ML Researcher · 2023

Network Intrusion Detection

University of Florida — ML Research

1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 1 1 1 1 0 0 1 0 1 1 0 1 0 0 1 1 1 1 1 0 1 0 0 1 1 1 0 1 0 1 1 1 0 0 1 0 0 1 1 1 1 0 0 1

The Challenge

Traditional signature-based IDS systems miss zero-day exploits, and the volume of modern network traffic makes manual analysis impossible. Security operations centers needed AI that classifies traffic in real-time with high accuracy.

The Approach

Built a comprehensive benchmarking framework with 8 distinct ML/DL algorithms enabling head-to-head comparison across binary and multi-class classification tasks on the NSL-KDD dataset.

The Solution

Deep Learning: Autoencoder, LSTM, MLP/DNN for high-accuracy classification
Traditional ML: KNN, LDA, QDA, SVM (Linear and Quadratic) for interpretable baselines
NSL-KDD dataset: 125,973 training records with 41 network features
Dual classification: Binary (Normal vs Attack) and Multi-class (5 attack types)

The Impact

0%+ LSTM Accuracy

0 Algorithms Compared

0 Attack Classes

0 Training Records

Tech Stack

Python TensorFlow NumPy Pandas LSTM Autoencoders SVM

“There is no single best model for intrusion detection. The right choice depends on your threat model, latency requirements, and need for interpretability.”

Read the Deep Dive Network Intrusion Detection: University to Production →

Next Case Study StealthWeb Enumerator →