Articolo in rivista, 2021, ENG, 10.1109/ACCESS.2020.3048319
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
malware, detection, machine learning, information hiding, cybersecurity
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