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

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 thr...

Full description

Saved in:
Bibliographic Details
Published in:Electronics (Basel) 2022-10, Vol.11 (20), p.3332
Main Authors: Alwan, Mohammed Hasan, Hammadi, Yousif I., Mahmood, Omar Abdulkareem, Muthanna, Ammar, Koucheryavy, Andrey
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Anomalies
Clustering
Data security
Datasets
Decision trees
Density
Feature selection
Intrusion detection systems
Machine learning
Methods
Optimization
Polynomials
Sensors
Slime
Support vector machines
Wireless networks
Wireless sensor networks
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics11203332