학술논문

A Review of State-of-the-Art Malware Attack Trends and Defense Mechanisms
Document Type
article
Source
IEEE Access, Vol 11, Pp 121118-121141 (2023)
Subject
Malware evolution
malware attack trends
defense mechanisms
malware detection
machine learning
deep learning
Electrical engineering. Electronics. Nuclear engineering
TK1-9971
Language
English
ISSN
2169-3536
Abstract
The increasing sophistication of malware threats has led to growing concerns in the anti-malware community, as malware poses a significant danger to online users despite the availability of numerous defense solutions. This study aims to comprehensively review malware evolution and current attack trends to identify effective defense mechanisms. It reviews the most recent journal articles, conference proceedings, reports, and online resources published during the last five years. We extensively review the malware landscape from 1970 to the present and analyze malware types, operational mechanisms, attack vectors, and vulnerabilities. Furthermore, we explore different defensive strategies developed in response to these evolving threats. Our findings highlight the increasing sophistication of malware attack trends, including a surge in cryptojacking, attacks on mobile devices, Internet of Things devices, ransomware, advanced persistent threats, supply chain attacks, fileless malware, cloud-based attacks, exploitation of remote employees, and attack trends on edge networks. Defense strategies have also evolved in parallel, emphasizing multilayered security measures to counter these dynamic threats. This study highlights the critical need for robust, multilayered security measures to combat dynamic malware. Despite these advancements, some open challenges and significant research gaps remain, which require further innovation. This review serves as a valuable guide for cybersecurity professionals by identifying the key trends, challenges, limitations, and future cybersecurity research opportunities.