A Framework of Machine Learning Based Intrusion Detection for Wireless Sensor Networks

Yu, Zhenwei; Tsai, Jeffrey J. P.

doi:10.1109/sutc.2008.39

Cited by 70 publications

(37 citation statements)

References 17 publications

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“…In [18] a trust management system is proposed to defend against attacks inside the WSN which uses beta distribution to evaluate the different nodes' credibility. Finally, many works are based on computational intelligence techniques [25] or propose Fuzzy logic approaches for detecting intrusion in WSN [26][27][28][29][30] or learning algorithms and neural networks (i.e., clustering, SOM, and learning automata) [5,7,[31][32][33]. The focus, with GoNe, is on the last approach, due to the potentialities of learning algorithms and neural networks in recognizing in an inexpensive and fast way the misbehavior happening within the WSN.…”

Section: Malicious Node Detectionmentioning

confidence: 99%

Performance Comparison of Reputation Assessment Techniques Based on Self-Organizing Maps in Wireless Sensor Networks

Sicari

Grieco

Coen‐Porisini

2017

Wireless Communications and Mobile Computing

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Many solutions based on machine learning techniques have been proposed in literature aimed at detecting and promptly counteracting various kinds of malicious attack (data violation, clone, sybil, neglect, greed, and DoS attacks), which frequently affect Wireless Sensor Networks (WSNs). Besides recognizing the corrupted or violated information, also the attackers should be identified, in order to activate the proper countermeasures for preserving network's resources and to mitigate their malicious effects. To this end, techniques adopting Self-Organizing Maps (SOM) for intrusion detection in WSN were revealed to represent a valuable and effective solution to the problem. In this paper, the mechanism, namely, Good Network (GoNe), which is based on SOM and is able to assess the reliability of the sensor nodes, is compared with another relevant and similar work existing in literature. Extensive performance simulations, in terms of nodes' classification, attacks' identification, data accuracy, energy consumption, and signalling overhead, have been carried out in order to demonstrate the better feasibility and efficiency of the proposed solution in WSN field.

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Section: Malicious Node Detectionmentioning

confidence: 99%

Performance Comparison of Reputation Assessment Techniques Based on Self-Organizing Maps in Wireless Sensor Networks

Sicari

Grieco

Coen‐Porisini

2017

Wireless Communications and Mobile Computing

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“…However, similar to [12] the focus is on the performance (i.e., the accuracy of detection) of the IDS rather than a generic security framework. In [14] a framework of a machine learning based IDS for WSNs was presented without any evaluation of the scheme; only the rules for the proposed IDS was listed without any results and real experiment on sensors. In [15], embedded sensor networks were utilized to supplement wireless intrusion detection systems (WIDSs) on physical site surveillance and security tasks.…”

Section: Related Workmentioning

confidence: 99%

Di-Sec: A distributed security framework for heterogeneous Wireless Sensor Networks

Valero

Jung

Uluagac

et al. 2012

2012 Proceedings IEEE INFOCOM

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Abstract-Wireless Sensor Networks (WSNs) are deployed for monitoring in a range of critical domains (e.g., health care, military, critical infrastructure). Accordingly, these WSNs should be resilient to attacks. The current approach to defending against malicious threats is to develop and deploy a specific defense mechanism for a specific attack. However, the problem with this traditional approach to defending sensor networks is that the solution for the Jamming attack does not defend against other attacks (e.g., Sybil and Selective Forwarding). In reality, one cannot know a priori what type of attack an adversary will launch. This work addresses the challenges with the traditional approach to securing sensor networks and presents a comprehensive framework, Di-Sec, that can defend against all known and forthcoming attacks. At the heart of Di-Sec lies the monitoring core (M-Core), which is an extensible and lightweight layer that gathers statistics relevant for the defense mechanisms. The MCore allows for the monitoring of both internal and external threats and supports the execution of multiple detection and defense mechanisms (DDMs) against different threats in parallel. Along with Di-Sec, a new user-friendly domain-specific language was developed, the M-Core Control Language (MCL). Using the MCL, a user can implement new defense mechanisms without the overhead of learning the details of the underlying software architecture (i.e., TinyOS, Di-Sec). Hence, the MCL expedites the development of sensor defense mechanisms by significantly simplifying the coding process for developers. The Di-Sec framework has been implemented and tested on real sensors to evaluate its feasibility and performance. Our evaluation of memory, communication, and sensing components shows that Di-Sec is feasible on today's resource-limited sensors and has a nominal overhead. Furthermore, we illustrate the basic functionality of Di-Sec by implementing and simultaneously executing DDMs for attacks at various layers of the communication stack (i.e., Jamming, Selective Forwarding, Sybil, and Internal attacks).

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“…In [21] the authors propose a framework of a machine learning based intrusion detection system. An intrusion detection agent is implemented by each node to overhear the traffic of its neighbors, but there is no cooperation among nodes to detect attackers.…”

Section: Related Workmentioning

confidence: 99%

Cross Layer Intrusion Detection System for Wireless Sensor Network

Boubiche¹

2012

IJNSA

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A Framework of Machine Learning Based Intrusion Detection for Wireless Sensor Networks

Cited by 70 publications

References 17 publications

Performance Comparison of Reputation Assessment Techniques Based on Self-Organizing Maps in Wireless Sensor Networks

Performance Comparison of Reputation Assessment Techniques Based on Self-Organizing Maps in Wireless Sensor Networks

Di-Sec: A distributed security framework for heterogeneous Wireless Sensor Networks

Cross Layer Intrusion Detection System for Wireless Sensor Network

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