Using a public dataset, the experimental results showed that the SVM linear function kernel is the best with an accuracy of 95.56%, recall of 96.43%, precision of 94.12%, and F1_score of 95.26%. Further, the PSO algorithm tuned the SVM parameters of the different kernel functions. In this method, the ant colony optimizer (ACO)-based feature selection method was proposed to get a minimum number of features while improving the support vector machines (SVMs) classifier’s results (i.e., the malware detection results). In this paper, a novel optimized machine learning image-based IoT malware detection method is proposed using visual representation (i.e., images) of the network traffic. As a result, improved network security mechanisms that can analyse network traffic and detect malicious traffic in real-time are required. Without robust security measures, a massive volume of confidential and classified data could be exposed to vulnerabilities through which hackers could do various illicit acts.

With the widespread use of IoT applications, malware has become a difficult and sophisticated threat.

Avast vs avg reddit 2016