A Fuzzy-Based Context-Aware Misbehavior Detecting Scheme for Detecting Rogue Nodes in Vehicular Ad Hoc Network

Ghaleb, Fuad A.; Saeed, Faisal; Alkhammash, Eman H.; Alghamdi, Norah Saleh; Al‐rimy, Bander Ali Saleh

doi:10.3390/s22072810

Cited by 10 publications

(11 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Node-centric IDSs determine whether a vehicle is malicious based on how it behaves on the road section [19]. The trustworthiness of legitimate vehicles is also assessed based on such behavior, which can be perceived by observing the number and validity of BSM messages shared by the vehicle [20,21].…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, NGSIM consists of many patterns representing different drive situations and driver behavior [7]. In addition, NGSIM provides high-quality contextual data that describe realistic real-world scenarios on different road sections [19]. Particularly, NGSIM was built by collecting data from vehicles moving on a road section with 500 m-long and seven-lane highway.…”

Section: The Datasetmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive IDS for Cooperative Intelligent Transportation Systems Using Deep Belief Networks

2022

View full text Add to dashboard Cite

The adoption of cooperative intelligent transportation systems (cITSs) improves road safety and traffic efficiency. Vehicles connected to cITS form vehicular ad hoc networks (VANET) to exchange messages. Like other networks and systems, cITSs are targeted by attackers intent on compromising and disrupting system integrity and availability. They can repeatedly spoof false information causing bottlenecks, traffic jams and even road accidents. The existing security infrastructure assumes that the network topology and/or attack behavior is static. However, the cITS is inherently dynamic in nature. Moreover, attackers may have the ability and resources to change their behavior continuously. Assuming a static IDS security model for VANETs is not suitable and can lead to low detection accuracy and high false alarms. Therefore, this paper proposes an adaptive security solution based on deep learning and contextual references that can cope with the dynamic nature of the cITS topologies and increasingly common attack behaviors. In this study, deep belief networks (DBN) modeling was used to train the detection model. Binary cross entropy was used as a loss function to measure the prediction error. Two activation functions were used, Relu and Softmax, for input–output mapping. The Relu was used in the hidden layers, while the Sigmoid was used in the last layer to map the real vector to output between 0 and 1. The adaptation mechanism was incorporated into the detection model using a moving average that monitors predicted values within a time window. In this way, the model can readjust the classification thresholds on-the-fly as appropriate. The proposed model was evaluated using the Next Generation Simulation (NGSIM) dataset, which is commonly used in such related works. The result is improved accuracy, demonstrating that the adaptation mechanism used in this study was effective.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: The Datasetmentioning

confidence: 99%

Adaptive IDS for Cooperative Intelligent Transportation Systems Using Deep Belief Networks

2022

View full text Add to dashboard Cite

show abstract

“…The fuzzy logic based misbehavior detection scheme is an effective way to detect malicious nodes [27]. An alternative approach works on previous experience, certificate authority (CA), and opinions to construct node trust [16].…”

Section: B Entity Centric Modelsmentioning

confidence: 99%

“…As IoV is a new field, very few studies have been conducted. Some context-based trust models are presented, such as [2,9,20,27,43]. The main problem with these models is; that they have not Partially used context and are unable to provide comprehensiveness in terms of context.…”

Section: Context-awarenessmentioning

confidence: 99%

CTMF: Context-Aware Trust Management Framework for Internet of Vehicles

Rehman

Hassan

Hooi³

et al. 2022

IEEE Access

View full text Add to dashboard Cite

Secure communication is the top concern of the Internet of Vehicles (IoV). The trust between nodes can have a considerable impact on ensuring IoV security. Therefore, the trustworthiness of a received message must be evaluated before acting upon it. A malicious node can broadcast bogus events to obtain network control. False reports and malicious vehicles render the network unreliable during emergencies. In this study, a unique trust framework is presented that considers most of the aspects of trust in IoV to accurately identify malicious nodes and events. Previous studies have proposed some trust models for VANETs, which have many deficiencies in serving IoV. In particular, they lack dynamism and practical implementations. All the existing models have two things in common, first they work on fixed parameters, and second, they use static scenarios. In contrast, the proposed framework is based on a context-awareness cognitive approach with artificial intelligence (AI) properties. The framework cognitively learns the environment from the received report and creates a context around an event. In addition to trust management (TM), the proposed framework offers a novel process for detecting and screening malicious nodes using anomaly outliers. The performance of the framework was examined using an experimental simulation. The proposed framework was compared with top benchmarks in the field. The results show inclining performance indicators. The proposed trustmanagement framework has the potential to serve as a component of IoV security. INDEX TERMSInternet of Vehicles (IoV), Trust Management (TM), Vehicular Ad Hoc Network (VANET), Context awareness

show abstract

“…Accordingly, many context-aware misbehavior models were suggested to validate the data shared among vehicles in the scene. In such an approach, a context reference is constructed online based on data collected locally from neighboring vehicles [2,8,9,15,17,[27][28][29][30][31][32][33]. A message that deviates much from the context is considered a malicious message and is accordingly used to decrement the trust value of the sender for the entitycentric approach.…”

Section: Related Workmentioning

confidence: 99%

An Improved Robust Misbehavior Detection Scheme for Vehicular Ad Hoc Network

et al. 2022

View full text Add to dashboard Cite

Vehicular Ad Hoc Networks (VANETs) have emerged to improve road safety and traffic efficiency and provide passengers comfort. Most VANET applications rely on the cooperation among vehicles sharing their sensed information. However, misbehaving vehicles which send false information can disrupt the VANETs potential. Although many solutions have been proposed to defend against misbehaving vehicles in VANET, most of these solutions relays on honest majority assumptions and are thus vulnerable to collusion attacks. Colluding vehicles send fake information, and because detection depends on cooperation, such information misleads benign vehicles to make an accurate decision. This study proposes an improved Robust Misbehavior Detection Scheme (iRMDS) by replacing the statistics-based detection threshold, which assumes an honest majority, with a machine learning-based classifier. A Neuro-Kalman-Based Robust Misbehavior detection scheme is proposed in three phases. In the first phase, attackers-Independent features are extracted from signal properties such as the receive signal strength and signal direction and have been integrated with context information features. In the second phase, the Kalman filter algorithm has been designed to extract consistent patterns of context information for each vehicle. That is, the innovation errors of the Kalman filter have been utilized as the input features to train the misbehavior detection model. In the third phase, the artificial neural network algorithm is integrated with the outputs of the Kalman Filter algorithm to recognize the malicious pattern. Results show that the overall performance of the proposed iRMDS solution achieves a 3.44% improvement compared to the related work. Such enhancement is promising in realizing reliable VANET applications to improve road safety, traffic efficiency, and passenger comfort.

show abstract

A Fuzzy-Based Context-Aware Misbehavior Detecting Scheme for Detecting Rogue Nodes in Vehicular Ad Hoc Network

Cited by 10 publications

References 42 publications

Adaptive IDS for Cooperative Intelligent Transportation Systems Using Deep Belief Networks

Adaptive IDS for Cooperative Intelligent Transportation Systems Using Deep Belief Networks

CTMF: Context-Aware Trust Management Framework for Internet of Vehicles

An Improved Robust Misbehavior Detection Scheme for Vehicular Ad Hoc Network

Contact Info

Product

Resources

About