PREDICTING SUPPLY CHAIN CYBER THREAT WITH MACHINE LEARNING
PREDICTING SUPPLY CHAIN CYBER THREAT WITH MACHINE LEARNING
Authors:
1st N. Pranathi, 2nd K.Siddhartha, 3rd T.Supriya, 4th B. Venkateswarlu
Geethanjali College Of Engineering and Technology, Cheeryal Department of CSE – Data Science
Abstract - Supply chains, with the transition into hyper-connected digital ecosystems, naturally face an increasing threat level from acts of ransomware, insiders, or stealthy data exfiltration attacks. Conventional security systems, being naturally reactive, become ineffective in matching these evolving threats. In this paper, an infrastructure for proactive cyber threats suited for supply chain scenarios has been proposed. The network monitoring system is the first of its kind that, through the use of artificial intelligence, combines Cyber Threat Intelligence (CTI) and advanced analysis based on the establishment behavior of traffic, and employs machine learning tools that will contain such things as detection of anomalies and higher-order classification to ward off the attacks by full-scale incidents. The system is bolstered by a visualization board that displays the monitoring of the system process and the automated alerts that are accordingly triggered for the immediate establishment of the situation. Ultimately, the present research strives to introduce an intelligent and scalable paradigm through which the protection against possible hacker attacks will switch from just-defense to prevention thus increasing the resilience of the organizational networks.
Keywords: Anomaly detection, machine learning, cyber supply chain security, cyber threat intelligence, cyber threat prediction, and real-time monitoring are the main words for the content.