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 8 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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A Hybrid Machine Learning‐Based Data‐Centric Cybersecurity Detection in the 5G‐Enabled IoT

Zeng,

An,

Zhou

et al. 2024

Security and Privacy

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By increasing smart objects as high‐potential computing devices and 5G technology, the Internet of Things (IoT) has emerging technology to provide a data‐centric infrastructure for detecting safe device‐to‐device communications by supporting security and privacy issues. As a 5G‐enabled communication technology, vehicle‐to‐vehicle (V2V) communication provides a wireless exchange information connection between smart vehicles, intelligent devices, and a cloud‐edge computing environment. This connection should be established with a safe and secured run‐time protection system to avoid many critical anomalies and misbehavior problems. Detecting run‐time malicious transformations with data‐centric misbehaving reactions is a main challenge for autonomous vehicle communications with 5G‐enabled communication technology. This paper provides a hybrid genetic algorithm‐based ensemble bagged trees (GA‐EBT) algorithm for a data‐centric misbehavior detection approach to support the V2V communications against malicious and misbehavior transactions. In this hybrid algorithm, GA provides a beneficial performance on the training accuracy factor based on a single‐objective fitness function and EBT supports a high‐degree prediction and training process using multiple decision tree models with bootstrapped samples of the training data. For the evaluation of the proposed algorithm, four real test cases are applied for messaging injection attacks in the V2V environments in comparison to the state‐of‐the‐art machine learning algorithms. The experimental results show that the proposed hybrid approach can achieve an optimal high rate accuracy factor of 99.999, precision and recall factors of 100%, and an F1‐Score factor of 100% to detect unexpected cyber‐attacks for the V2V communications in the IoT environment.

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

Cited by 8 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

A Hybrid Machine Learning‐Based Data‐Centric Cybersecurity Detection in the 5G‐Enabled IoT

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