In this study, we propose a new approach for detecting previously unencountered instances of known classes of malicious software based on their temporal behavior. In the proposed approach, time-stamped security data are continuously monitored within the target computer system or network and then processed by the knowledge-based temporal abstraction (KBTA) methodology. Using KBTA, continuously measured data (e. g., the number of running processes) and events (e. g., installation of a software) are integrated with a security-domain, temporal-abstraction knowledge-base (i. e., a security ontology for abstracting meaningful patterns from raw, time-oriented security data), to create higher-level, time-oriented concepts and patterns, also known as temporal abstractions. Automatically-generated temporal abstractions can be monitored to detect suspicious temporal patterns. These patterns are compatible with a set of predefined classes of malware as defined by a security expert employing a set of time and value constraints. The new approach was applied for detecting worm-related malware using two different ontologies. Evaluation results demonstrated the effectiveness of the new approach. The approach can be used for detecting other types of malware by updating the security ontology with new definitions of temporal patterns.
- Computer and network security
- Host-based intrusion detection systems
- Intelligent visualization
- Knowledge-based systems