PREDICTIVE MODELLING FOR NETWORK THREAT DETECTION USING ARTIFICIAL INTELLIGENCE TECHNIQUES

Author(s)	SHAIK MEHABOOB, R.DIVYA SREE, DR.M.SUJITHA, DR.M.NISHA, DR.G.SONIYA PRIYATHARSINI
Country	India
Abstract	The integration of artificial intelligence (AI) techniques has transformed network security by enabling predictive modeling for proactive threat detection. This research proposes a novel approach to enhancing network security through advanced AI-driven predictive analytics. By analyzing vast volumes of network traffic data, AI algorithms can identify patterns indicative of cyber threats, including malware, intrusions, and anomalous activities. The predictive models developed in this study can anticipate potential network vulnerabilities and detect emerging threats before they escalate into security breaches. This proactive approach strengthens network defenses, reduces the risk of cyberattacks, and safeguards critical data. By combining AI and predictive modeling, this research aims to establish a more resilient and adaptive network security framework in an increasingly interconnected digital landscape.
Keywords	Predictive modelling, Network security, Artificial intelligence, Threat detection, Cybersecurity, Machine learning, Anomaly detection, Predictive analytics, Network traffic analysis, Cyber threats.
Field	Engineering
Published In	Volume 16, Issue 2, April-June 2025
Published On	2025-04-09
DOI	https://doi.org/10.71097/IJSAT.v16.i2.3410

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International Journal on Science and Technology