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. 2018 Jan 9;13(1):e0188760.
doi: 10.1371/journal.pone.0188760. eCollection 2018.

A Hierarchical Detection Method in External Communication for Self-Driving Vehicles Based on TDMA

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A Hierarchical Detection Method in External Communication for Self-Driving Vehicles Based on TDMA

Khattab M Ali Alheeti et al. PLoS One. .
Free PMC article


Security is considered a major challenge for self-driving and semi self-driving vehicles. These vehicles depend heavily on communications to predict and sense their external environment used in their motion. They use a type of ad hoc network termed Vehicular ad hoc networks (VANETs). Unfortunately, VANETs are potentially exposed to many attacks on network and application level. This paper, proposes a new intrusion detection system to protect the communication system of self-driving cars; utilising a combination of hierarchical models based on clusters and log parameters. This security system is designed to detect Sybil and Wormhole attacks in highway usage scenarios. It is based on clusters, utilising Time Division Multiple Access (TDMA) to overcome some of the obstacles of VANETs such as high density, high mobility and bandwidth limitations in exchanging messages. This makes the security system more efficient, accurate and capable of real time detection and quick in identification of malicious behaviour in VANETs. In this scheme, each vehicle log calculates and stores different parameter values after receiving the cooperative awareness messages from nearby vehicles. The vehicles exchange their log data and determine the difference between the parameters, which is utilised to detect Sybil attacks and Wormhole attacks. In order to realize efficient and effective intrusion detection system, we use the well-known network simulator (ns-2) to verify the performance of the security system. Simulation results indicate that the security system can achieve high detection rates and effectively detect anomalies with low rate of false alarms.

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.


Fig 1
Fig 1. A typical Sybil and Wormhole attacks in VANETs.
Fig 2
Fig 2. Taxonomy clustering scheme.
Fig 3
Fig 3. An example of the clustering in VANETs.
Fig 4
Fig 4. TDMA structure.
Fig 5
Fig 5. Authentication scenario.
Fig 6
Fig 6. IDS architecture.
Fig 7
Fig 7. Type parameters of detection.
Fig 8
Fig 8. Performance metrics.
Fig 9
Fig 9. Case 1 and Case 2 of self-driving vehicles.

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Grant support

This research has been supported by the UK Engineering and Physical Sciences Research Council (EPSRC) Grant EP/K004638/1 (project named RoBoSAS), EP/R02572X/1 and EP/P017487/1.

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