An Intrusion Detection System Based on Normalized Mutual Information Antibodies Feature Selection and Adaptive Quantum Artificial Immune System

Zhang, Ling; Hao, Zhang Jia

doi:10.4018/ijswis.308469

Cited by 34 publications

(16 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The existing trust management schemes [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25]44,45,51,52 failed to fulfill the most fundamental requirement for industrial WSN (ICN). Finally, after sincerely analyzing existing work, we can say that without considering indirect (feedback or reputation) trust, frequency of misbehavior, current, and past misbehavior, a malicious node might disguise the network to ruin its reputation 38 and remain not detected as well as trustworthy [46][47][48][49][50] . The survival of ICNs is highly dependent on the successful cooperation of tamper-resistant SNs 3 .…”

Section: Literature Reviewmentioning

confidence: 99%

A secure and dependable trust assessment (SDTS) scheme for industrial communication networks

Khan

Singh

Ahmad

et al. 2023

Sci Rep

View full text Add to dashboard Cite

Due to tamper-resistant sensor nodes and wireless media, Industrial Wireless Sensor Networks (WSNs) are susceptible to various security threats that severely affect industrial/business applications. The survival of sensor networks is highly dependent on the flourishing collaboration of sensor nodes. Trust management schemes seem to be realistic and promising techniques to improve security as well as cooperation (dependability) among sensor nodes by estimating the trust level (score) of individual sensor nodes. This research paper presents a well-organized and motivating secure, dependable trust assessment (SDTS) scheme for industrial WSNs to cope with unexpected behavior such as an on–off attack, bad-mouthing attack, garnished attack, etc., by employing robust trust evaluation components based on success ratio and node misbehaviour. SDTS incorporates an interesting trust evaluation function in which the trust range can be adjusted in accordance with the application requirement. SDTS include direct communication trust, indirect communication trust, data trust, and misbehavior-based trust to defend the multiple internal attacks. SDTS works according to the behavior of nodes, i.e., whether the sensor nodes are interacting frequently or not. Moreover, abnormal attenuation and dynamic slide lengths are incorporated in the proposed model (SDTS) to deal with various natural calamities and internal attacks. SDTS is compared against three recent state-of-the-art methods and found efficient in terms of ease of trust assessment, false-positive rate (2.5%), false-negative rate (2%), attack detection rate (90%), detection accuracy (91%), average energy consumption (0.40 J), and throughput (108 Kbps) under the load of 500 sensor nodes with 50% malicious nodes. Investigational results exhibit the potency of the proposed scheme.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

A secure and dependable trust assessment (SDTS) scheme for industrial communication networks

Khan

Singh

Ahmad

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…No IoTbased dataset was used for model training. (Ling & Hao, 2022) present an algorithm for feature selection based on artificial immune with cooperative evolution of multiple operators. Authors evaluate their work using KDD99 and UNSW-NB15 datasets, which are not oriented for evaluating the performance of an IDS in IoT networks (Thakkar & Lohiya, 2021).…”

Section: Related Workmentioning

confidence: 99%

Engineering the application of machine learning in an IDS based on IoT traffic flow

Prazeres¹,

Costa²,

Santos³

et al. 2023

Intelligent Systems with Applications

View full text Add to dashboard Cite

“…This communication process can potentially introduce latency and consume bandwidth, particularly in IoT networks with limited resources. Zhang Ling et al 35 36 introduced two approaches, namely NMAIFS MOP‐AQAI and NMIFS MOP‐AQGA, to tackle the challenges related to detection rate and accuracy. The experiments were conducted using the KDD99 and UNSW‐NB15 datasets.…”

Section: Literature Surveymentioning

confidence: 99%

P‐DNN: Parallel DNN based IDS framework for the detection of IoT vulnerabilities

Sharmila¹,

Nagapadma²

2023

Security and Privacy

View full text Add to dashboard Cite

SummaryThe rapid growth of the Internet of Things (IoT) in our daily life has recently received attention from hackers in releasing novel attacks. This is because the existing traditional Intrusion Detection System (IDS) uses an alert‐based approach that cannot detect new emerging attacks, making it unfeasible for devices with limited resources. The study presents the deployment of a Parallel Deep Neural Network (P‐DNN) IDS framework to improve the detection rate and resource constraint issues. To validate the framework for detecting the latest IoT vulnerabilities, we generated a proprietary dataset created under the IoT environment. The experimental results reveal that the P‐DNN IDS framework proposed in this study outperformed the K‐nearest Neighbour (KNN), Support Vector Machine (SVM), and Naive Bayes algorithms with respect to performance metrics. In addition, a comparative analysis of the P‐DNN framework with Snort and Suricata IDS results demonstrates a significant reduction in CPU consumption, memory utilization, and processing time. The results of this investigation indicate that the deployment of the P‐DNN framework has the capability to detect vulnerabilities in resource‐constraint IoT environments effectively.

show abstract

An Intrusion Detection System Based on Normalized Mutual Information Antibodies Feature Selection and Adaptive Quantum Artificial Immune System

Cited by 34 publications

References 30 publications

A secure and dependable trust assessment (SDTS) scheme for industrial communication networks

A secure and dependable trust assessment (SDTS) scheme for industrial communication networks

Engineering the application of machine learning in an IDS based on IoT traffic flow

P‐DNN: Parallel DNN based IDS framework for the detection of IoT vulnerabilities

Contact Info

Product

Resources

About