An intrusion detection system against malicious attacks on the communication network of driverless cars

Alheeti, Khattab M. Ali; Gruebler, Anna; McDonald-Maier, Klaus

doi:10.1109/ccnc.2015.7158098

Cited by 85 publications

(46 citation statements)

References 13 publications

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“…Fuzzy sets are increasingly popular to tackle such problems efficiently [38] and will thus be employed to address the problem of classification using a fuzzification of the features that were obtained from the ns-2 trace file. Our previous work [39] did not employ fuzzy sets and we obtained a false alarm rate of 12.24%. After incorporating fuzzy sets, we obtain a false alarm rate of 0.17% [25].…”

Section: Fuzzification Of the Datasetmentioning

confidence: 73%

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Intelligent Intrusion Detection of Grey Hole and Rushing Attacks in Self-Driving Vehicular Networks

2016

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Vehicular ad hoc networks (VANETs) play a vital role in the success of self-driving and semi self-driving vehicles, where they improve safety and comfort. Such vehicles depend heavily on external communication with the surrounding environment via data control and Cooperative Awareness Messages (CAMs) exchanges. VANETs are potentially exposed to a number of attacks, such as grey hole, black hole, wormhole and rushing attacks. This work presents an intelligent Intrusion Detection System (IDS) that relies on anomaly detection to protect the external communication system from grey hole and rushing attacks. These attacks aim to disrupt the transmission between vehicles and roadside units. The IDS uses features obtained from a trace file generated in a network simulator and consists of a feed-forward neural network and a support vector machine. Additionally, the paper studies the use of a novel systematic response, employed to protect the vehicle when it encounters malicious behaviour. Our simulations of the proposed detection system show that the proposed schemes possess outstanding detection rates with a reduction in false alarms. This safe mode response system has been evaluated using four performance metrics, namely, received packets, packet delivery ratio, dropped packets and the average end to end delay, under both normal and abnormal conditions.

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Section: Fuzzification Of the Datasetmentioning

confidence: 73%

“…In order to evaluate the performance of the IDS, we need to compare it with the previous best achieved average error rate, which is 2.05% [39], while we have achieved an 0.18% error rate with the IDS presented here. In [39], the average of false alarm is 4.68%, while we have achieved 0.15% with this IDS.…”

Section: Discussionmentioning

confidence: 99%

Intelligent Intrusion Detection of Grey Hole and Rushing Attacks in Self-Driving Vehicular Networks

2016

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“…For instance, incapable (damaged, etc.) or hacked vehicles could potentially send false information mistakenly or maliciously to one another (Alheeti, Gruebler, & McDonald‐Maier, ). At a larger scale, cyber terrorism such as a large‐scale immobilization of vehicles (Yeomans, ) may lead to relatively unquantifiable and uninsurable risks.…”

Section: Emerging Technology Risksmentioning

confidence: 99%

Semiautonomous Vehicle Risk Analysis: A Telematics‐Based Anomaly Detection Approach

2018

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The transition to semiautonomous driving is set to considerably reduce road accident rates as human error is progressively removed from the driving task. Concurrently, autonomous capabilities will transform the transportation risk landscape and significantly disrupt the insurance industry. Semiautonomous vehicle (SAV) risks will begin to alternate between human error and technological susceptibilities. The evolving risk landscape will force a departure from traditional risk assessment approaches that rely on historical data to quantify insurable risks. This article investigates the risk structure of SAVs and employs a telematics-based anomaly detection model to assess split risk profiles. An unsupervised multivariate Gaussian (MVG) based anomaly detection method is used to identify abnormal driving patterns based on accelerometer and GPS sensors of manually driven vehicles. Parameters are inferred for vehicles equipped with semiautonomous capabilities and the resulting split risk profile is determined. The MVG approach allows for the quantification of vehicle risks by the relative frequency and severity of observed anomalies and a location-based risk analysis is performed for a more comprehensive assessment. This approach contributes to the challenge of quantifying SAV risks and the methods employed here can be applied to evolving data sources pertinent to SAVs. Utilizing the vast amounts of sensor-generated data will enable insurers to proactively reassess the collective performances of both the artificial driving agent and human driver.

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“…However different threshold functions are utilized to define the reliability or unreliability of the neighborhood nodes. Alheeti et al (2015) worked on detection system for malicious nodes in VANET. The work developed a real-time revelation and isolation of the malignant vehicles.…”

Section: Previous Studiesmentioning

confidence: 99%

Performance Evaluation of Blackhole Attack on AODV in VANET

Saad

2018

American Journal of Applied Sciences

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Black hole is one of the ongoing network threats that impact the physical components of Vehicular Ad hoc Networks (VANETs) when more layers deal with routing mechanism of vehicular ad hoc network. An algorithm for Ad hoc On-Demand Distance Vector (AODV) protocol in VANET was developed in order to identify collaborative group of nodes that behave as black hole. The simulation was configured to perform three scenarios with number of nodes set to 10, 20 and 40 nodes respectively. The result showed that the proposed algorithm provides a better throughput performance, less end to end delay, high packet delivery ration, less packet drops and less time for processing the incoming and outgoing nodes. Future works may consider testing the proposed solution on other types of attacks such as Wormhole, Jellyfish and Sybil attacks.

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An intrusion detection system against malicious attacks on the communication network of driverless cars

Cited by 85 publications

References 13 publications

Intelligent Intrusion Detection of Grey Hole and Rushing Attacks in Self-Driving Vehicular Networks

Intelligent Intrusion Detection of Grey Hole and Rushing Attacks in Self-Driving Vehicular Networks

Semiautonomous Vehicle Risk Analysis: A Telematics‐Based Anomaly Detection Approach

Performance Evaluation of Blackhole Attack on AODV in VANET

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