• Sujith Shaju
• Gracemol Thankachan
• Mr. Mervin George
• Mrs. Vineetha Varghese

This paper presents a proactive approach to early threat detection in corporate cybersecurity. By leveraging machine learning (ML), it analyses network traffic data to identify patterns indicative of malicious activity. The business context involves the role of a cybersecurity expert tasked with summarizing network traffic data to uncover patterns, trends, and anomalies. Key problem statements include identifying frequently targeted destination IP addresses, detecting the most attacked logical ports, classifying common attack types, and uncovering temporal attack patterns. Methodology includes preprocessing and analysing historical network traffic data using ML techniques to learn and identify threat patterns and anomalies. Expected outcomes encompass the development of a robust threat detection system, enhancing cybersecurity posture, and ensuring business continuity.

Cybersecurity, ML, ANNs, threat detection, network traffic analysis, pattern recognition, anomaly detection.

"INTEGRATED MACHINE LEARNING TECHNIQUES FOR EARLY DETECTION OF WEB-BASED ATTACKS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 3, page no.i438-i447, March-2024, Available :http://www.jetir.org/papers/JETIR2403855.pdf

"INTEGRATED MACHINE LEARNING TECHNIQUES FOR EARLY DETECTION OF WEB-BASED ATTACKS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 3, page no. ppi438-i447, March-2024, Available at : http://www.jetir.org/papers/JETIR2403855.pdf