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

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.

[1]  J. Baranowski.  Predicting IoT failures with Bayesian workflow , 2022, Eksploatacja i Niezawodnosc - Maintenance and Reliability.

[2]  Koushik Kumar Nundy,et al.  ANNet: A Lightweight Neural Network for ECG Anomaly Detection in IoT Edge Sensors , 2022, IEEE Transactions on Biomedical Circuits and Systems.

[3]  Ramón J. Durán Barroso,et al.  TSMAE: A Novel Anomaly Detection Approach for Internet of Things Time Series Data Using Memory-Augmented Autoencoder , 2023, IEEE Transactions on Network Science and Engineering.

[4]  R. Mateus,et al.  Short and long forecast to implement predictive maintenance in a pulp industry , 2021, Eksploatacja i Niezawodnosc - Maintenance and Reliability.

[5]  Prabhat Kumar,et al.  Toward Design of an Intelligent Cyber Attack Detection System using Hybrid Feature Reduced Approach for IoT Networks , 2021, Arabian Journal for Science and Engineering.

[6]  Laith Abualigah,et al.  Improved binary gray wolf optimizer and SVM for intrusion detection system in wireless sensor networks , 2020, Journal of Ambient Intelligence and Humanized Computing.

[7]  Mason Thammawichai,et al.  A Double-Layered Hybrid Approach for Network Intrusion Detection System using combined Naive Bayes and SVM , 2021, IEEE Access.

[8]  Jie Gu,et al.  An effective intrusion detection approach using SVM with naïve Bayes feature embedding , 2021, Comput. Secur..

[9]  Qiuzhen Lin,et al.  A multi-objective immune algorithm for intrusion feature selection , 2020, Appl. Soft Comput..

[10]  Ahmad Sharieh,et al.  A feature selection algorithm for intrusion detection system based on Pigeon Inspired Optimizer , 2020 .

[11]  Xiaojiang Du,et al.  Selection of effective machine learning algorithm and Bot-IoT attacks traffic identification for internet of things in smart city , 2020, Future Gener. Comput. Syst..

[12]  Ritu Tiwari,et al.  Particle swarm optimization and feature selection for intrusion detection system , 2020, Sādhanā.

[13]  Kuan-Ching Li,et al.  An intrusion detection approach based on improved deep belief network , 2020, Applied Intelligence.

[14]  Abdul Razaque,et al.  Deep recurrent neural network for IoT intrusion detection system , 2020, Simul. Model. Pract. Theory.

[15]  Sule Yildirim Yayilgan,et al.  A Novel Hybrid IDS Based on Modified NSGAII-ANN and Random Forest , 2020, Electronics.

[16]  Sheng Wang,et al.  BAT: Deep Learning Methods on Network Intrusion Detection Using NSL-KDD Dataset , 2020, IEEE Access.

[17]  Paulus Insap Santosa,et al.  Towards a Lightweight Detection System for Cyber Attacks in the IoT Environment Using Corresponding Features , 2020, Electronics.

[18]  Feng Liu,et al.  A Deep Learning Approach for Network Intrusion Detection Based on NSL-KDD Dataset , 2019, 2019 IEEE 13th International Conference on Anti-counterfeiting, Security, and Identification (ASID).

[19]  Jie Gu,et al.  A novel approach to intrusion detection using SVM ensemble with feature augmentation , 2019, Comput. Secur..

[20]  M. M. A. Hashem,et al.  Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches , 2019, Internet Things.

[21]  Bayu Adhi Tama,et al.  TSE-IDS: A Two-Stage Classifier Ensemble for Intelligent Anomaly-Based Intrusion Detection System , 2019, IEEE Access.

[22]  Ali Dehghantanha,et al.  A Two-Layer Dimension Reduction and Two-Tier Classification Model for Anomaly-Based Intrusion Detection in IoT Backbone Networks , 2019, IEEE Transactions on Emerging Topics in Computing.

[23]  Yixian Yang,et al.  Building an Effective Intrusion Detection System Using the Modified Density Peak Clustering Algorithm and Deep Belief Networks , 2019, Applied Sciences.

[24]  Elena Sitnikova,et al.  Towards the Development of Realistic Botnet Dataset in the Internet of Things for Network Forensic Analytics: Bot-IoT Dataset , 2018, Future Gener. Comput. Syst..

[25]  Sajjan G. Shiva,et al.  Comparative Analysis of ML Classifiers for Network Intrusion Detection , 2019, ICICT.

[26]  Zhen Liu,et al.  An Adaptive Ensemble Machine Learning Model for Intrusion Detection , 2019, IEEE Access.

[27]  Jong Hyuk Park,et al.  Semi-supervised learning based distributed attack detection framework for IoT , 2018, Appl. Soft Comput..

[28]  Ahmet Rizaner,et al.  Trust aware support vector machine intrusion detection and prevention system in vehicular ad hoc networks , 2018, Comput. Secur..

[29]  R. Vijayanand,et al.  Intrusion detection system for wireless mesh network using multiple support vector machine classifiers with genetic-algorithm-based feature selection , 2018, Comput. Secur..

[30]  Dhiren Patel,et al.  A feasible approach to intrusion detection in virtual network layer of Cloud computing , 2018, Sādhanā.

[31]  Marc-Oliver Pahl,et al.  Graph-based IoT microservice security , 2018, NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium.

[32]  Qi Shi,et al.  A Deep Learning Approach to Network Intrusion Detection , 2018, IEEE Transactions on Emerging Topics in Computational Intelligence.

[33]  Kehe Wu,et al.  A Novel Intrusion Detection Model for a Massive Network Using Convolutional Neural Networks , 2018, IEEE Access.

[34]  Naiqi Wu,et al.  SVM-DT-based adaptive and collaborative intrusion detection , 2018, IEEE/CAA Journal of Automatica Sinica.

[35]  Haipeng Yao,et al.  An Intrusion Detection Framework Based on Hybrid Multi-Level Data Mining , 2019, International Journal of Parallel Programming.

[36]  Robert C. Atkinson,et al.  Threat analysis of IoT networks using artificial neural network intrusion detection system , 2016, 2016 International Symposium on Networks, Computers and Communications (ISNCC).

[37]  Aboul Ella Hassanien,et al.  Binary grey wolf optimization approaches for feature selection , 2016, Neurocomputing.

[38]  Gholamhossein Dastghaibyfard,et al.  Two-tier network anomaly detection model: a machine learning approach , 2017, Journal of Intelligent Information Systems.

[39]  Antonio Martínez-Álvarez,et al.  Feature selection by multi-objective optimisation: Application to network anomaly detection by hierarchical self-organising maps , 2014, Knowl. Based Syst..

[40]  Andrew Lewis,et al.  Grey Wolf Optimizer , 2014, Adv. Eng. Softw..

[41]  Julio Ortega Lopera,et al.  Network Anomaly Classification by Support Vector Classifiers Ensemble and Non-linear Projection Techniques , 2013, HAIS.

[42]  Xiaobo Zhou,et al.  An Adaptive Growing Hierarchical Self Organizing Map for Network Intrusion Detection , 2010, 2010 Proceedings of 19th International Conference on Computer Communications and Networks.

[43]  Ali A. Ghorbani,et al.  A detailed analysis of the KDD CUP 99 data set , 2009, 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications.

[44]  Thomas Weigert,et al.  An adaptive automatically tuning intrusion detection system , 2008, TAAS.

[45]  Malcolm I. Heywood,et al.  A Hierarchical SOM based Intrusion Detection System , 2008 .

[46]  Eksploatacja i Niezawodnosc - Maintenance and Reliability , 2022 .