Experimenting and assessing machine learning tools for detecting and analyzing malicious behaviors in complex environments

被引：1
作者：
Cuzzocrea A. [1 ,2 ]
Martinelli F. [3 ]
Mercaldo F. [3 ]
Grasso G.M. [4 ]
机构：
[1] DIA Department, University of Trieste and ICAR-CNR, Trieste
[2] ICAR-CNR, Rende
[3] IIT Institute, National Research Council, Pisa
[4] COSPECS Department, University of Messina, Messina
来源：
Journal of Reliable Intelligent Environments | 2018年 / 4卷 / 04期
基金：
欧盟地平线“2020”;
关键词：
Complex environments; Machine learning; Security;
D O I：
10.1007/s40860-018-0072-3
中图分类号：
学科分类号：
摘要：
This paper proposes applying and experimentally assessing machine learning tools to solve security issues in complex environments, specifically identifying and analyzing malicious behaviors. To evaluate the effectiveness of machine learning algorithms to detect anomalies, we consider the following three real-world case studies: (i) detecting and analyzing Tor traffic, on the basis of a machine learning-based discrimination technique; (ii) identifying and analyzing CAN bus attacks via deep learning; (iii) detecting and analyzing mobile malware, with particular regard to ransomware in Android environments, by means of structural entropy-based classification. Derived observations confirm the effectiveness of machine learning in supporting security of complex environments. © 2018, Springer Nature Switzerland AG.
引用
收藏
页码：225 / 245
页数：20
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