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The UTC Graduate School is pleased to announce that Milan Artis will present Master's research titled, A Comparative Study of Network Intrusion Detection Using Classical Machine Learning Methods and Deep Neural Networks on 06/28/2024 at 1pm-2:30pm in Remote (zoom) Everyone is invited to attend. 

Computer Science

Chair: Dr. Shahnewaz Karim Sakib



Intrusion detection systems (IDS) can be improved by using machine learning to teach the IDS what traffic is normal and therefore should be allowed into a network, or what traffic is abnormal and should be denied access to a network. The accuracy and speed of intrusion detections can be improved through the use of machine learning methods that provide a good fit with the data received by the IDS. There are numerous machine learning methods that can be employed for the purpose of improving intrusion detection. We use the NSL KDD data set to evaluate various machine learning models in order to determine which models are more effective for use in intrusion detection, and perform SHAP analysis to determine which features have greater effect on the models.

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