Abstract: Malware detection is a critical component of modern cybersecurity, as malicious software poses a substantial threat to the security and privacy of individuals and organizations. Traditional signature-based approaches to malware detection have limitations in identifying new, previously unseen malware variants. Machine learning has emerged as a powerful tool in this domain, offering the ability to detect malware based on patterns and behaviour’s rather than relying solely on known signatures. These abstract highlights the key aspects of using machine learning for malware detection. Machine learning algorithms are capable of analysing large datasets of file characteristics, network traffic, and system behaviours to identify subtle and evolving patterns associated with malware. By employing techniques such as deep learning, decision trees, and support vector machines, these algorithms can generalize from labelled training data to make predictions about the nature of unknown files or activities. Additionally, feature engineering and feature selection processes enhance the ability of machine learning models to distinguish between benign and malicious entities effectively. The dynamic nature of malware necessitates real-time or near-real-time detection methods. Machine learning enables the development of predictive models that continuously adapt to new threats, making it possible to stay ahead of evolving malware variants. Moreover, the integration of machine learning with other security measures, such as anomaly detection and threat intelligence, further enhances the overall efficacy of cybersecurity systems.
Keywords: Malware detection, Machine learning, Behavioural analysis, Decision trees, Feature engineering.
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DOI:
10.17148/IJARCCE.2024.13453
