Feature Selection for Network Intrusion Detection: An Information-Theoretic Approach
Date:
I will present my research on information-theoretic feature selection for network intrusion detection systems. This work introduces novel methods to identify minimal yet maximally discriminative features in cybersecurity datasets, significantly reducing computational overhead while maintaining state-of-the-art detection accuracy. The talk will include:
- Benchmark results against industry-standard datasets (CIC-IDS2017, NSL-KDD)
- Comparative analysis with traditional feature selection methods
- Practical implications for real-time network monitoring systems