IDS with Machine Learning: Simulating Cyberattacks (⚔️ Simulating Attacks. Strengthening Defenses)

🧠 Motivations: Why Build an ML‑Powered IDS?
When it comes to securing networks, Intrusion Detection Systems (IDS) are vital. The goal of this project is twofold:

Understand cyber‑attack behavior by simulating various intrusion methods.

Train machine learning models—like Random Forest, K‑Nearest Neighbors, and SVM—to detect malicious activity in real-time.

This isn’t about deploying a hardened product-ready system; instead, it’s a hands-on, educational deep dive into how AI can aid in cybersecurity defense exploration—open-source, modifiable, and perfect for learning.

📚 Dataset & Attack Types
The project leverages the NSL‑KDD dataset—a cleaned-up and improved version of the classic KDD’99 intrusion dataset. It includes labels such as:

• DoS (Denial of Service)
• Probe (reconnaissance attempts)
• R2L (Remote-to-Local breaches)
• U2R (User-to-Root exploits)

Its reduced redundancy makes training and performance metrics more reliable. Through this dataset, you get a realistic sandbox to simulate and detect threats.

⚙️ Implementation: From Data to Detection

1. Preprocessing & Feature Selection

• Encode categorical network features
• Normalize/remont standardize continuous attributes
• Analyze feature importance

1. Model Training & Comparison

• Train Random Forest, KNN, and SVM
• Evaluate based on accuracy, recall, precision—especially for each attack class

1. Results

• Typically, Random Forest leads in overall accuracy
• KNN and SVM may excel at detecting specific attack types
• The evaluation module helps you see where models perform best or struggle

🛠️ Why It Matters

• Learning: Ideal for students and new cybersecurity enthusiasts to build a data-driven intrusion detection pipeline.
• Comparative Study: See how tree-based, instance-based, and margin-based algorithms handle network threats.
• Extensible: Swap in deep learning models, integrate streaming data, add alerting pipelines, or use more recent datasets.

🌐 Check It Out on GitHub
Clone and explore the code here:

https://github.com/Aishwarya2701/IDS-with-ML

The repo includes:

• Data loaders and preprocessors
• Training scripts for all three ML models
• Scripts for in-depth evaluation and comparison
• Detailed README to help you get started quickly

🚀 Next Steps You Can Take

• Add Deep Learning: Try CNNs, RNNs, or transformers on network traffic.
• Streamline for live data: Integrate Kafka or Flink for real-time detection.
• Improve Feature Engineering: Use PCA, anomaly scores, or window-based features.
• Enhance Visualization: Create dashboards with confusion matrices, ROC curves, or decision boundaries.

🎯 Final Thoughts
This project is a fantastic entry point into cybersecurity and machine learning. It walks you through simulating real-world attacks, extracting meaningful features, and selecting the best algorithm for detection. Whether you're a student, educator, or hobbyist, there's ample room to extend and adapt this system.

💬 Your Turn
Curious which ML model worked best? Want help integrating anomaly-based detection or visual dashboards? Let’s discuss in the comments!

Let’s simulate to secure.