Abstract
The fast growth of the Internet of Things (IoT) and its diverse applications increase the risk of cyberattacks, one type of which is malware attacks. Due to the IoT devices’ different capabilities and the dynamic and ever-evolving environment, applying complex security measures is challenging, and applying only basic security standards is risky. Artificial Immune Systems (AIS) are intrusion-detecting algorithms inspired by the human body’s adaptive immune system techniques. Most of these algorithms imitate the human’s body B-cell and T-cell defensive mechanisms. They are lightweight, adaptive, and able to detect malware attacks without prior knowledge. In this work, we review the recent advances in employing AIS for the improved detection of malware in IoT networks. We present a critical analysis that highlights the limitations of the state-of-the-art in AIS research and offer insights into promising new research directions.
Highlights
Review of Artificial Immune SystemToday’s world is more connected than ever before
True positive (TP): malware is detected as a malicious application; True negative (TN): benign software is detected as non-malicious application; False positive (FP): benign software is detected as a malicious application; False negative (FN): malware is detected as non-malicious application
We have demonstrated that these are either unsuitable for detecting unknown malware files or are not cost-efficient for Internet of Things (IoT) applications
Summary
Today’s world is more connected than ever before. Societies are reliant on technology, which has become inextricable from people’s daily lives. We examine the ways in which IoT devices increase the risk of malware attacks and review pertinent detection and prevention methods. Artificial Immune System (AIS) methods are inspired by the human immune system’s methodology for fighting attacks They are proven to be adaptive, distributed, robust, and not computationally expensive, which makes them suitable to secure the IoT. For this reason, we dedicate this review paper to investigating and analyzing the AIS methods in detecting malware files in the IoT.
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