Improving Learning and Adaptation in Security Games by Exploiting Information Asymmetry, by Xiaofan He. Huaiyu Dai and Peng Ning, in INFOCOM 2015
Open Source Project
Source: Machine Learning and Big Data in Cyber Security Eyal Kolman Technion lecture
Speaker: by yal Kolman of RSA given at Technion-Israel Institute of Technoloy, Technion Computer Engineering summer school 2014
- This video discusses about the challenging in applying machine learning to detect attacks.
- It also introduces 3 case studies of how to use machine learning in the domain of security.
- High cost of errors
- If the detection generates a lot of wrong alerts, then the detection is not useful.
- Data is not public
- Most of the security data are private
- Semantic gam
- Detection is not enough
- Evaluation difficulty
- There are few labels
- There are few attacks
- Detect inpersonation
- based on users behavior
- Detect fraud in bank account
- Detect malicious domain
- Events with cookies