Effective Malware Detection for Embedded Computing Systems with Limited Exposure

arXiv:2404.02344v1 Announce Type: new
Abstract: One of the pivotal security threats for the embedded computing systems is malicious software a.k.a malware. With efficiency and efficacy, Machine Learning (ML) has been widely adopted for malware detection in recent times. Despite being efficient, the existing techniques require a tremendous number of benign and malware samples for training and modeling an efficient malware detector. Furthermore, such constraints limit the detection of emerging malware samples due to the lack of sufficient malware samples required …

arxiv computing cs.cr cs.cv detection efficiency embedded exposure machine machine learning malicious malicious software malware malware detection security security threats software systems techniques threats