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DTSTART:19700308T020000
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DTSTAMP:20211207T055403Z
LOCATION:Second Floor Atrium
DTSTART;TZID=America/Chicago:20211116T083000
DTEND;TZID=America/Chicago:20211116T170000
UID:submissions.supercomputing.org_SC21_sess278_rpost110@linklings.com
SUMMARY:Detecting Network Intrusion Anomalies through Egonet-Based Data Mi
 ning with Apache Spark
DESCRIPTION:Posters, Research Posters\n\nDetecting Network Intrusion Anoma
 lies through Egonet-Based Data Mining with Apache Spark\n\nPaik, Kwak, Lu\
 n\nNetwork intrusions often contain dangerous breaches to network security
  systems and their data. We design an anomaly detection system to identify
  network intrusions. Our proposed detection method is inspired by the use 
 of egonets in the oddball algorithm but differs by the extracted features 
 and the anomaly classification procedure. The detection process follows th
 e generalized design: create a k-nearest-neighbors graph from a network da
 taset; extract each node’s egonet’s edge weights, number of edges, and tot
 al eigenvector centrality sum; compare each node’s egonet’s features throu
 gh pairwise comparisons; and define a median “truth” line from the compari
 son and label nodes as anomalous based on their distance from the line. We
  have achieved an anomaly detection accuracy score of up to 92.9% with the
  eigenvector centrality score vs. edge weight feature comparison. We paral
 lelize our algorithm by implementing Resilient Distributed Datasets in Apa
 che Spark.\n\nRegistration Category: Tech Program Reg Pass, Exhibit Hall O
 nly
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