Multi-Class, Multi-Tier Network Intrusion Detection: A Comprehensive and Reproducible Benchmark

Machine learning ML and deep learning DL have dominated Intrusion Detection System IDS research in recent years. Unfortunately, many existing studies have produced inflated results and unreliable benchmarks due to critical oversights and mistakes in the ML and DL pipeline, from data collection an...

Few-Shot Learning for Network Intrusion Detection: Methods, Datasets, and Performance

Anomaly-based network intrusion detection systems NIDS are an important first line of defense. However, training NIDS for new attack types is challenging, because labeled attack data are rarely available. Few-shot learning FSL addresses this problem by learning from few samples. However, the...

A Calibrated and Explainable Bimodal Machine Learning Framework for Hybrid Intrusion Detection

Modern communication systems face critical gaps in detecting unknown attacks and rare threat classes due to extreme data imbalance and black-box decision logic. We propose a bimodal framework of calibrated and explainable machine learning ML for network security, unifying known-class precision wi...

Hybrid Topological Data Analysis and LSTM Networks for Enhanced Network Intrusion Detection Using CIC-IDS2017 Dataset

Network intrusion detection systems NIDS are crucial in cybersecurity infrastructure, needing advanced techniques to detect hostile activity in network traffic. This research introduces a hybrid approach that combines Topological Data Analysis TDA with Long Short-Term Memory LSTM networks to...