High Density Sensor Networks Intrusion Detection System for Anomaly Intruders Using the Slime Mould Algorithm

Abstract

The Intrusion Detection System (IDS) is an important feature that should be integrated in high density sensor networks, particularly in wireless sensor networks (WSNs). Dynamic routing information communication and an unprotected public media make them easy targets for a wide variety of security threats. IDSs are helpful tools that can detect and prevent system vulnerabilities in a network. Unfortunately, there is no possibility to construct advanced protective measures within the basic infrastructure of the WSN. There seem to be a variety of machine learning (ML) approaches that are used to combat the infiltration issues plaguing WSNs. The Slime Mould Algorithm (SMA) is a recently suggested ML approach for optimization problems. Therefore, in this paper, SMA will be integrated into an IDS for WSN for anomaly detection. The SMA’s role is to reduce the number of features in the dataset from 41 to five features. The classification was accomplished by two methods, Support Vector Machine with polynomial core and decision tree. The SMA showed comparable results based on the NSL-KDD dataset, where 99.39%, 0.61%, 99.36%, 99.42%, 99.33%, 0.58%, and 99.34%, corresponding to accuracy, error rate, sensitivity, specificity, precision, false positive rate, and F-measure, respectively, are obtained, which are significantly improved values when compared to other works.

Advances in Slime Mould Algorithm: A Comprehensive Survey

“…The experimental results demonstrate that the proposed AOLISMA can enhance the throughput and reduce the control overhead. Alwan MH et al [155] integrated an SMA into an intrusion detection system (IDS) in wireless sensor networks for anomaly detection purposes. The SMA was used to decrease the number of features.…”

Urban Traffic Flow Estimation System Based on Gated Recurrent Unit Deep Learning Methodology for Internet of Vehicles

“…Studies are either statistical or using deep learning techniques. However, DNN are not limited to computer vision field or images, it was utilized in other studies such as security [3,4], that is, the study presented in this work will show the implementation of DNN in the urban planning as well. The study group in [5] provide an overview of the several strategies that can be used to anticipate and model traffic flow and then explores the limits of each strategy.…”

Cyber-Attacks in WSN & Security Optimization By a Novel Technique based Intensive Binary Pigeon Optimization (IBiPO) & Bi-LSTM-based IDS Framework

“…This algorithm incorporates the operations of both ELM and multi-kernel SVM for increasing the robustness and detection accuracy. Alwan, et al [22] utilized a Slime Mould Algorithm (SMA) for developing a new IDS framework for WSN. Elsaid, et al [23] developed an optimized collaboration based IDS framework for increasing the security of WSNs.…”