• T.Venkata Subba Rao
• N.Naveen
• L.Chaitanya Sai
• S.Sai Ganesh
• B.Srinivasa Rao

The escalating complexity and volume of cyberattacks pose a significant threat to individuals, organizations, and critical infrastructure. Machine learning techniques offer a powerful tool in combating these threats by enabling proactive detection and mitigation. This project specifically employs the random forest algorithm to enhance cybersecurity threat detection. Random forests, consisting of an ensemble of decision trees, are renowned for their robustness and accuracy in classification tasks. The model is trained on a comprehensive dataset of cybersecurity threats, learning to discern patterns indicative of malicious activity. The trained model can then analyze network traffic, system logs, or other relevant data to identify potential cyberattacks in real-time. This project aims to improve detection rates 96% and reduce false positives compared to traditional signature-based cybersecurity systems.

Cybersecurity, Machine Learning, Random Forest Algorithm, Threat Detection, Proactive, Ensemble Learning, Decision Trees, Classification, Robustness, Accuracy, Dataset, Malicious Activity, Network Traffic Analysis, Real-Time Detection, False Positives, Signature-Based Systems

"DETECTING CYBER SECURITY THREATS USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.d580-d589, May-2024, Available :http://www.jetir.org/papers/JETIR2405364.pdf

"DETECTING CYBER SECURITY THREATS USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. ppd580-d589, May-2024, Available at : http://www.jetir.org/papers/JETIR2405364.pdf