Malicious attack detection based on continuous Hidden Markov Models in Wireless sensor networks

M., Anselme R. Affane; Satori, Hassan; Boutazart, Youssef; Abderahim, Ezzine

doi:10.1016/j.micpro.2023.104888

Cited by 5 publications

(2 citation statements)

References 15 publications

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“…Deep learning models are not always suitable for dealing with the malicious activity of wireless networks. The work [ 17 ] shows the Hidden Markov Model (HMM) and Gaussian Mixture Model (GMM) stochastic assumptions outperform other machine learning models. Additionally, they also work on their own dataset.…”

Section: Related Workmentioning

confidence: 99%

Automated Sensor Node Malicious Activity Detection with Explainability Analysis

Zubair,

Janicke,

Mohsin

et al. 2024

Sensors

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Cybersecurity has become a major concern in the modern world due to our heavy reliance on cyber systems. Advanced automated systems utilize many sensors for intelligent decision-making, and any malicious activity of these sensors could potentially lead to a system-wide collapse. To ensure safety and security, it is essential to have a reliable system that can automatically detect and prevent any malicious activity, and modern detection systems are created based on machine learning (ML) models. Most often, the dataset generated from the sensor node for detecting malicious activity is highly imbalanced because the Malicious class is significantly fewer than the Non-Malicious class. To address these issues, we proposed a hybrid data balancing technique in combination with a Cluster-based Under Sampling and Synthetic Minority Oversampling Technique (SMOTE). We have also proposed an ensemble machine learning model that outperforms other standard ML models, achieving 99.7% accuracy. Additionally, we have identified the critical features that pose security risks to the sensor nodes with extensive explainability analysis of our proposed machine learning model. In brief, we have explored a hybrid data balancing method, developed a robust ensemble machine learning model for detecting malicious sensor nodes, and conducted a thorough analysis of the model’s explainability.

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Section: Related Workmentioning

confidence: 99%

Automated Sensor Node Malicious Activity Detection with Explainability Analysis

Zubair,

Janicke,

Mohsin

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…The primary challenge in WSNs is the vulnerability of security protocols to various attacks, particularly sinkhole and wormhole attacks. These attacks can lead to serious consequences such as data tampering, unauthorized data access, and the disruption of network services [8,9]. Previous studies have proposed various security mechanisms, but many suffe high computational complexity and increased communication overhead [10].…”

Section: Introductionmentioning

confidence: 99%

Secure Cooperative Routing in Wireless Sensor Networks

Batool,

Bibi,

Alhazmi

et al. 2024

Applied Sciences

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In wireless sensor networks (WSNs), sensor nodes are randomly distributed to transmit sensed data packets to the base station periodically. These sensor nodes, because of constrained battery power and storage space, cannot utilize conventional security measures. The widely held challenging issues for the network layer of WSNs are the packet-dropping attacks, mainly sinkhole and wormhole attacks, which focus on the routing pattern of the protocol. This thesis presents an improved version of the second level of the guard to the system, intrusion detection systems (IDSs), to limit the hostile impact of these attacks in a Low Energy Adaptive Clustering Hierarchy (LEACH) environment. The proposed system named multipath intrusion detection system (MIDS) integrates an IDs with ad hoc on-demand Multipath Distance Vector (AOMDV) protocol. The IDS agent uses the number of packets transmitted and received to calculate intrusion ratio (IR), which helps to mitigate sinkhole attacks and from AOMDV protocol round trip time (RTT) is computed by taking the difference between route request and route reply time to mitigate wormhole attack. MATLAB simulation results show that this cooperative model is an effective technique due to the higher packet delivery ratio (PDR), throughput, and detection accuracy. The proposed MIDS algorithm is proven to be more efficient when compared with an existing LEACH-based IDS system and MS-LEACH in terms of overall energy consumption, lifetime, and throughput of the network.

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Intelligent fuzzy logic based intrusion detection system for effective detection of black hole attack in WSN

Ravindran

2024

Peer-to-Peer Netw. Appl.

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Malicious attack detection based on continuous Hidden Markov Models in Wireless sensor networks

Cited by 5 publications

References 15 publications

Automated Sensor Node Malicious Activity Detection with Explainability Analysis

Automated Sensor Node Malicious Activity Detection with Explainability Analysis

Secure Cooperative Routing in Wireless Sensor Networks

Intelligent fuzzy logic based intrusion detection system for effective detection of black hole attack in WSN

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