International Journal of Broadband Cellular Communication
Bot Defender: A Collaborative Defense Framework for Botnet Intrusion Mitigation
1. Associate Professor, Department of E&TC, SKNCOE, SPPU, Pune
2. Assistant professor, Department of E&TC, SKNCOE, SPPU, Pune
3. Student, Department of E&TC, SKNCOE, SPPU, Pune
Abstract
Botnet attacks represent a significant threat to cybersecurity, compromising vast numbers of devices and causing extensive harm. BOT DEFENDER is a novel collaborative defense framework designed to mitigate botnet intrusions effectively. This framework leverages the power of collective intelligence, integrating data from multiple sources to detect and respond to botnet threats in real-time. By utilizing advanced machine learning algorithms, BOT DEFENDER can identify anomalous network behavior indicative of botnet activities, allowing for swift intervention. The framework's collaborative nature ensures a comprehensive defense strategy, enhancing the resilience of individual systems through shared threat intelligence. Experimental results demonstrate that BOT DEFENDER significantly reduces the impact of botnet intrusions, showcasing its potential as a robust solution for modern cybersecurity challenges. Bot Defender, a collaborative framework that protects against botnet attacks. Bot Defender combines a proposed network traffic analyzer and machine learning technique to prevent botnet attacks. The proposed network traffic analyzer performs an in-depth traffic analysis to detect bots and filter out all the traffic from the identified bots. It significantly reduces network traffic by filtering out a huge amount of traffic from the bots and transfers significantly reduced amounts of traffic to the machine learning model for further analysis. The machine learning models such as DT, XGBOOST is powered by a novel feature selection technique, an extended dataset construction technique inspired by human learning patterns and a stacking ensemble-based machine learning model, to detect bots. This proposed work exhibits a consistent performance of the proposed machine learning model. Finally, to evaluate the performance of Bot Defender, we design and develop a live botnet attack strategy
Collaborative framework, Network traffic analyzer, Machine learning, Bot detection,
Live botnet attack strategy
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