A Novel Negative and Positive Selection Algorithm to Detect Unknown Malware in the IoT

The Internet of Things (IoT) paradigm is a key enabler to many critical applications, thus demands reliable security measures. IoT devices have limited computational power, hence, are inadequate to carry rigorous security mechanisms. This paper proposes the Negative-Positive-Selection (NPS) method which uses an artificial immunity system technique for malware detection. NPS is suitable for the computation restrictions and security challenges associated with IoT. The performance of NPS is benchmarked against state-of-the-art malware detection schemes using a real dataset. Our results show a 21% improvement in malware detection and a 65% reduction in the number of detectors. NPS meets IoT-specific requirements as it outperforms other malware detection mechanisms whilst having less demanding computational requirements.