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DTSTART;TZID=America/New_York:20220503T180000
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DTSTAMP:20260415T225608
CREATED:20220502T171646Z
LAST-MODIFIED:20220504T073258Z
UID:10000346-1651600800-1651604400@www.ieeetoronto.ca
SUMMARY:DDoS Detection System – Students Research in ML and DL at Durham College
DESCRIPTION: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. \nWe have used the UNSW-15 dataset for AI-based DDOS detection systems. \nThe 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. \nSpeaker(s): Minu Ahlawat\, Dwij Dua\, Megha Garg\, Taxil Savani \nRegister: https://events.vtools.ieee.org/m/312339
URL:https://www.ieeetoronto.ca/event/ddos-detection-system-students-research-in-ml-and-dl-at-durham-college/
LOCATION:toronto\, Ontario\, Canada\, Virtual: https://events.vtools.ieee.org/m/312339
CATEGORIES:Magnetics,Women in Engineering
ORGANIZER;CN="Reza Dibaj":MAILTO:reza.dibaj@ieee.org
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