dc.citation.conferencePlace |
US |
- |
dc.citation.conferencePlace |
Pittsburgh |
- |
dc.citation.endPage |
196 |
- |
dc.citation.startPage |
191 |
- |
dc.citation.title |
2nd IEEE/ACM International Conference on Internet-of-Things Design and Implementation, IoTDI 2017 |
- |
dc.contributor.author |
Yoon, Man-Ki |
- |
dc.contributor.author |
Mohan, Sibin |
- |
dc.contributor.author |
Choi, Jaesik |
- |
dc.contributor.author |
Christodorescu, Mihai |
- |
dc.contributor.author |
Sha, Lui |
- |
dc.date.accessioned |
2023-12-19T19:09:14Z |
- |
dc.date.available |
2023-12-19T19:09:14Z |
- |
dc.date.created |
2017-04-28 |
- |
dc.date.issued |
2017-04-20 |
- |
dc.description.abstract |
Existing techniques used for anomaly detection do not fully utilize the intrinsic properties of embedded devices. In this paper, we propose a lightweight method for detecting anomalous executions using a distribution of system call frequencies. We use a cluster analysis to learn the legitimate execution contexts of embedded applications and then monitor them at run-time to capture abnormal executions. Our prototype applied to a real-world open-source embedded application shows that the proposed method can effectively detect anomalous executions without relying on sophisticated analyses or affecting the critical execution paths. |
- |
dc.identifier.bibliographicCitation |
2nd IEEE/ACM International Conference on Internet-of-Things Design and Implementation, IoTDI 2017, pp.191 - 196 |
- |
dc.identifier.doi |
10.1145/3054977.3054999 |
- |
dc.identifier.scopusid |
2-s2.0-85019028806 |
- |
dc.identifier.uri |
https://scholarworks.unist.ac.kr/handle/201301/35338 |
- |
dc.identifier.url |
http://dl.acm.org/citation.cfm?id=3054999 |
- |
dc.language |
영어 |
- |
dc.publisher |
2nd IEEE/ACM International Conference on Internet-of-Things Design and Implementation, IoTDI 2017 |
- |
dc.title |
Learning Execution Contexts from System Call Distribution for Anomaly Detection in Smart Embedded System |
- |
dc.type |
Conference Paper |
- |
dc.date.conferenceDate |
2017-04-18 |
- |