BROWSE
Detail
Conference Paper
Behavior-Based Anomaly Detection on Big Data
Cited - time in 
Share 
Share
- Authors
- Hyunjoo Kim, Jonghyun Kim, Ikkyun Kim, Tai-myung Chung
- Issue Date
- 2015-11
- Citation
- SRI Security Congress 2015, pp.73-80
- Language
- English
- Type
- Conference Paper
- Abstract
- Recently, cyber-targeted attacks such as APT (Advanced Persistent Threat) are rapidly growing as a social and national threat. It is an intelligent cyber-attack that infiltrates the target organization and enterprise clandestinely using various methods and causes considerable damage by making a final attack after long-term and through preparations. These attacks are threatening cyber worlds such as Internet by infecting and attacking the devices on this environment with the malicious code, and by destroying them or gaining their authorities. Detecting these attacks requires collecting and analysing data from various sources (network, host, security equipment, and devices) over the long haul. Therefore, we propose the method that can recognize the cybertargeted attack and detect the abnormal behavior based on Big Data. The proposed approach analyses faster and precisely various logs and monitoring data using Big Data storage and processing technology. In particular, we evaluated that the suspicious behavior analysis using MapReduce is effective in analysing large-scale behavior monitoring and log data from various sources.
- KSP Keywords
- Abnormal behavior, Behavior analysis, Behavior monitoring, Big data storage, Cyber attacks, Log data, Malicious code, Monitoring data, Organization and enterprise, Persistent Threat(PT), Processing Technology