A Novel Method for Detecting Disk Filtration Attacks via the Various Machine Learning Algorithms  

作　　者：Weijun Zhu Mingliang Xu 

机构地区：[1]School of Information Engineering,Zhengzhou University,Zhengzhou 450001,China

出　　处：《China Communications》2020年第4期99-108,共10页中国通信（英文版）

基　　金：supported by the National Natural Science Foundation of China under grant No.U1204608,No.61472370,No.61672469 and No.61822701;the National Key R&D Program of China under grant No.2016YFB0800100.

摘　　要：Disk Filtration(DF) Malware can attack air-gapped computers. However, none of the existing technique can detect DF attacks. To address this problem, a method for detecting the DF attacks based on the fourteen Machine Learning(ML) algorithms is proposed in this paper. First, we collect a number of data about Power Spectral Density(PSD) and frequency of the sound wave from the Hard Disk Drive(HDD). Second, the corresponding machine learning models are trained respectively using the collected data. Third, the trained ML models are employed to detect whether a DF attack occurs or not respectively, if given pair of values of PSD and frequency are input. The experimental results show that the max accuracy of detection is greater than or equal to 99.4%.

关 键 词：air-gapped COMPUTERS DISK FILTRATION machine learning INTRUSION detection 

分 类 号：TP309[自动化与计算机技术—计算机系统结构] TP333.35[自动化与计算机技术—计算机科学与技术]