# Challenges of Malware Detection in the IoT and a Review of Artificial Immune System Approaches
## 7 Conclusions  
IoT systems are interconnected and heterogeneous devices with limited computational capacity. The number of IoT applications and their integration into traditional networks is increasing rapidly. This has led to new and fast-spreading security threats, not least malware attacks, that traditional security solutions fail to address adequately. Traditional IoT malware detection techniques employ signature-based and behavioral-based methods. We have demonstrated that these are either unsuitable for detecting unknown malware files or are not cost-efficient for IoT applications. AIS represents a research direction inspired by the human body's adaptive immune system for the detection of new threats. AIS methods are generally attractive for malware detection owing to their ability to detect unknown attacks and intelligently keep records of any attack for future use. In addition, they are a prime contender in the design of IoT malware detection because the offered features are the best match with IoT system characteristics. The features of AIS methods, such as their adaptivity, distributed implementation, lightweight computation, and robustness, are  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Method/Properties** & **Robust** & **Lightweight** & **Fault Tolerant** & **Adaptive** & **Distributed** \\ \hline NPS: negativeselection + positiveselection [50] & ✔ & ✔ & ✔ & ✔ & ✔ \\ \hline MNSA: negativeselection + positiveselection [51] & ✘ & ✘ & ✘ & ✔ & ✔ \\ \hline PCSA: positiveselection [37] & ✘ & ✔ & ✘ & ✔ & ✔ \\ \hline NSSNN: negativeselection + neuralnetwork [54] & ✘ & ✘ & ✔ & ✔ & ✔ \\ \hline AWA: artificialimmune ecosystem [57] & ✔ & ✘ & ✘ & ✔ & ✔ \\ \hline Immune system based method [58] & ✔ & ✘ & ✘ & ✔ & ✘ \\ \hline Artificial Immune based method [59] & ✔ & ✘ & ✘ & ✔ & ✔ \\ \hline Immune System based method [60] & ✔ & ✘ & ✘ & ✔ & ✔ \\ \hline \hline \end{tabular}
\end{table}
Table 5: IoT system properties adopted in AIS solutions.  
compatible with the IoT devices' specific requirements. To this end, this article surveys recent research in the field of AIS for malware detection. We provide a critical analysis of existing works, draw key insights, and identify promising future research directions in which novel AIS techniques can be developed to address imminent and increasing IoT security challenges.  
Conceptualization, H.A. and G.G.; methodology, H.A.; software, H.A.; validation, H.A., G.G., M.J. and J.K.; formal analysis, H.A., G.G., M.J. and J.K.; investigation, H.A.; writing--original draft preparation, H.A.; writing--review and editing, H.A., G.G., M.J. and J.K.; visualization, H.A.; supervision, G.G., M.J. and J.K. All authors have read and agreed to the published version of the manuscript.  
[PERSON] is a recipient of a Kingdom of Saudi Arabia Ministry of Education Scholarship. Furthermore, the Kingdom of Saudi Arabia Ministry of Education supports the studies of [PERSON].  
We use the NSL-KDD dataset to run the performance analysis in this research. The dataset is available at [[https://www.unb.ca/cic/datasets/nsl.html](https://www.unb.ca/cic/datasets/nsl.html)]([https://www.unb.ca/cic/datasets/nsl.html](https://www.unb.ca/cic/datasets/nsl.html)) (accessed on 1 October 2021).  
The authors declare no conflict of interest.