A misbehavior detection system to detect novel position falsification attacks in the Internet of Vehicles
In the Internet of Vehicle (IoV) networks, vehicles exchange periodic Basic Safety Messages (BSMs) containing information regarding speed and position. Safety-critical applications like blind-spot warning and lane change warning systems use the BSMs to ensure the safety of road users. To create chao...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/167109 |
| _version_ | 1826116126255349760 |
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| author | Ilango, Harun Surej Ma, Maode Su, Rong |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Ilango, Harun Surej Ma, Maode Su, Rong |
| author_sort | Ilango, Harun Surej |
| collection | NTU |
| description | In the Internet of Vehicle (IoV) networks, vehicles exchange periodic Basic Safety Messages (BSMs) containing information regarding speed and position. Safety-critical applications like blind-spot warning and lane change warning systems use the BSMs to ensure the safety of road users. To create chaos in the network, an insider attacker may inject false information into the BSM and broadcast it to nearby vehicles. One such attack is the position falsification attack, where the attacker inserts false information regarding the position in the BSMs. The literature has explored the use of Misbehavior Detection Systems (MDSs) to detect such attacks. But the limitaitons of the existing approaches are that they either perform exceptionally well in specific environmental settings or have compromised detection accuracy favoring a generalized model. Moreover, all the current machine learning-based detection models are signature-based, which requires prior knowledge about the attacks for effective detection. Motivated by the research gap, we propose a Novel Position Falsification Attack Detection System for the Internet of Vehicles (NPFADS for the IoV) to learn and detect novel position falsification attacks emerging in IoV networks. The performance of NPFADS is analyzed using the metrics precision, recall, F1 score, ROC, and PR curves. The Vehicular Reference Misbehavior (VeReMi) dataset is used as the benchmark for the study. The system's performance is compared to existing MDSs in the literature. The analysis shows that our proposed system outperforms the existing supervised learning models even when initialized with zero knowledge about the novel position falsification attacks. |
| first_indexed | 2024-10-01T04:06:16Z |
| format | Journal Article |
| id | ntu-10356/167109 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:06:16Z |
| publishDate | 2023 |
| record_format | dspace |
| spelling | ntu-10356/1671092023-05-12T15:41:13Z A misbehavior detection system to detect novel position falsification attacks in the Internet of Vehicles Ilango, Harun Surej Ma, Maode Su, Rong School of Electrical and Electronic Engineering Engineering Engineering::Electrical and electronic engineering Engineering::Electrical and electronic engineering::Wireless communication systems Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Internet of Vehicles Position Falsification Attacks Machine Learning Novel Attack Detection Basic Safety Messages (BSMs) VeReMi Dataset Vehicle-to-Vehicle (V2V) Communication Anomaly Detection In the Internet of Vehicle (IoV) networks, vehicles exchange periodic Basic Safety Messages (BSMs) containing information regarding speed and position. Safety-critical applications like blind-spot warning and lane change warning systems use the BSMs to ensure the safety of road users. To create chaos in the network, an insider attacker may inject false information into the BSM and broadcast it to nearby vehicles. One such attack is the position falsification attack, where the attacker inserts false information regarding the position in the BSMs. The literature has explored the use of Misbehavior Detection Systems (MDSs) to detect such attacks. But the limitaitons of the existing approaches are that they either perform exceptionally well in specific environmental settings or have compromised detection accuracy favoring a generalized model. Moreover, all the current machine learning-based detection models are signature-based, which requires prior knowledge about the attacks for effective detection. Motivated by the research gap, we propose a Novel Position Falsification Attack Detection System for the Internet of Vehicles (NPFADS for the IoV) to learn and detect novel position falsification attacks emerging in IoV networks. The performance of NPFADS is analyzed using the metrics precision, recall, F1 score, ROC, and PR curves. The Vehicular Reference Misbehavior (VeReMi) dataset is used as the benchmark for the study. The system's performance is compared to existing MDSs in the literature. The analysis shows that our proposed system outperforms the existing supervised learning models even when initialized with zero knowledge about the novel position falsification attacks. Agency for Science, Technology and Research (A*STAR) Submitted/Accepted version This research is supported by A*STAR, Singapore under its RIE2020 Advanced Manufacturing and Engineering (AME) Industry Alignment Fund – Pre Positioning (IAF-PP) (Award A19D6a0053). 2023-05-11T05:52:29Z 2023-05-11T05:52:29Z 2022 Journal Article Ilango, H. S., Ma, M. & Su, R. (2022). A misbehavior detection system to detect novel position falsification attacks in the Internet of Vehicles. Engineering Applications of Artificial Intelligence, 116, 105380-. https://dx.doi.org/10.1016/j.engappai.2022.105380 1873-6769 https://hdl.handle.net/10356/167109 10.1016/j.engappai.2022.105380 2-s2.0-85137724718 116 105380 en A19D6a0053 Engineering Applications of Artificial Intelligence © 2022 Elsevier Ltd. All rights reserved. This paper was published in Engineering Applications of Artificial Intelligence and is made available with permission of Elsevier Ltd. application/pdf |
| spellingShingle | Engineering Engineering::Electrical and electronic engineering Engineering::Electrical and electronic engineering::Wireless communication systems Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Internet of Vehicles Position Falsification Attacks Machine Learning Novel Attack Detection Basic Safety Messages (BSMs) VeReMi Dataset Vehicle-to-Vehicle (V2V) Communication Anomaly Detection Ilango, Harun Surej Ma, Maode Su, Rong A misbehavior detection system to detect novel position falsification attacks in the Internet of Vehicles |
| title | A misbehavior detection system to detect novel position falsification attacks in the Internet of Vehicles |
| title_full | A misbehavior detection system to detect novel position falsification attacks in the Internet of Vehicles |
| title_fullStr | A misbehavior detection system to detect novel position falsification attacks in the Internet of Vehicles |
| title_full_unstemmed | A misbehavior detection system to detect novel position falsification attacks in the Internet of Vehicles |
| title_short | A misbehavior detection system to detect novel position falsification attacks in the Internet of Vehicles |
| title_sort | misbehavior detection system to detect novel position falsification attacks in the internet of vehicles |
| topic | Engineering Engineering::Electrical and electronic engineering Engineering::Electrical and electronic engineering::Wireless communication systems Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Internet of Vehicles Position Falsification Attacks Machine Learning Novel Attack Detection Basic Safety Messages (BSMs) VeReMi Dataset Vehicle-to-Vehicle (V2V) Communication Anomaly Detection |
| url | https://hdl.handle.net/10356/167109 |
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