A context-aware robust intrusion detection system: a reinforcement learning-based approach

Sethi, Kamalakanta; Rupesh, E. Sai; Kumar, Rahul; Bera, Padmalochan; Madhav, Y. Venu

doi:10.1007/s10207-019-00482-7

Cited by 89 publications

(53 citation statements)

References 45 publications

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“…In particular, the data analysis for detection (DataAnalysisForDetecion) and the data analysis for optimization (DataAnalysisForOptimization) are well represented: respectively 14 and 17 instances. For example, in Sethi et al (2019), an instance of DataAnalysisForDetection is presented to prevent and detect intrusions in enterprise network. In Chen & Chen (2017), an instance of DataAnalysisForOptimization is described to optimize scheduling; • almost all articles are applied to a specific domain (AgentMiningApplicationDomain).…”

Section: Agentminingapproachmentioning

confidence: 99%

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Agent mining approaches: an ontological view

Strugeon

Oliveira

Zekri

et al. 2021

The Knowledge Engineering Review

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Introduced as an interdisciplinary area that combines multi-agent systems, data mining and knowledge discovery, agent mining is currently in practice. To develop agent mining applications involves a combination of different approaches (model, architecture, technique and so on) from software agent and data mining (DM) areas. This paper presents an investigation of the approaches used in the agent mining systems by deeply analyzing 121 papers resulting from a systematic literature review. An ontology was defined to capitalize the knowledge collected from this study. The ontology is organized according to seven main facets: the problem addressed, the application domain, the agent-related and the mining-related elements, the models, processes and algorithms. This ontology is aimed at providing support to decisions about agent mining application design.

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Section: Agentminingapproachmentioning

confidence: 99%

“…),Thanudas et al (2019),Yang et al (2019); • AgentMiningAlgorithm, AgentMiningModel and AgentMiningProcess can be found in several papers. Some papers even include instances for each of these three concepts (e.g Larsen et al (2019),Roldán et al (2018),Sethi et al (2019)…”

mentioning

confidence: 99%

Agent mining approaches: an ontological view

Strugeon

Oliveira

Zekri

et al. 2021

The Knowledge Engineering Review

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“…Extant research has not proposed using RL to ensure security in the 6LoWPAN network. However, there are several studies [12]- [17] where RL is used to enhance IDS performance in detecting application-based attacks. They employ Q-learning [13] and a centralised hybrid IDS to perform the detection task over the data received through cluster heads in the WSN.…”

Section: Related Work and Motivationsmentioning

confidence: 99%

“…In their proposed model RL is used to enhance anomaly IDS detection performance. Similarly, [12] investigates different RL methods, [16], [17]. In [17], researchers employ distributed DRL to boost IDS performance and prepare it against adversarial attack.…”

Section: Related Work and Motivationsmentioning

confidence: 99%

Reinforcement-Learning-Based IDS for 6LoWPAN

Pasikhani¹,

Clark²,

Gope³

2021

Preprint

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<p>The Routing Protocol for low power Lossy networks (RPL) is a critical operational component of low power wireless personal area networks using IPv6 (6LoWPANs). In this paper we propose a Reinforcement Learning (RL) based IDS to detect various attacks on RPL in 6LoWPANs, including several unaddressed by current research. The proposed scheme can also detect previously unseen attacks and the presence of mobile intruders. The scheme is well suited to the resource constrained environments of our target networks.</p><br>

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“…Speed and also the ability to run adversarial attacks with limited resources are important criteria for real-life deployment of neural networks [16][17][18]. JSMA obeys these constraints making its use widespread beyond computer vision applications such as in cybersecurity, anomaly detection and intrusion detection [19][20][21]. Later on, [11] proposes the second example of targeted L 0 attacks known as CW L 0 .…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Jacobian-Based Saliency Maps Attacks

Combey

Loison

Faucher

et al. 2020

MAKE

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Neural network classifiers (NNCs) are known to be vulnerable to malicious adversarial perturbations of inputs including those modifying a small fraction of the input features named sparse or L0 attacks. Effective and fast L0 attacks, such as the widely used Jacobian-based Saliency Map Attack (JSMA) are practical to fool NNCs but also to improve their robustness. In this paper, we show that penalising saliency maps of JSMA by the output probabilities and the input features of the NNC leads to more powerful attack algorithms that better take into account each input’s characteristics. This leads us to introduce improved versions of JSMA, named Weighted JSMA (WJSMA) and Taylor JSMA (TJSMA), and demonstrate through a variety of white-box and black-box experiments on three different datasets (MNIST, CIFAR-10 and GTSRB), that they are both significantly faster and more efficient than the original targeted and non-targeted versions of JSMA. Experiments also demonstrate, in some cases, very competitive results of our attacks in comparison with the Carlini-Wagner (CW) L0 attack, while remaining, like JSMA, significantly faster (WJSMA and TJSMA are more than 50 times faster than CW L0 on CIFAR-10). Therefore, our new attacks provide good trade-offs between JSMA and CW for L0 real-time adversarial testing on datasets such as the ones previously cited.

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A context-aware robust intrusion detection system: a reinforcement learning-based approach

Cited by 89 publications

References 45 publications

Agent mining approaches: an ontological view

Agent mining approaches: an ontological view

Reinforcement-Learning-Based IDS for 6LoWPAN

Probabilistic Jacobian-Based Saliency Maps Attacks

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