Using Machine Learning To Detect And Prevent Cyber Attacks
Keywords:
Cyberattacks, Digital Age, Financial Losses, Data Breaches, Security Vulnerabilities, Machine Learning (ML), Cyber Threats.Abstract
Cyberattacks pose a significant challenge in the age of digital advancements, causing various sectors to experience financial losses and data breaches alongside security vulnerabilities. This paper researches the use of machine learning models in cyber threat detection and prevention by focusing on publicly accessible intrusion detection datasets, such as NSL-KDD, CIC-IDS 2017, and UNSW-NB15. A comparative evaluation of supervised and unsupervised learning techniques is done, where key performance metrics such as False Positive Rate (FPR) and Attack Mitigation Efficiency are taken into account. Based on the results, it shows that Neural Networks have achieved the highest mitigation efficiency in attacks (94.2%) and the lowest FPR (3.8%), making them the most effective model for cybersecurity applications. Random Forest also performed well with a mitigation efficiency of 90.5% and an FPR of 4.5%. K-Means Clustering, on the other hand, has a problem in that it has a high false positive rate of 7.4% and low detection accuracy. In conclusion, AI-driven security frameworks have the potential to improve the mechanisms of cyber defense and support the inclusion of advanced ML models to enhance the detection and prevention of threats.
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References
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