# Challenges of Malware Detection in the IoT and a Review of Artificial Immune System Approaches
## 2 Security Challenges and Malware Attacks in the IoT
### Malware in the IoT  
The malware detection techniques presented in the previous section have been followed to implement malware detection methods in the IoT; for instance, SVELTE, which is a signature and anomaly-based intrusion detection method, has been used to protect the IoT from routing attacks based on the IPv6 routing protocol [31]. On one hand, applyinga signature-based technique for malware detection in the IoT is not the best approach because it is not designed to detect unknown/newly developed malware files; on the other hand, designing a behavioral-based or specification-based method to secure the IoT is computationally expensive due to the long simulation process it requires.  
Major AI solutions to securing the IoT fall under either behavior or specification-based techniques, which are complex to implement in IoT systems. For instance, the authors in [32] evaluate the recent advances in AI/ML techniques in securing the IoT. They use 80% of the dataset only to train the module, which is computationally expensive, and state that, despite the advances in AI techniques in the IoT, the security method is still vulnerable when implemented in a real IoT system. Furthermore, the authors in [33] published a survey about AI solutions enhancing IoT security by presenting the challenges and limitations of algorithms. Besides the weak probability and instability of AI algorithms, they are computationally complex, with high resource consumption. Therefore, in this work, we analyze the AIS solutions to secure the IoT that are less complex for implementation with high detection probabilities.  
As businesses and consumers continue to connect devices to the Internet without proper security measures, IoT devices are increasingly leveraged by cybercriminals to dispense malware payloads [34]. In the first half of 2019, SonicWall observed a 55% increase in IoT attacks--a number that outpaces the first two quarters of the previous year. A security vendor has detected over 100 million attacks on IoT devices in the first half of 2019, which highlights the continued threat to unsecured IoT devices [35]. Kaspersky, the Russian Anti-Virus vendor, has claimed to detect 106 million attacks coming from 267,000 unique IP addresses in the first half of 2019 [35]. This number of attacks was almost nine times more than what was reported for the first quarter of 2018, when only 12 million were detected, originating from 69,000 IP addresses. According to the authors in [35], a major reason driving this surge is consumers' increased propensity to buy smart home solutions without due diligence in terms of security measures. Due to all the reasons listed above, malware attacks are major security threats in the IoT and thus require an IoT-specific security solution.  
The best way to secure the IoT based on its characteristics and architecture is to implement a distributed, dynamic, adaptive, and self-monitoring method. This leads us to investigate the AIS solutions and how these can be applied to secure the IoT against malware attacks.