DDoS Detection System – Students Research in ML and DL at Durham College

#DDoS #Detection #System #Machine #Learning

The research goal is to implement different machine learning algorithms to detect any DDoS (Distributed Denial of Service) attacks using the UNSW-NB15 dataset. We started by going through the data description and finding null values in our features. After that we dropped the ‘id’ column.

We have used the UNSW-15 dataset for AI-based DDOS detection systems.

The UNSW-15 dataset has a hybrid of the real modern normal and the contemporary synthesized attack activities of the network traffic. It contains different attacks, including DoS, worms, Backdoors etc. The raw network packets of the UNSW-NB 15 datasets are created by the IXIA Perfect Storm tool in the Cyber Range Lab of the Australian Centre for Cyber Security (ACCS) for generating a hybrid of real modern normal activities and synthetic contemporary attack behaviours. We incorporated different feature selection methods for dropping insignificant features followed by the implementation of 6 classification algorithms, namely Naive Bayes, Random Forest, Decision Tree, KNN, Logistic Regression and SVM.

  Date and Time




  • Date: 03 May 2022
  • Time: 06:00 PM to 07:00 PM
  • All times are (GMT-05:00) Canada/Eastern
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  • toronto, Ontario
  • Canada

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Minu Ahlawat


DDoS Detection System – Students Research in ML and DL at Durham College

Speakers: Minu Ahlawat, Dwij Dua, Megha Garg, Taxil Savani