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

作     者：Weijun Zhu Mingliang Xu Weijun Zhu;Mingliang Xu

作者机构：School of Information EngineeringZhengzhou UniversityZhengzhou 450001China 

出 版 物：《China Communications》 (中国通信（英文版）)

年 卷 期：2020年第17卷第4期

页      面：99-108页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0839[工学-网络空间安全] 081201[工学-计算机系统结构] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

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. 

主　　题：air-gapped computers disk filtration machine learning intrusion detection 

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%.