Empowering Malware Detection Efficiency within Processing-in-Memory Architecture

arXiv:2404.08818v1 Announce Type: new
Abstract: The widespread integration of embedded systems across various industries has facilitated seamless connectivity among devices and bolstered computational capabilities. Despite their extensive applications, embedded systems encounter significant security threats, with one of the most critical vulnerabilities being malicious software, commonly known as malware. In recent times, malware detection techniques leveraging Machine Learning have gained popularity. Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs) have proven particularly efficient in image processing tasks. However, one major …

applications architecture arxiv capabilities computational connectivity critical critical vulnerabilities cs.ar cs.cr detection devices efficiency embedded embedded systems industries integration malicious malicious software malware malware detection memory security security threats software systems threats vulnerabilities