System Malware Detection Using Machine Learning for Cybersecurity Risk and Management
Keywords:
Malware detection, Machine Learning, Cybersecurity, Zero-day vulnerabilities, Feature extractionAbstract
In the context of the relentless increase in the velocities and complexities of cyberattacks, malware remains one of the major cybersecurity threats that organizations, individuals, and governments are facing. Traditional signature-based detection systems can’t keep up with evolving zero-day threats. The focus of malware detection in this study is to enhance it using machine learning algorithms. With machine learning models, automatically analyzing vast volumes of data can pick malicious patterns and allow the evolution of such in real-time by matching the pace with emerging threats. The work contributes to showing that machine learning-based malware detection systems enhance both the accuracy of detection and resistance to new malware variants. These adjuncts reduce cybersecurity risks. The challenges of reducing false positives are also discussed in the work, with suggestions for optimized feature extraction methods that enhance the performance and scalability of the system.
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