Tight Arms Race: Overview of Current Malware Threats and Trends in Their Detection

IEEE Access

Wojciech Mazurczyk¹  Marek Pawlicki²  Michal Choras²  Luca Caviglione³  Artur Janicki⁴  Igino Corona⁵  Katarzyna Wasielewska⁵ 

[1] Faculty of Electronics and Information Technology, Warsaw University of Technology, Warsaw, Poland;Faculty of Mathematics and Computer Science, FernUniversit&x00E4;Institute for Applied Mathematics and Information Technologies, National Research Council of Italy, Genova, Italy;Pluribus One Srl, Cagliari, Italy;t in Hagen, Hagen, Germany;

关键词: Cyber security;    information hiding;    machine learning;    malware;    threat detection;   

DOI  :  10.1109/ACCESS.2020.3048319

来源: DOAJ

【 摘 要 】

Cyber attacks are currently blooming, as the attackers reap significant profits from them and face a limited risk when compared to committing the “classical” crimes. One of the major components that leads to the successful compromising of the targeted system is malicious software. It allows using the victim's machine for various nefarious purposes, e.g., making it a part of the botnet, mining cryptocurrencies, or holding hostage the data stored there. At present, the complexity, proliferation, and variety of malware pose a real challenge for the existing countermeasures and require their constant improvements. That is why, in this paper we first perform a detailed meta-review of the existing surveys related to malware and its detection techniques, showing an arms race between these two sides of a barricade. On this basis, we review the evolution of modern threats in the communication networks, with a particular focus on the techniques employing information hiding. Next, we present the bird's eye view portraying the main development trends in detection methods with a special emphasis on the machine learning techniques. The survey is concluded with the description of potential future research directions in the field of malware detection.

【 授权许可】

Unknown