Semantics-Based Online Malware Detection: Towards Efficient Real-Time Protection Against Malware
Abstract— Recently, malware has increasingly become a critical threat to embedded systems, while the conventional software solutions, such as antivirus and patches, have not been so successful in defending the ever-evolving and advanced malicious programs. we propose a hard ware enhanced architecture, GuardOL, to perform online malware detection. GuardOL is a combined approach using processor and ﬁeld-programmable gate array < Final Year Projects 2016 > Our approach aims to capture the malicious behavior (i.e., highlevel semantics) of malware. To this end, we ﬁrst propose the frequency-centric model for feature construction using system call patterns of known malware and benign samples. We then develop a machine learning approach (using multilayer perceptron) in FPGA to train classiﬁer using these features.