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Object Detection in Cybersecurity: A Review of Automation of Malware Detection
Abstract
With the increase in malware attacks, the need for automated malware detection in cybersecurity has become more important. Traditional methods of malware detection, such as signature-based detection and heuristic analysis, are becoming less effective in detecting advanced and evasive malware. It has the potential to drastically improve the detection of malware, as well as reduce the manual efforts required in scanning and flagging malicious activity. This chapter also examines the advantages and limitations and the challenges associated with deploying object detection in cybersecurity, such as its reliance on labeled data, false positive rates, and its potential for evasion. Finally, the review presents the potential of object detection in cybersecurity, as well as the future research directions needed to make the technique more reliable and useful for cybersecurity professionals. It provides a comparison of the results obtained by these techniques with traditional methods, emphasizing the potential of object detection in detecting advanced and evasive malware.
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