• Praneeth Narisetty
• Pavan Narra

Several vulnerabilities in the computing environment are exploited by cyber-criminals everywhere. Ethical Hackers are increasingly concerned with assessing vulnerabilities and recommending mitigation strategies. It has been urgent for the community of cyber security professionals to develop effective techniques. Cyber-attacks on computer networks are dynamic and complex, making most techniques used in today’s IDS unsuitable for handling them. As a result of machine learning's effectiveness in cybersecurity issues, machine learning for cyber security has become a major issue recently. Intruder detection, malware classification and detection, spam detection, and phishing detection have all been addressed with machine learning techniques. In spite of the fact that machine learning cannot completely automate cyber security, it is better suited to identifying cyber security threats than other software-oriented approaches, which in turn reduces the workload of security analysts. Hence, efficient adaptive methods like various techniques of machine learning can result in higher detection rates, lower false alarm rates and reasonable computation and communication costs. We believe the task of detecting attacks is fundamentally different from these other applications, so it is extremely difficult for the intrusion detection community to apply machine learning effectively.

"A MACHINE LEARNING APPROACH FOR DETECTING CYBERATTACKS IN NETWORKS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 6, page no.g26-g31, June-2022, Available :http://www.jetir.org/papers/JETIR2206605.pdf

"A MACHINE LEARNING APPROACH FOR DETECTING CYBERATTACKS IN NETWORKS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 6, page no. ppg26-g31, June-2022, Available at : http://www.jetir.org/papers/JETIR2206605.pdf