Machine Learning for Cybersecurity: Innovations, Threats, and Defense Strategies

Prof. Kanta sham

Abstract


The application of Machine Learning (ML) in cybersecurity stands as a transformative approach in combating evolving threats while fortifying defense strategies. This abstract navigates through the realm of ML, examining its pivotal role in cybersecurity by deciphering innovative techniques for threat detection, anomaly identification, and predictive analysis within complex networks. However, alongside these advancements, the abstract sheds light on the formidable challenges, including adversarial attacks, data poisoning, and the ethical implications of automated decision-making. Furthermore, it investigates the critical need for resilient defense strategies, emphasizing the amalgamation of ML with traditional cybersecurity methods to fortify resilience against sophisticated threats, thereby paving the way for a more secure digital landscape

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