preview

Prescriptive Analytics For Cyber Security

Good Essays

Prescriptive Analytics for Cyber Security Anomaly Detection Algorithm Status and Future Steps Xinle (Liam) Wang E295, MEng in IEOR University of California, Berkeley Introduction: Our capstone project team is working on Prescriptive Analytics for Cyber Security. The project mainly consists of two parts – building a predictive anomaly detection algorithm that detects suspicious cyber anomalies based on multiple cyber datasets, and implementing a prescriptive model which optimizes the output from anomaly detection and recommend the best course of action. We have been closely working with Mr. Eric Chasin from Innvo Solutions LLC, and Prof. Anil Aswani from IEOR department, to together achieve our goal of creating an integrated system or model that would automatically detect and prescribe actions for cyber anomalies. First, Chris led our group in learning to integrate different cyber data sources into ElasticSearch, which is a big data analytics platform, using tools such as Amazon Web Services and Logstash. After getting familiar with the data, Siddarth, Aldre and I together summarized time-window based features from the datasets that are helpful in anomaly detection, and Kenneth led the group in preprocessing the data with Python to extract the features we have discussed. At the current stage, Aldre and I are working simultaneously on anomaly detection algorithms for the suspicious network flow patterns. Specifically, Aldre has been working on Transductive

Get Access