Articolo in rivista, 2021, ENG, 10.1109/ACCESS.2020.3048319

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

L. Caviglione, M. Choras, I. Corona, A. Janicki, W. Mazurczyk, M. Pawlicki, K. Wasielewska

National Research Council of Italy; FernUniversitaet in Hagen; PluribusOne; Warsaw University of Technology; ITTI Sp. z o.o; UTP University of Science and Technology, Bydgoszcz; State University of Applied Sciences, Elblag.

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. On this basis, we review the evolution of modern threats in the communication networks and we present the bird's eye view portraying the main development trends in detection methods with a special emphasis on the machine learning techniques.

IEEE access 9 , pp. 5371–5396

Keywords

malware, detection, machine learning, information hiding, cybersecurity

CNR authors

Caviglione Luca

CNR institutes

IMATI – Istituto di matematica applicata e tecnologie informatiche "Enrico Magenes"

ID: 440980

Year: 2021

Type: Articolo in rivista

Creation: 2021-01-04 10:26:45.000

Last update: 2021-05-18 12:56:57.000

CNR authors

External IDs

CNR OAI-PMH: oai:it.cnr:prodotti:440980

DOI: 10.1109/ACCESS.2020.3048319

ISI Web of Science (WOS): 000608188900001

Scopus: 2-s2.0-85099080569