Paper Info
Title
AI^2: Training a Big Data Machine to Defend
Abstract
We present AI <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , an analyst-in-the-loop security system where Analyst Intuition (AI) is put together with state-of-the-art machine learning to build a complete end-to-end Artificially Intelligent solution (AI). The system presents four key features: a big data behavioral analytics platform, an outlier detection system, a mechanism to obtain feedback from security analysts, and a supervised learning module. We validate our system with a real-world data set consisting of 3.6 billion log lines and 70.2 million entities. The results show that the system is capable of learning to defend against unseen attacks. With respect to unsupervised outlier analysis, our system improves the detection rate in 2.92× and reduces false positives by more than 5×.
Year
DOI
Venue
2016
10.1109/BigDataSecurity-HPSC-IDS.2016.79
2016 IEEE 2nd International Conference on Big Data Security on Cloud (BigDataSecurity), IEEE International Conference on High Performance and Smart Computing (HPSC), and IEEE International Conference on Intelligent Data and Security (IDS)
Keywords
DocType
Citations 
machine learning,human-in-the-loop,anomaly detection,active learning,security,InfoSec,behavioral analytics,big data
Conference
15
PageRank 
References 
Authors
0.93
0
5
Name
Order
Citations
PageRank
Kalyan Veeramachaneni171661.50
Ignacio Arnaldo2817.69
Vamsi Korrapati3150.93
Constantinos Bassias4150.93
Ke Li5150.93