Poster
:
Community Detection of Anomalies in Large Scale Network using Deep Learning
SessionPosters
Event Type
Poster
Time
Location
DescriptionAnomalies in network traffic is taking many different, and sometimes unrelated dimensions in these days of data-explosion phenomenon that produces “Big Data”. The line of demarcation between normal and abnormal data that are being transmitted and data that are at-rest is becoming thinner by the day because of various improvement in the levels of sophistication of cyber-security attacks.
The need for an equally sophisticated methods for cyber-security necessitated recent developments that combine both human and machine intelligence in addressing the explosive rate of cyber-attacks. The use of artificial intelligence (AI) in combating cyber-attacks cannot be over emphasized in this era.
Many ongoing and current researches are providing tools for analyzing labeled and unlabeled data; however, the challenges of finding anomalies and patterns in large scale datasets still exists because of rapid changes in the threat landscape such as the increase in “living-off-the-land” attacks. This research proposal plans to implement a novel and robust solution that combines AI and cybersecurity to solve complex network security problems. The idea proposes the use of Long Short-Term Memory (LSTM), PageRank and Feature learning models to identify, group and predict anomalies in large scale real-world networks that contain millions or billions of nodes.