🚀 IntrusionX: SSA-Optimized Conv–LSTM for Network Intrusion Detection

📌 Overview

IntrusionX is a hybrid deep learning framework for Network Intrusion Detection (IDS).
It combines Convolutional Neural Networks (CNNs) for feature extraction with Long Short-Term Memory (LSTM) networks for temporal learning.
To optimize hyperparameters, we use the Squirrel Search Algorithm (SSA) — a lightweight swarm intelligence method.

Our pipeline includes leak-free preprocessing, stratified data splitting, and dynamic class weighting, ensuring improved performance on rare attack classes.

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✨ Key Results

• Binary classification (Normal vs Attack): 98% accuracy, AUC = 0.9986
• 5-class classification (DoS, Probe, R2L, U2R, Normal): 87% accuracy, Weighted F1 = 0.90
• Rare-class recall: R2L = 93%, U2R = 71%

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⚡ Quick Start

1. Clone the repo:

git clone https://github.com/TheAhsanFarabi/IntrusionX.git
cd IntrusionX