Constructing important features from massive network traffic for lightweight intrusion detection

Wang, Wei; He, Yejun; Liu, Jiqiang; Gombault, Sylvain

doi:10.1049/iet-ifs.2014.0353

Cited by 39 publications

(16 citation statements)

References 20 publications

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“…Given the presence of massive network traffic in the real world, it is prudent to consider large number of instances for experimentation as in the case of the UGR'16 dataset. With the advent of Internet of things (IOT), network traffic will only become more and more complex in the coming years [36,37].…”

Section: Resultsmentioning

confidence: 99%

A Stacking Ensemble for Network Intrusion Detection Using Heterogeneous Datasets

Rajagopal

Kundapur

Hareesha

2020

Security and Communication Networks

145

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The problem of network intrusion detection poses innumerable challenges to the research community, industry, and commercial sectors. Moreover, the persistent attacks occurring on the cyber-threat landscape compel researchers to devise robust approaches in order to address the recurring problem. Given the presence of massive network traffic, conventional machine learning algorithms when applied in the field of network intrusion detection are quite ineffective. Instead, a hybrid multimodel solution when sought improves performance thereby producing reliable predictions. Therefore, this article presents an ensemble model using metaclassification approach enabled by stacked generalization. Two contemporary as well as heterogeneous datasets, namely, UNSW NB-15, a packet-based dataset, and UGR’16, a flow-based dataset, that were captured in emulated as well as real network traffic environment, respectively, were used for experimentation. Empirical results indicate that the proposed stacking ensemble is capable of generating superior predictions with respect to a real-time dataset (97% accuracy) than an emulated one (94% accuracy).

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Section: Resultsmentioning

confidence: 99%

A Stacking Ensemble for Network Intrusion Detection Using Heterogeneous Datasets

Rajagopal

Kundapur

Hareesha

2020

Security and Communication Networks

145

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“…In [22][23][24][25][26][27][28], the authors have proposed a FS method based on limited criteria using the NSGA-II for network anomaly detection and pattern classification. They have evaluated their work in terms of classification accuracy and time of execution for different benchmark datasets.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi‐objective‐based feature selection for DDoS attack detection in IoT networks

2020

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In this study, the authors propose a multi-objective optimisation-based feature selection (FS) method for the detection of distributed denial of service (DDoS) attacks in an internet of things (IoT) network. An intrusion detection system (IDS) is one approach for the detection of cyber-attacks. FS is required to reduce the dimensionality of data and improve the performance of the IDS. One of the reasons for the failure of an IDS is incorrect selection of features because most of the FS methods are based on a limited number of objectives such as accuracy or relevance of data, but these are not enough as they can be misleading for attack detection the contribution of this work is to develop appropriate FS method. They have implemented the nondominated sorting algorithm with its adapted jumping gene operator to solve the optimisation problem and exploited an extreme learning machine as the classifier for FS based on six important objectives for an IoT network. Experimental results verify that the proposed method performs well for FS and have achieved 99.9% and has reduced the total number of features by nearly 90%. The proposed method outperforms other proposed FS methods for the detection of DDoS attacks by an IDS.

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“…With the intention of reducing computational costs and improving IDS classification performance, several machine learning techniques with Chi-square (CHI), Information gain (IG) [16] and Correlation-based Feature Selection (CFS) such as filter techniques [17] are used in this study. The filter technique based on the ranking approach sorts the features according to their usefulness in the classification movement.…”

Section: Introductionmentioning

confidence: 99%

Distributed Denial-of-Service Prediction on IoT Framework by Learning Techniques

Dwivedi

Vardhan

Tripathi

2020

Open Computer Science

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Distributed denial-of-service (DDoS) attacks on the Internet of Things (IoT) pose a serious threat to several web-based networks. The intruder’s ability to deal with the power of various cooperating devices to instigate an attack makes its administration even more multifaceted. This complexity can be further increased while lots of intruders attempt to overload an attack against a device. To counter and defend against modern DDoS attacks, several effective and powerful techniques have been used in the literature, such as data mining and artificial intelligence for the intrusion detection system (IDS), but they have some limitations. To overcome the existing limitations, in this study, we propose an intrusion detection mechanism that is an integration of a filter-based selection technique and a machine learning algorithm, called information gain-based intrusion detection system (IGIDS). In addition, IGIDS selects the most relevant features from the original IDS datasets that can help to distinguish typical low-speed DDoS attacks and, then, the selected features are passed on to the classifiers, i.e. support vector machine (SVM), decision tree (C4.5), naïve Bayes (NB) and multilayer perceptron (MLP) to detect attacks. The publicly available datasets as KDD Cup 99, CAIDA DDOS Attack 2007, CONFICKER worm, and UNINA traffic traces, are used for our experimental study. From the results of the simulation, it is clear that IGIDS with C4.5 acquires high detection and accuracy with a low false-positive rate.

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Constructing important features from massive network traffic for lightweight intrusion detection

Cited by 39 publications

References 20 publications

A Stacking Ensemble for Network Intrusion Detection Using Heterogeneous Datasets

A Stacking Ensemble for Network Intrusion Detection Using Heterogeneous Datasets

Multi‐objective‐based feature selection for DDoS attack detection in IoT networks

Distributed Denial-of-Service Prediction on IoT Framework by Learning Techniques

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