Effective Malware Detection for Embedded Computing Systems with Limited Exposure

arXiv:2404.02344v1 Announce Type: cross
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 …

abstract arxiv computing computing systems cs.cr cs.cv detection efficiency embedded machine machine learning malware malware detection pivotal security software systems threats type