted using Principal Component analysis (PCA) was the most effective modeling method.
References
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Deep Learning
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