AI-Based Techniques for DDoS Attack Detection in WSN: A Systematic Literature Review

Mohammed Al-Naeem,Adamu Abubakar Ibrahim,M.M Hafizur Rahman,Mohammed Ashikur Rahman

doi:10.3844/jcssp.2020.848.855

Abstract

Wireless Sensor Networks (WSNs) are currently being used in various industries such as healthcare, engineering, the environment and so on. Security is a significant issue for WSN due to its infrastructure and limited physical security. Distributed Denial of Service (DDoS) is one of the most vulnerable attacks that can be defined as attacks launched from multiple ends into a set of legitimate sensor nodes in the WSN to drain their inadequate energy resources. Nowadays, Artificial intelligence techniques are performing better accuracy than the traditional methods to detect intrusion for the various attack. This Systematic Literature Review (SLR) attempts to investigate the current status of DDoS detection techniques and to identify the most capable and effective detection system using artificial intelligence to detect distributed DoS attack. Preferred Reporting Item for Systematic Review and Meta-Analysis (PRISMA) statement is used to conduct this review. Based on 15 out of 983 that met inclusion criteria, Support Vector Machine (SVM) and Artificial Neural Network (ANN) is the most used AI-based techniques to detect distributed denial of service attack in the wireless sensor network. The performance of AI techniques-based detection system for DDoS attack in WSN is remarkable.

Highlights

The wireless sensor network is a combination of self-configured sensors that can communicate via radio link without any centralized controlling system (Yu and Tsai, 2008)
As per the first inclusion criteria of this Systematic Literature Review (SLR), Papers were selected from the year 2013 to 2019. 33.33% of the Papers were selected from the year 2016. 20.0% of papers were published in the year 2013
Since Wireless Sensor Networks (WSNs) are different from other networks, it required innovation solution for security

Summary

Introduction

The wireless sensor network is a combination of self-configured sensors that can communicate via radio link without any centralized controlling system (Yu and Tsai, 2008). For example, health, institutes, data centers and modern industries, the use of WSN is increasing extensively (Cheng et al, 2016; Ogbodo et al, 2017). WSN is composed of different independent, minor, minimal effort and low power sensor nodes. These nodes can accumulate data from the vast network and send data to concentrated backend elements called base stations or sinks for additional processing (Alsheikh et al, 2014). An attacker can inject messages in WSNs because it uses radio communication which can be captured and inject malicious messages to perform a denial of service attack (Yu and Tsai, 2008)

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