Machine Learning For Cybersecurity: Enhancing Intrusion Detection Systems And Threat Mitigation
Keywords:
Machine learning, cybersecurity, intrusion detection, threat mitigation, adversarial attacksAbstract
The time of sophistication and frequency of
cyberattacks demands more sophisticated security
mechanisms. In response, intrusion detection and
threat mitigation became a powerful machine
learning (ML) problem since it offers automated,
real-time responses to cyber threats. In this study,
we look at ML-based intrusion detection systems,
and threat mitigation techniques as well as ML’s
implementation challenges for cybersecurity. The
issues of adversarial attacks, data privacy concerns,
and model interpretability are discussed in the
paper. Through the effort to solve these challenges
and the improvement of ML-based security
frameworks, organizations can improve their cyber
security defenses against even more evolving cyber
threats. The future course of research should focus
on how to improve model robustness, as well as how
to incorporate cybersecurity into the element of
ethical considerations.
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