This repository contains the code, dataset, images and materials for the research project titled "Comparative Analysis of Deep Learning and Machine Learning Models for Network Intrusion Detection," published in IEEE Xplore. This paper is presented in the 14th International Conference on Computing Communication and Networking Technologies (ICCCNT) 2023 held on August 2023 in IIT Delhi.
- Dharaneish V C 📧🔗👥
- Nithin Kumar S 📧👥
- Hari Varsha V 📧👥
- Senthil Kumar T (Professor, Guide) 📧🔗
- Gireesh Kumar T (Professor, Guide) 📧🔗👥
- Sulakshan Vajipayajula 📧
Department of Computer Science and Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India.
The increasing prevalence of security breaches and malicious software attacks is a major concern in the digital landscape, sparking continued interest in malware detection. Malware attacks have a significant impact on computer users, networks, businesses, organizations, and governments. Despite the development of multiple intrusion detection systems aimed at protecting data and resources from attacks, the frequent emergence of new threats and attacks poses a challenge for these systems to detect and prevent them from penetrating the network. One such attack is Advanced Persistent Threats (APTs) which can cause significant damage to computer network and organizations. To handle these attacks, the study has developed an APT detection system that uses various Machine learning (ML) and Deep Learning (DL) based classifiers, which can more effectively extract data features from huge amounts of complex data and understand patterns to detect anomalies and potential threats. This study compares and evaluate their performance and efficiency on NSL-KDD dataset. By evaluating using various evaluation metrics, it was found that Extreme Gradient Boosting (XGBoost) is the most effective model among all models, followed by Multi-Layer Perceptron (MLP) and Convolutional Neural Networks (CNN).
Intrusion Detection System, Network Security, Deep Learning, Machine Learning, Predictive Analytics
The dataset used in this study can be downloaded from UNB CIC Datasets.
This project is conducted under the IBM Shared University Research program.
To replicate the experiments and results presented in the paper, run all the cells in Python Notebook in order.
/NSLKDD_preprocess_8models.ipynb: Contains the source code for preprocessing and all the 8 machine learning and deep learning models./NSL-KDD: Dataset in csv format;/images: High quality pictures of all the ones in our published paper./NSL-KDD Features.xlsx- Description of each features in the NSL KDD datasetNSLKDD Dataset description.pdf- NSL KDD Dataset description and detailed analysis
If you find this work useful in your research, please cite our paper: https://ieeexplore.ieee.org/document/10308108/ https://doi.org/10.1109/ICCCNT56998.2023.10308108
This project is licensed under the MIT License.