Malware Detection and Prevention using Artificial Intelligence Techniques
Md Jobair Hossain Faruk, Hossain Shahriar, Maria Valero, Farhat Lamia, Barsha, Shahriar Sobhan, Md Abdullah Khan, Michael Whitman, Alfredo, Cuzzocreak, Dan Lo, Akond Rahman, Fan Wu

TL;DR
This paper reviews AI-based techniques for malware detection and prevention, highlighting current technologies, their limitations, and future directions to improve cybersecurity defenses against malicious software.
Contribution
It provides a comprehensive review of AI, machine learning, and deep learning methods for malware detection, identifying gaps and proposing future research directions.
Findings
AI techniques enhance malware detection accuracy
Current methods have limitations in real-time detection
Future approaches can significantly improve cybersecurity
Abstract
With the rapid technological advancement, security has become a major issue due to the increase in malware activity that poses a serious threat to the security and safety of both computer systems and stakeholders. To maintain stakeholders, particularly, end users security, protecting the data from fraudulent efforts is one of the most pressing concerns. A set of malicious programming code, scripts, active content, or intrusive software that is designed to destroy intended computer systems and programs or mobile and web applications is referred to as malware. According to a study, naive users are unable to distinguish between malicious and benign applications. Thus, computer systems and mobile applications should be designed to detect malicious activities towards protecting the stakeholders. A number of algorithms are available to detect malware activities by utilizing novel concepts…
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