Vulnerability Detection In Firmware Based On Clonal Selection Algorithm

With the security breaches in Internet of Things devices, the detection of firmware vulnerability is more crucial than ever. Presently, many methods for firmware vulnerability detection have been proposed, but there are still some room for improvement on the detection precision. In this paper, we propose to use clonal selection algorithm to detect vulnerability functions in firmware. Firstly, we use the Relief algorithm to select the features that are more suitable for clonal selection algorithm. Then, we utilize principal component analysis algorithm to calculate the weights of the features. In the process of detection, we establish a set of specific detectors for each vulnerability function. In the end, we detect the vulnerability functions through these specific detectors. The experimental results show that the precision of our approach on detecting real vulnerabilities is competitive to the typical algorithm VDNS which is based on the neural network.