# Challenges of Malware Detection in the IoT and a Review of Artificial Immune System Approaches
###### 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.  
adaptive immunology; Artificial Immune Systems (AIS); Internet of Things (IoT); malware detection; security +
Footnote †: journal: Sensors and Actuator Networks  
and malware detection, specifically detecting unknown malware files, is one of the ongoing challenges. IoT devices usually have a small amount of memory and processing capacity, which makes them lightweight. This characteristic limits the complexity of possible security solutions. There are three overarching challenges in the detection of malware attacks in the IoT. Firstly, the low computational power of most IoT devices limits the complexity of the security algorithm. Secondly, the rise of unseen malware attacks targeting IoT systems necessitates a fast-adapting detection mechanism (which requires complex algorithms). Thirdly, the rapid spread of IoT devices and the resulting increased security risk requires a highly robust protection mechanism.  
Current malware detection solutions that work effectively on traditional networks are either too complex to implement on IoT networks or lack the adaptability and robustness to enable secure operations. 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.  
Contribution and Organization of the PaperThis is the first paper to survey IoT security challenges and state-of-the-art malware detection methods with a focus on AIS. We present a critical analysis of the literature and offer a quantitative comparison, presenting new findings for the state-of-the-art solutions. Although the authors in [9] present an AIS survey for IoT security, they do not offer a quantitative performance analysis. The insights that we can draw from this comparison are not available in any other published paper, to the best of our knowledge. Furthermore, we present promising future research directions and implementation suggestions for employing AIS methods to secure the IoT.  
The paper is structured into five main parts, as shown in Figure 1. In Section 2, we present an overview of IoT systems' characteristics and their impact on security, followed by malware attack analysis and detection techniques. Section 3 presents an introduction to the immune system techniques and AIS methods inspired by adaptive immunology: In Section 4, we offer a literature review and a critical analysis of AIS implementations in securing the IoT. Section 5 presents a performance analysis of the AIS for malware detection in the IoT state-of-the-art method. In Section 6, we present trends, promises, and implementation suggestions for future work.  
Figure 1: The structure of this review paper.