Temporal-based intrusion detection for IoV
| dc.contributor.author | Hamad, Mohammad | |
| dc.contributor.author | Hammadeh, Zain A. H. | |
| dc.contributor.author | Saidi, Selma | |
| dc.contributor.author | Prevelakis, Vassilis | |
| dc.date.accessioned | 2021-06-21T09:47:16Z | |
| dc.date.available | 2021-06-21T09:47:16Z | |
| dc.date.issued | 2020 | |
| dc.description.abstract | The Internet of Vehicle (IoV) is an extension of Vehicle-to-Vehicle (V2V) communication that can improve vehicles’ fully autonomous driving capabilities. However, these communications are vulnerable to many attacks. Therefore, it is critical to provide run-time mechanisms to detect malware and stop the attackers before they manage to gain a foothold in the system. Anomaly-based detection techniques are convenient and capable of detecting off-nominal behavior by the component caused by zero-day attacks. One significant critical aspect when using anomaly-based techniques is ensuring the correct definition of the observed component’s normal behavior. In this paper, we propose using the task’s temporal specification as a baseline to define its normal behavior and identify temporal thresholds that give the system the ability to predict malicious tasks. By applying our solution on one use-case, we got temporal thresholds 20–40 % less than the one usually used to alarm the system about security violations. Using our boundaries ensures the early detection of off-nominal temporal behavior and provides the system with a sufficient amount of time to initiate recovery actions. | en |
| dc.identifier.doi | 10.1515/itit-2020-0009 | |
| dc.identifier.pissn | 2196-7032 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/36578 | |
| dc.language.iso | en | |
| dc.publisher | De Gruyter | |
| dc.relation.ispartof | it - Information Technology: Vol. 62, No. 5-6 | |
| dc.subject | Real-time systems | |
| dc.subject | Security | |
| dc.subject | Safety | |
| dc.subject | Intrusion Detection | |
| dc.title | Temporal-based intrusion detection for IoV | en |
| dc.type | Text/Journal Article | |
| gi.citation.endPage | 239 | |
| gi.citation.publisherPlace | Berlin | |
| gi.citation.startPage | 227 |