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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Valdes, A. Cheung, S. |
| Copyright Year | 2009 |
| Description | Author affiliation: SRI International, 333 Ravenswood Avenue, Menlo Park, CA 94025, USA (Valdes, A.; Cheung, S.) |
| Abstract | Digital control systems are increasingly being deployed in critical infrastructure such as electric power generation and distribution. To protect these process control systems, we present a learning-based approach for detecting anomalous network traffic patterns. These anomalous patterns may correspond to attack activities such as malware propagation or denial of service. Misuse detection, the mainstream intrusion detection approach used today, typically uses attack signatures to detect known, specific attacks, but may not be effective against new or variations of known attacks. Our approach, which does not rely on attack-specific knowledge, may provide a complementary detection capability for protecting digital control systems. |
| Starting Page | 22 |
| Ending Page | 29 |
| File Size | 3838986 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781424441785 |
| DOI | 10.1109/THS.2009.5168010 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-05-11 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Learning Programmable control Process control Intrusion detection Training data Distributed control Master-slave Communication system traffic control Computer crime Pattern matching |
| Content Type | Text |
| Resource Type | Article |
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