Deep Learning Based Cyber-Security for IoT: Malware Detection Through Image Analysis

As the IoT ecosystem expands, Cyber security threats become more complex, threatening traditional malware analysis techniques with significant challenges. Traditional detection techniques, both static and dynamic analysis approaches, are known to have weaknesses in scalability and evasions by sophisticated malware. Against this background, this paper presents a new approach applying deep learning and image processing techniques as a solution to these identified limitations in IoT security. We map malware into visually distinct patterns through the conversion of malware binaries to gray-scale images based on their similarity in visual features between malware families, and we classify these images using deep convolutional neural networks, thus allowing us to do multi-class malware categorization efficiently and accurately. We test our method on the Malimg dataset, comprising 9,349 images from 25 malware families, and prove that we can improve on the state-of-the-art in classification. Experimental assessment carried out on the Google Colab for malware demonstrates the superior performance of our approach integrated with the traditional approach in the detection of malware identification. This work thus offers a scalable, resource-efficient solution toward improving the Cyber security of IoT environments, presenting a strong alternative toward the current static and dynamic analysis limitations.