Collaborative Learning Based Sybil Attack Detection in Vehicular AD-HOC Networks (VANETS)
Vehicular Ad-hoc network (VANET) is an imminent technology having both exciting prospects and substantial challenges, especially in terms of security. Due to its distributed network and frequently changing topology, it is extremely prone to security attacks. The researchers have proposed different s...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-09-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/18/6934 |
| _version_ | 1797482532452696064 |
|---|---|
| author | Sofia Azam Maryum Bibi Rabia Riaz Sanam Shahla Rizvi Se Jin Kwon |
| author_facet | Sofia Azam Maryum Bibi Rabia Riaz Sanam Shahla Rizvi Se Jin Kwon |
| author_sort | Sofia Azam |
| collection | DOAJ |
| description | Vehicular Ad-hoc network (VANET) is an imminent technology having both exciting prospects and substantial challenges, especially in terms of security. Due to its distributed network and frequently changing topology, it is extremely prone to security attacks. The researchers have proposed different strategies for detecting various forms of network attacks. However, VANET is still exposed to several attacks, specifically Sybil attack. Sybil Attack is one of the most challenging attacks in VANETS, which forge false identities in the network to undermine communication between network nodes. This attack highly impacts transportation safety services and may create traffic congestion. In this regard, a novel collaborative framework based on majority voting is proposed to detect the Sybil attack in the network. The framework works by ensembling individual classifiers, i.e., K-Nearest Neighbor, Naïve Bayes, Decision Tree, SVM, and Logistic Regression in a parallel manner. The Majority Voting (Hard and Soft) mechanism is adopted for a final prediction. A comparison is made between Majority Voting Hard and soft to choose the best approach. With the proposed approach, 95% accuracy is achieved. The proposed framework is also evaluated using the Receiver operating characteristics curve (ROC-curve). |
| first_indexed | 2024-03-09T22:33:43Z |
| format | Article |
| id | doaj.art-4f6e7c8b25694746a88876caf6001c95 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T22:33:43Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-4f6e7c8b25694746a88876caf6001c952023-11-23T18:51:42ZengMDPI AGSensors1424-82202022-09-012218693410.3390/s22186934Collaborative Learning Based Sybil Attack Detection in Vehicular AD-HOC Networks (VANETS)Sofia Azam0Maryum Bibi1Rabia Riaz2Sanam Shahla Rizvi3Se Jin Kwon4Department of Computer Science and IT, University of Azad Jammu and Kashmir, Muzaffarabad 13100, CO, PakistanDepartment of Computer Science and IT, University of Azad Jammu and Kashmir, Muzaffarabad 13100, CO, PakistanDepartment of Computer Science and IT, University of Azad Jammu and Kashmir, Muzaffarabad 13100, CO, PakistanRaptor Interactive (Pty) Ltd., Eco Boulevard, Witch Hazel Ave, Centurion 0157, South AfricaDepartment of AI Software, Kangwon National University, Samcheok 25913, KoreaVehicular Ad-hoc network (VANET) is an imminent technology having both exciting prospects and substantial challenges, especially in terms of security. Due to its distributed network and frequently changing topology, it is extremely prone to security attacks. The researchers have proposed different strategies for detecting various forms of network attacks. However, VANET is still exposed to several attacks, specifically Sybil attack. Sybil Attack is one of the most challenging attacks in VANETS, which forge false identities in the network to undermine communication between network nodes. This attack highly impacts transportation safety services and may create traffic congestion. In this regard, a novel collaborative framework based on majority voting is proposed to detect the Sybil attack in the network. The framework works by ensembling individual classifiers, i.e., K-Nearest Neighbor, Naïve Bayes, Decision Tree, SVM, and Logistic Regression in a parallel manner. The Majority Voting (Hard and Soft) mechanism is adopted for a final prediction. A comparison is made between Majority Voting Hard and soft to choose the best approach. With the proposed approach, 95% accuracy is achieved. The proposed framework is also evaluated using the Receiver operating characteristics curve (ROC-curve).https://www.mdpi.com/1424-8220/22/18/6934VANETsybil attackvehicular ad hoc networkmachine learning |
| spellingShingle | Sofia Azam Maryum Bibi Rabia Riaz Sanam Shahla Rizvi Se Jin Kwon Collaborative Learning Based Sybil Attack Detection in Vehicular AD-HOC Networks (VANETS) Sensors VANET sybil attack vehicular ad hoc network machine learning |
| title | Collaborative Learning Based Sybil Attack Detection in Vehicular AD-HOC Networks (VANETS) |
| title_full | Collaborative Learning Based Sybil Attack Detection in Vehicular AD-HOC Networks (VANETS) |
| title_fullStr | Collaborative Learning Based Sybil Attack Detection in Vehicular AD-HOC Networks (VANETS) |
| title_full_unstemmed | Collaborative Learning Based Sybil Attack Detection in Vehicular AD-HOC Networks (VANETS) |
| title_short | Collaborative Learning Based Sybil Attack Detection in Vehicular AD-HOC Networks (VANETS) |
| title_sort | collaborative learning based sybil attack detection in vehicular ad hoc networks vanets |
| topic | VANET sybil attack vehicular ad hoc network machine learning |
| url | https://www.mdpi.com/1424-8220/22/18/6934 |
| work_keys_str_mv | AT sofiaazam collaborativelearningbasedsybilattackdetectioninvehicularadhocnetworksvanets AT maryumbibi collaborativelearningbasedsybilattackdetectioninvehicularadhocnetworksvanets AT rabiariaz collaborativelearningbasedsybilattackdetectioninvehicularadhocnetworksvanets AT sanamshahlarizvi collaborativelearningbasedsybilattackdetectioninvehicularadhocnetworksvanets AT sejinkwon collaborativelearningbasedsybilattackdetectioninvehicularadhocnetworksvanets |