A Global Hybrid Intrusion Detection System for Wireless Sensor Networks

Maleh, Yassine; Ezzati, Abdellah; Qasmaoui, Youssef; Mbida, Mohamed

doi:10.1016/j.procs.2015.05.108

Cited by 81 publications

(39 citation statements)

References 11 publications

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“…Maleh et al (2015) [7], used support vector machine (SVM) algorithm for IDS which included a learning algorithm and for detecting malicious behaviours and lightweight IDS, which is based on attack signatures. Simulation results showed lower false alarm, high detection rate and can efficiently detect abnormal events.…”

Section: Figure 2 Intrusion Detection Techniques For Wireless Networmentioning

confidence: 99%

Intrusion Detection Systems

2019

IJRTE

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Intrusion Detection System is competent to detect the intrusions and alerting the administrator of system about the signs of possible intrusions. This paper presents a detailed review of the intrusion detection techniques used in WSNs. More specifically, the existing methods for blackhole and sinkhole attacks detection are reviewed. However, it is noted that most intrusion detection schemes proposed in the literature are either inefficient or have low detection rates/high false positive rates. This survey also highlights the research gap in this domain and provides better scope for the advanced work.

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Section: Figure 2 Intrusion Detection Techniques For Wireless Networmentioning

confidence: 99%

Intrusion Detection Systems

2019

IJRTE

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“…Bezemskij et al [14] proposed a method based on Bayesian networks, which could not only determine whether a robot was attacked, but also determine whether the attack came from the cyber-domain or the physical-domain. Maleh et al [15] maked use of a support vector machine (SVM) algorithm to detect the presence of malicious behavior through a set of signature rule anomaly detection and provided global lightweight IDS. Subsequently, Jones et al [16] proposed a two-level intrusion detection system, consisting of a signature detection component and an anomaly detection component, where the anomaly detection component trained through a deep neural network (DNN) to detect commands that deviated from expected behaviors.…”

Section: Related Workmentioning

confidence: 99%

Improved Deep Belief Networks (IDBN) Dynamic Model-Based Detection and Mitigation for Targeted Attacks on Heavy-Duty Robots

Xie

et al. 2018

Applied Sciences

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Featured Application: The research is mainly used for heavy-duty robots to detect and mitigate target attacks, which come from both the cyber-domain and the physical-domain. Abstract:In recent years, the robots, especially heavy-duty robots, have become the hardest-hit areas for targeted attacks. These attacks come from both the cyber-domain and the physical-domain. In order to improve the security of heavy-duty robots, this paper proposes a detection and mitigation mechanism which based on improved deep belief networks (IDBN) and dynamic model. The detection mechanism consists of two parts: (1) IDBN security checks, which can detect targeted attacks from the cyber-domain; (2) Dynamic model and security detection, used to detect the targeted attacks which can possibly lead to a physical-domain damage. The mitigation mechanism was established on the base of the detection mechanism and could mitigate transient and discontinuous attacks. Moreover, a test platform was established to carry out the performance evaluation test for the proposed mechanism. The results show that, the detection accuracy for the attack of the cyber-domain of IDBN reaches 96.2%, and the detection accuracy for the attack of physical-domain control commands reaches 94%. The performance evaluation test has verified the reliability and high efficiency of the proposed detection and mitigation mechanism for heavy-duty robots.

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“…It is not that only the wired networks suffer from the malicious activities, but the wireless sensor networks (WSNs) also suffer from them more prominently due to their open‐air transmission and self‐organizing architecture. Maleh et al discuss a hybrid lightweight IDS for protecting the WSNs by using the cluster‐based topology that requires less communication cost. Santoro et al design a hybrid IDS for WSNs to identify the virtual jamming attack.…”

Section: Introductionmentioning

confidence: 99%

Intrusion detection in RFID system using computational intelligence approach for underground mines

Gautam

2018

Int J Communication

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The radio frequency identification technology (RFID) is commonly used for object tracking and monitoring. In this paper, we discuss a model for intrusion detection system based on RFID to identify the abnormal behavior of underground mines' toxic gases. This model consists of various types of sensor nodes that are integrated with RFID tag, which are deployed in the underground mines by using Zigbee protocol. It consists of coordinators, routers, and sensor nodes, according to different capabilities and the probabilities of intrusive activities that occur in underground mines. It can detect the real-time abnormal

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A Global Hybrid Intrusion Detection System for Wireless Sensor Networks

Cited by 81 publications

References 11 publications

Intrusion Detection Systems

Intrusion Detection Systems

Improved Deep Belief Networks (IDBN) Dynamic Model-Based Detection and Mitigation for Targeted Attacks on Heavy-Duty Robots

Intrusion detection in RFID system using computational intelligence approach for underground mines

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