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Research Paper | Information Technology | India | Volume 12 Issue 4, April 2023 | Popularity: 3 / 10

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Cybersecurity Threat Prediction Using Machine Learning

Venkata Sai Swaroop Reddy Nallapa Reddy

Abstract: Machine learning (ML) has emerged as a pivotal force in cybersecurity, providing innovative solutions to detect, predict, and mitigate cyber threats in real-time. With the growing complexity and frequency of cyberattacks, traditional security systems often fail to address dynamic and evolving threats such as malware, phishing, Advanced Persistent Threats (APTs), and zero-day vulnerabilities. This paper explores the transformative role of ML in enhancing cybersecurity by leveraging techniques such as supervised learning for malware classification, unsupervised learning for anomaly detection, and deep learning for identifying complex attack patterns. By analyzing vast datasets, ML models can uncover subtle correlations and anomalies, enabling proactive threat detection and timely intervention. Integrating ML with existing security frameworks builds adaptive and robust systems capable of responding to emerging threats effectively. However, challenges such as adversarial attacks, data privacy concerns, and the need for explainability underscore the importance of ethical and responsible deployment. This paper provides a comprehensive review of ML applications in cybersecurity, emphasizing their potential to revolutionize threat prediction while addressing associated challenges. By combining historical data with real-time threat intelligence, ML not only enhances organizational defenses but also ensures resilience in the ever-changing digital landscape. This research underscores the necessity of continuous innovation and collaboration to refine ML-driven cybersecurity solutions and meet future challenges effectively.

Keywords: Machine Learning, Cybersecurity, Threat Prediction, Malware Detection, Anomaly Detection, Deep Learning, Real-Time Protection, Ethical AI, Adaptive Systems

Edition: Volume 12 Issue 4, April 2023

Pages: 1972 - 1976

DOI: https://www.doi.org/10.21275/SR23048115831

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