• Divya D P
• Divyashree S
• Ananya Vinayak Shanbhag
• Bhuvana H R
• Deepthi M

Intrusion-Detection Systems (IDS) play a vital part in preserving computer systems and networks from unauthorized access, attacks, and malicious activities. With the ever-evolving landscape of cyber threats, the requirement for resilient and flexible intrusion detection approaches becomes paramount. One such approach involves employing artificial intelligence algorithms, and in this instance, the Random-Forest algorithm appears as a powerful tool. Utilizing machine learning (ML) to detect intrusions involves leveraging computational algorithms and statistical models to automatically identify abnormal activities or potential security threats within a computer system or network. Conventional intrusion detection methods frequently rely on rule-based approaches, but machine learning introduces a more adaptive and data-driven paradigm. Random Forest is a popular and efficient choice for intrusion-detection due to several key advantages that address matters pertaining to the nature of cybersecurity data and the requirements of intrusion-detection systems. The utilization of a Random Forest-based Intrusion-Detection System (IDS) is a noteworthy development in enhancing the cybersecurity posture of a network or system. Utilize ensemble learning, feature importance analysis, and adaptability to diverse patterns positions Random Forest as a robust and efficient solution for identifying both known and novel threats.

IMPLEMENTATION PAPER ON CYBER THREATS DETECTION APPLYING RANDOM - FOREST ALGORITHM

"IMPLEMENTATION PAPER ON CYBER THREATS DETECTION APPLYING RANDOM - FOREST ALGORITHM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.246-252, May-2024, Available :http://www.jetir.org/papers/JETIRGG06040.pdf

"IMPLEMENTATION PAPER ON CYBER THREATS DETECTION APPLYING RANDOM - FOREST ALGORITHM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. pp246-252, May-2024, Available at : http://www.jetir.org/papers/JETIRGG06040.pdf