Metaheuristic-Based Neural Network Training And Feature Selector For Intrusion Detection
Intrusion Detection (ID) in the context of computer networks is an essential technique in modern defense-in-depth security strategies. As such, Intrusion Detection Systems (IDSs) have received tremendous attention from security researchers and professionals. An important concept in ID is anomaly det...
| Main Author: | |
|---|---|
| Format: | Thesis |
| Language: | English |
| Published: |
2019
|
| Subjects: | |
| Online Access: | http://eprints.usm.my/46632/ http://eprints.usm.my/46632/1/WaheedGhanem-Phd201924.pdf |
| _version_ | 1848880659344392192 |
|---|---|
| author | Ghanem, Waheed Ali Hussein Mohammed |
| author_facet | Ghanem, Waheed Ali Hussein Mohammed |
| author_sort | Ghanem, Waheed Ali Hussein Mohammed |
| building | USM Institutional Repository |
| collection | Online Access |
| description | Intrusion Detection (ID) in the context of computer networks is an essential technique in modern defense-in-depth security strategies. As such, Intrusion Detection Systems (IDSs) have received tremendous attention from security researchers and professionals. An important concept in ID is anomaly detection, which amounts to the isolation of normal behavior of network traffic from abnormal (anomaly) events. This isolation is essentially a classification task, which led researchers to attempt the application of well-known classifiers from the area of machine learning to intrusion detection. Neural Networks (NNs) are one of the most popular techniques to perform non-linear classification, and have been extensively used in the literature to perform intrusion detection. However, the training datasets usually compose feature sets of irrelevant or redundant information, which impacts the performance of classification, and traditional learning algorithms such as backpropagation suffer from known issues, including slow convergence and the trap of local minimum. Those problems lend themselves to the realm of optimization. Considering the wide success of swarm intelligence methods in optimization problems, the main objective of this thesis is to contribute to the improvement of intrusion detection technology through the application of swarm-based optimization techniques to the basic problems of selecting optimal packet features, and optimal training of neural networks on classifying those features into normal and attack instances. To realize these objectives, the research in this thesis follows three basic stages, succeeded by extensive evaluations. |
| first_indexed | 2025-11-15T18:06:38Z |
| format | Thesis |
| id | usm-46632 |
| institution | Universiti Sains Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T18:06:38Z |
| publishDate | 2019 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | usm-466322020-06-26T08:38:56Z http://eprints.usm.my/46632/ Metaheuristic-Based Neural Network Training And Feature Selector For Intrusion Detection Ghanem, Waheed Ali Hussein Mohammed QA75.5-76.95 Electronic computers. Computer science Intrusion Detection (ID) in the context of computer networks is an essential technique in modern defense-in-depth security strategies. As such, Intrusion Detection Systems (IDSs) have received tremendous attention from security researchers and professionals. An important concept in ID is anomaly detection, which amounts to the isolation of normal behavior of network traffic from abnormal (anomaly) events. This isolation is essentially a classification task, which led researchers to attempt the application of well-known classifiers from the area of machine learning to intrusion detection. Neural Networks (NNs) are one of the most popular techniques to perform non-linear classification, and have been extensively used in the literature to perform intrusion detection. However, the training datasets usually compose feature sets of irrelevant or redundant information, which impacts the performance of classification, and traditional learning algorithms such as backpropagation suffer from known issues, including slow convergence and the trap of local minimum. Those problems lend themselves to the realm of optimization. Considering the wide success of swarm intelligence methods in optimization problems, the main objective of this thesis is to contribute to the improvement of intrusion detection technology through the application of swarm-based optimization techniques to the basic problems of selecting optimal packet features, and optimal training of neural networks on classifying those features into normal and attack instances. To realize these objectives, the research in this thesis follows three basic stages, succeeded by extensive evaluations. 2019-04 Thesis NonPeerReviewed application/pdf en http://eprints.usm.my/46632/1/WaheedGhanem-Phd201924.pdf Ghanem, Waheed Ali Hussein Mohammed (2019) Metaheuristic-Based Neural Network Training And Feature Selector For Intrusion Detection. PhD thesis, Universiti Sains Malaysia. |
| spellingShingle | QA75.5-76.95 Electronic computers. Computer science Ghanem, Waheed Ali Hussein Mohammed Metaheuristic-Based Neural Network Training And Feature Selector For Intrusion Detection |
| title | Metaheuristic-Based Neural Network
Training And Feature Selector For
Intrusion Detection |
| title_full | Metaheuristic-Based Neural Network
Training And Feature Selector For
Intrusion Detection |
| title_fullStr | Metaheuristic-Based Neural Network
Training And Feature Selector For
Intrusion Detection |
| title_full_unstemmed | Metaheuristic-Based Neural Network
Training And Feature Selector For
Intrusion Detection |
| title_short | Metaheuristic-Based Neural Network
Training And Feature Selector For
Intrusion Detection |
| title_sort | metaheuristic-based neural network
training and feature selector for
intrusion detection |
| topic | QA75.5-76.95 Electronic computers. Computer science |
| url | http://eprints.usm.my/46632/ http://eprints.usm.my/46632/1/WaheedGhanem-Phd201924.pdf |