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Intrusion detection system (IDS) using machine learning

Intrusion Detection Systems (IDS) play a critical role in safeguarding organizational networks by identifying potential threats and anomalies. However, traditional IDS approaches often suffer from high false alarm rates, leading to unnecessary alerts for administrators. This research focuses on enha...

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Bibliographic Details
Main Author: Tan, May May
Format: Final Year Project / Dissertation / Thesis
Published: 2024
Subjects:
T Technology (General)
TJ Mechanical engineering and machinery
Online Access:http://eprints.utar.edu.my/6498/1/fyp_CN_2024_TMM.pdf
http://eprints.utar.edu.my/6498/
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spelling my-utar-eprints.64982024-10-03T01:32:58Z Intrusion detection system (IDS) using machine learning Tan, May May T Technology (General) TJ Mechanical engineering and machinery Intrusion Detection Systems (IDS) play a critical role in safeguarding organizational networks by identifying potential threats and anomalies. However, traditional IDS approaches often suffer from high false alarm rates, leading to unnecessary alerts for administrators. This research focuses on enhancing machine learning and deep learning-based IDS models to improve accuracy, precision, recall, and F1 score, ultimately aiming for a more balanced and effective performance. The study leverages the CIC-IDS2017 dataset for model training, employing Random Forest (RF), Deep Neural Network (DNN), and Deep Autoencoder (DAE) architectures. A rigorous pre-processing phase, including data cleaning and feature selection using Pearson’s Correlation, enhances the dataset's quality and relevance. Subsequently, the refined dataset undergoes model training and testing. Hyperparameter tuning, facilitated by grid search, fine-tunes key features to optimize model performance. Evaluation metrics such as accuracy, precision, recall, and F1 score are employed to assess the models' efficacy in binary and multi-class classification tasks. Results demonstrate significant improvements and balanced performance compared to previous research models, achieving an average performance of 99.5% across all models. 2024-01 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/6498/1/fyp_CN_2024_TMM.pdf Tan, May May (2024) Intrusion detection system (IDS) using machine learning. Final Year Project, UTAR. http://eprints.utar.edu.my/6498/
institution Universiti Tunku Abdul Rahman
building UTAR Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tunku Abdul Rahman
content_source UTAR Institutional Repository
url_provider http://eprints.utar.edu.my
topic T Technology (General)
TJ Mechanical engineering and machinery
spellingShingle T Technology (General)
TJ Mechanical engineering and machinery
Tan, May May
Intrusion detection system (IDS) using machine learning
description Intrusion Detection Systems (IDS) play a critical role in safeguarding organizational networks by identifying potential threats and anomalies. However, traditional IDS approaches often suffer from high false alarm rates, leading to unnecessary alerts for administrators. This research focuses on enhancing machine learning and deep learning-based IDS models to improve accuracy, precision, recall, and F1 score, ultimately aiming for a more balanced and effective performance. The study leverages the CIC-IDS2017 dataset for model training, employing Random Forest (RF), Deep Neural Network (DNN), and Deep Autoencoder (DAE) architectures. A rigorous pre-processing phase, including data cleaning and feature selection using Pearson’s Correlation, enhances the dataset's quality and relevance. Subsequently, the refined dataset undergoes model training and testing. Hyperparameter tuning, facilitated by grid search, fine-tunes key features to optimize model performance. Evaluation metrics such as accuracy, precision, recall, and F1 score are employed to assess the models' efficacy in binary and multi-class classification tasks. Results demonstrate significant improvements and balanced performance compared to previous research models, achieving an average performance of 99.5% across all models.
format Final Year Project / Dissertation / Thesis
author Tan, May May
author_facet Tan, May May
author_sort Tan, May May
title Intrusion detection system (IDS) using machine learning
title_short Intrusion detection system (IDS) using machine learning
title_full Intrusion detection system (IDS) using machine learning
title_fullStr Intrusion detection system (IDS) using machine learning
title_full_unstemmed Intrusion detection system (IDS) using machine learning
title_sort intrusion detection system (ids) using machine learning
publishDate 2024
url http://eprints.utar.edu.my/6498/1/fyp_CN_2024_TMM.pdf
http://eprints.utar.edu.my/6498/
_version_ 1814061966998634496
score 13.211869

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