Anomaly detection in traffic using L1-norm minimization extreme learning machine

Wang, Yibing; Li, Dong; Du, Yi; Pan, Zhisong

doi:10.1016/j.neucom.2014.04.073

Cited by 51 publications

(21 citation statements)

References 38 publications

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“…Anomaly detection also called outlier detection [11] has been widely applied in traffic detection [4,12] , abnormal crowd behavior detection [13,14] , credit card fraud detection [15] , and network intrusion detection [16,17] . The widely-used anomaly detection methods can be divided into following categories: Classification based methods, Nearest neighbor-based methods, Clustering based methods and Statistic based methods .…”

Section: Related Workmentioning

confidence: 99%

Anomaly detection via adaptive greedy model

et al. 2019

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Section: Related Workmentioning

confidence: 99%

Anomaly detection via adaptive greedy model

et al. 2019

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“…The L1-ELM is a neural network designed with " " hidden layer nodes that produce an output based on weighted values. The output is formulated as follows [38]:…”

Section: Hybrid Classifiermentioning

confidence: 99%

Validating User Flows to Protect Software Defined Network Environments

Abdulqadder

Zou

Aziz

et al. 2018

Security and Communication Networks

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Software Defined Network is a promising network paradigm which has led to several security threats in SDN applications that involve user flows, switches, and controllers in the network. Threats as spoofing, tampering, information disclosure, Denial of Service, flow table overloading, and so on have been addressed by many researchers. In this paper, we present novel SDN design to solve three security threats: flow table overloading is solved by constructing a star topology-based architecture, unsupervised hashing method mitigates link spoofing attack, and fuzzy classifier combined with L1-ELM running on a neural network for isolating anomaly packets from normal packets. For effective flow migration Discrete-Time Finite-State Markov Chain model is applied. Extensive simulations using OMNeT++ demonstrate the performance of our proposed approach, which is better at preserving holding time than are other state-of-the-art works from the literature.

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“…Wang et al [7], proposed the mechanism of finding unknown attacks or the abnormal behaviours with the help of Traffic classification, Anomaly detection, Extreme learning machine, Support vector machine, L1-norm minimization and finally proposed that ELM found to be advantageous. However resource requirement was not adequate and performance was comparatively low.…”

Section: Bmentioning

confidence: 99%

Misuse and Anomaly Based Combined Hybrid Approaches for Intrusion Detection: A Survey

Sujata¹,

Priyanka²,

Rani³

2018

International Journal of Advanced Research in Computer and Comm

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Intrusion Detection Systems (IDS) can be defined with various detection techniques. Misuse based and anomaly based detection techniques are some of the techniques used for identifying the known and unknown attacks. This paper shows a survey on both misuse and anomaly based detection techniques for IDS where it is observed that these two techniques when applied at a time gives better results than when applied individually on a particular dataset and it has been depicted in this paper by considering a case study where decision support system and expert rule based approach are applied on KDD 99 dataset for observing both normal and abnormal behaviour of the data.

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Anomaly detection in traffic using L1-norm minimization extreme learning machine

Cited by 51 publications

References 38 publications

Anomaly detection via adaptive greedy model

Anomaly detection via adaptive greedy model

Validating User Flows to Protect Software Defined Network Environments

Misuse and Anomaly Based Combined Hybrid Approaches for Intrusion Detection: A Survey

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