Double Layered Priority based Gray Wolf Algorithm (PrGWO-SK) for safety management in IoT network through anomaly detection

• Autorzy: Agrawal Akhileshwar Prasad , Singh Nanhay

Identyfikatory

DOI

/10.17531/ein.2022.4.5

• Języki publikacji: EN

Abstrakty

EN

For mitigating and managing risk failures due to Internet of Things (IoT) attacks, many Machine Learning (ML) and Deep Learning (DL) solutions have been used to detect attacks but mostly suffer from the problem of high dimensionality. The problem is even more acute for resource starved IoT nodes to work with high dimension data. Motivated by this problem, in the present work a priority based Gray Wolf Optimizer is proposed for effectively reducing the input feature vector of the dataset. At each iteration all the wolves leverage the relative importance of their leader wolves’ position vector for updating their own positions. Also, a new inclusive fitness function is hereby proposed which incorporates all the important quality metrics along with the accuracy measure. In a first, SVM is used to initialize the proposed PrGWO population and kNN is used as the fitness wrapper technique. The proposed approach is tested on NSL-KDD, DS2OS and BoTIoT datasets and the best accuracies are found to be 99.60%, 99.71% and 99.97% with number of features as 12,6 and 9 respectively which are better than most of the existing algorithms.

Słowa kluczowe

EN

Gray Wolf Optimizer; anomaly detection; feature selection; predictive maintenance

• Wydawca: Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne
• Czasopismo: Eksploatacja i Niezawodność
• Rocznik: 2022
• Tom: Vol. 24, no. 4
• Strony: 641--654
• Opis fizyczny: Bibliogr. 47 poz., rys., tab.

Twórcy

autor

Agrawal Akhileshwar Prasad

• Guru Gobind Singh Indraprastha University, Dept of Computer Science and Engg., Ambedkar Institute of Advanced Communication Technologies and Research (now NSUT-E), Geeta Colony, Delhi-110031, India

• https://orcid.org/0000-0001-6366-0311

autor

Singh Nanhay

• Guru Gobind Singh Indraprastha University, Dept of Computer Science and Engg., Ambedkar Institute of Advanced Communication Technologies and Research (now NSUT-E), Geeta Colony, Delhi-110031, India

• https://orcid.org/0000-0002-3303-1386

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).