Anomaly Detection in Network Traffic Using Advanced Machine Learning Techniques

IEEE BASE PAPER TITLE:

Anomaly Detection in Network Traffic Using Advanced Machine Learning Techniques

OUR PROPOSED PROJECT TITLE:

Machine Learning based Intrusion Detection System for Detecting Various Attacks

IEEE BASE PAPER ABSTRACT:

Anomaly detection in network traffic is a critical aspect of network security, particularly in defending against the increasing sophistication of cyber threats. This study investigates the application of various machine learning models for detecting anomalies in network traffic, specifically focusing on their effectiveness in addressing challenges such as class imbalance and feature complexity. The models assessed include Isolation Forest, Naive Bayes, XGBoost, LightGBM, and SVM classification. Through comprehensive evaluation, this research explores both supervised and unsupervised approaches, comparing their performance across key metrics like accuracy, F1-score, and recall. The results reveal that while models like XGBoost and LightGBM exhibit impressive performance, with LightGBM achieving near-perfect training accuracy (1.0) and solid test accuracy (0.85), others like Isolation Forest show limitations with low accuracy. The study highlights the strengths and weaknesses of each model, providing valuable insights into their practical application for network anomaly detection. By comparing different algorithms, this research contributes to advancing the application of machine learning in network security, offering guidance on model selection and optimization for improved detection of cyber threats.

PROJECT OUTPUT VIDEO:

ALGORITHM / MODEL USED:

• CatBoost Classifier.
• ExtraTree Classifier.
• Gradient Boosting Classifier.

OUR PROPOSED PROJECT ABSTRACT:

The exponential growth of internet usage has heightened the need for robust cybersecurity mechanisms to detect and prevent malicious activities in network traffic. This project, titled “Anomaly Detection in Network Traffic Using Advanced Machine Learning Techniques” focuses on developing a Intrusion Detection System (IDS) that leverages powerful machine learning models to identify and classify various types of cyber-attacks. Built using Python as the core programming language, with HTML, CSS, and JavaScript for the front-end and Flask as the web framework, the system provides an interactive platform for real-time anomaly detection.

The solution integrates and evaluates three state-of-the-art classifiers: CatBoost Classifier, ExtraTree Classifier, and Gradient Boosting Classifier. Performance evaluation reveals exceptional accuracy, with both CatBoost and ExtraTree Classifiers achieving a remarkable 99.9% accuracy on training and test sets, while the Gradient Boosting Classifier closely follows with 99.8% accuracy.

The system is trained and tested on the KDD dataset, a widely used benchmark in the cybersecurity domain, comprising 494,021 instances and 42 attributes. The dataset encompasses various network intrusions such as back, buffer_overflow, ftp_write, neptune, smurf, and others, which are further categorized into four main attack classes: Probe, Denial of Service (DoS), Remote to Local (R2L), and User to Root (U2R).

For enhanced computational efficiency and model interpretability, the project focuses on 13 critical features, including duration, protocol type, flag, src_bytes, dst_bytes, count, and others that significantly influence anomaly detection. The classifier effectively distinguishes between normal traffic and attacks across the five target classes: Normal, Probe, DoS, R2L, and U2R.

This work demonstrates the effectiveness of machine learning in network intrusion detection, offering a scalable and high-accuracy solution that can be deployed in real-world cybersecurity infrastructure to monitor, detect, and mitigate potential threats in real-time.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

• System : Pentium i3 Processor.
• Hard Disk : 20 GB.
• Monitor : 15’’ LED.
• Input Devices : Keyboard, Mouse.
• Ram : 8 GB. 

SOFTWARE REQUIREMENTS: 

• Operating System : Windows 10 / 11.
• Coding Language : Python 3.12.0.
• Web Framework : Flask.
• Frontend : HTML, CSS, JavaScript.

REFERENCE:

STEPHANIE NESS, VISHWANATH ESWARAKRISHNAN, HARISH SRIDHARAN, VARUN SHINDE, NAGA VENKATA PRASAD JANAPAREDDY, AND VINEET DHANAWAT, “Anomaly Detection in Network Traffic Using Advanced Machine Learning Techniques”, IEEE ACCESS, VOLUME 13, 2025.

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Frequently Asked Questions (FAQ’s) & Answers:

1. What is the purpose of this project?

The project aims to detect anomalies or cyberattacks in network traffic using advanced machine learning models, providing a reliable intrusion detection system that classifies traffic into categories such as Normal, Probe, DoS, R2L, and U2R.

2. Which machine learning models are used in this system?

The project uses three machine learning models: CatBoost Classifier, ExtraTree Classifier, and Gradient Boosting Classifier.

3. What dataset is used for training the models?

The system uses the KDD dataset, which contains 494,021 instances with 42 attributes and various types of attack patterns.

4. How accurate are the models?

Both the CatBoost Classifier and ExtraTree Classifier achieved 99.9% accuracy on the training and test datasets. The Gradient Boosting Classifier achieved 99.8% accuracy.

5. How is the system developed?

The backend is built with Python, Flask is used as the web framework, and the frontend is developed using HTML, CSS, and JavaScript.

6. What types of attacks can the system detect?

The system can detect and classify a variety of attacks including back, buffer_overflow, ftp_write, guess_passwd, imap, ipsweep, neptune, nmap, perl, phf, pod, portsweep, rootkit, satan, smurf, spy, teardrop, warezclient, and warezmaster, categorized into four main classes: Probe, DoS, R2L, and U2R.

7. How many features are selected from the dataset for the models?

The system selects 13 important features, including Duration, Protocol Type, Flag, src_bytes, dst_bytes, count, srv_count, same_srv_rate, diff_srv_rate, dst_host_same_srv_rate, dst_host_srv_count, serror_rate, and srv_serror_rate.

8. Does the system provide any graphical output?

Yes, the system generates static visualization graphs and confusion matrices to help visualize the performance of each model.

9. Can the system handle large datasets?

Yes, the system is designed to handle large datasets efficiently, leveraging optimized machine learning models.

10. How do users interact with the system?

Users upload network traffic data files via the web interface and receive classification results along with performance visualizations.

11. What data processing steps done in this project?

Data Loading, Data Inspection, Data Transformation/Feature Engineering, Data Analysis

12. Is this system ready for real-time network monitoring?

Currently, the system processes uploaded data files, but it can be further extended for real-time monitoring with additional integration.

13. What is the key advantage of using multiple models?

Using multiple models allows comparison of their performances, ensuring robust and reliable intrusion detection under different scenarios.

14. What is the difference between the existing system and the proposed system?

The existing system used models like Isolation Forest, Naive Bayes, XGBoost, LightGBM, and SVM. While these models provided decent performance, particularly LightGBM and XGBoost, the proposed system introduces CatBoost Classifier, ExtraTree Classifier, and Gradient Boosting Classifier, achieving significantly higher accuracy (up to 99.9%). Additionally, the proposed system optimizes feature selection and provides an enhanced, user-friendly web interface for improved usability and result visualization.