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DTSTAMP:20220504T142729Z
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DESCRIPTION:The research goal is to implement different machine learning al
 gorithms to detect any DDoS (Distributed Denial of Service) attacks using 
 the UNSW-NB15 dataset. We started by going through the data description an
 d finding null values in our features. After that we dropped the ‘id’ 
 column.\n\nWe have used the UNSW-15 dataset for AI-based DDOS detection sy
 stems.\n\nThe UNSW-15 dataset has a hybrid of the real modern normal and t
 he contemporary synthesized attack activities of the network traffic. It c
 ontains different attacks\, including DoS\, worms\, Backdoors etc. The raw
  network packets of the UNSW-NB 15 datasets are created by the IXIA Perfec
 t Storm tool in the Cyber Range Lab of the Australian Centre for Cyber Sec
 urity (ACCS) for generating a hybrid of real modern normal activities and 
 synthetic contemporary attack behaviours. We incorporated different featur
 e selection methods for dropping insignificant features followed by the im
 plementation of 6 classification algorithms\, namely Naive Bayes\, Random 
 Forest\, Decision Tree\, KNN\, Logistic Regression and SVM.\n\nSpeaker(s):
   Minu Ahlawat\, \n\ntoronto\, Ontario\, Canada\, Virtual: https://events.
 vtools.ieee.org/m/312339
LOCATION:toronto\, Ontario\, Canada\, Virtual: https://events.vtools.ieee.o
 rg/m/312339
ORGANIZER:reza.dibaj@ieee.org
SEQUENCE:2
SUMMARY:DDoS Detection System – Students Research in ML and DL at Durham 
 College
URL;VALUE=URI:https://events.vtools.ieee.org/m/312339
X-ALT-DESC:Description: <br /><p>The research goal is to implement differen
 t machine learning algorithms to detect any DDoS (Distributed Denial of Se
 rvice) attacks using the UNSW-NB15 dataset. We started by going through th
 e data description and finding null values in our features. After that we 
 dropped the &lsquo\;id&rsquo\; column.</p>\n<p>We have used the UNSW-15 da
 taset for AI-based DDOS detection systems.</p>\n<p>The UNSW-15 dataset has
  a hybrid of the real modern normal and the contemporary synthesized attac
 k activities of the network traffic. It contains different attacks\, inclu
 ding DoS\, worms\, Backdoors etc. The raw network packets of the UNSW-NB 1
 5 datasets are created by the IXIA Perfect Storm tool in the Cyber Range L
 ab of the Australian Centre for Cyber Security (ACCS) for generating a hyb
 rid of real modern normal activities and synthetic contemporary attack beh
 aviours. 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\, Lo
 gistic Regression and SVM.</p>
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