Community Detection of Anomalies in Large Scale Network using Deep Learning
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.