We propose a fast ransomware detection method using Memory-Assisted-Stochastic-Dynamic-Fixed-Point arithmetic using a four-layer Deep Belief Network (DBN) structure. The method stores random bit-streams in memory to produce efficient cross-correlation for the stochastic computation in FPGA. The memory technique for stochastic computation with dynamic fixed-point arithmetic trains the Deep Belief Network (DBN) to detect ransomwares with 91% precision rate and detection speed of.006ms. The method represents a promising step toward improving ransomware detection in devices with limited power and memory resources such as the Internet of Things (IoTs).
Ransomware Detection Using Limited Precision Deep Learning Structure in FPGA
01.07.2018
1572958 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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