An Attacks Detection Mechanism for Intelligent Transport System

Valentini, Edivaldo Pastori; Meneguette, Rodolfo I.; Alsuhaim, Adil

doi:10.1109/bigdata50022.2020.9378309

Cited by 7 publications

(2 citation statements)

References 18 publications

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“…Due to the highly dynamic and rapid nature of the vehicular network, it has multiple vulnerabilities and is prone to many security attacks, e.g., DoS/DDoS attacks. A security mechanism was proposed by Valentini et al [28], which prioritizes the wellbeing of individuals in vehicular networks by developing a statistical model which searches for extreme values that can be caused by the DoS/DDoS attacks. The proposed mechanism consumes a considerable amount of MAC frames, ARP requests, and features of DoS/DDoS attacks that are initiated by attacker vehicles with malicious intent.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-BSM: An Anomaly Detection and Position Falsification Attack Mitigation Approach in Connected Vehicles

2022

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With the dawn of the emerging technologies in the field of vehicular environment, connected vehicles are advancing at a rapid speed. The advancement of such technologies helps people daily, whether it is to reach from one place to another, avoid traffic, or prevent any hazardous incident from occurring. Safety is one of the main concerns regarding the vehicular environment when it comes to developing applications for connected vehicles. Connected vehicles depend on messages known as basic safety messages (BSMs) that are repeatedly broadcast in their communication range in order to obtain information regarding their surroundings. Different kinds of attacks can be initiated by a vehicle in the network with malicious intent by inserting false information in these messages, e.g., speed, direction, and position. This paper focuses on the position falsification attacks that can be carried out in the vehicular environment and be avoided using the multi-BSM approach. Multi-BSM uses consecutive multiple BSMs with different parameters to detect and warn other vehicles about position falsification attacks. Multi-BSM is compared to other anomaly detection algorithms and evaluated with rigorous simulations. Multi-BSM shows a high level of anomaly detection, even in high vehicle density, with up to 97% accuracy rate compared to the respective algorithms.

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Section: Literature Reviewmentioning

confidence: 99%

Multi-BSM: An Anomaly Detection and Position Falsification Attack Mitigation Approach in Connected Vehicles

2022

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“…Some attacks that we can cite in this paper are the reconnaissance attack and the Denial of Service (DoS) attack [5] . The reconnaissance attack is usually used to learn about the target, so the cybercriminal studies its behavior such as vulnerabilities the target may have [6,7] . On the other hand, the DoS makes services unavailable by overloading the target network, as presented by Murini [8] .…”

Section: Introductionmentioning

confidence: 99%

TENNER: intrusion detection models for industrial networks based on ensemble learning

Dalarmelina,

Arora,

Filho

et al. 2024

Journal of Surveillance, Security and Safety

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In the pursuit of discerning patterns within computer network attacks, the utilization of Machine Learning and Deep Learning algorithms has been prevalent for crafting detection models based on extensive network traffic datasets. Furthermore, enhancing detection efficacy is feasible by applying cluster learning techniques, wherein multiple Machine Learning models collaborate to yield detection outcomes. Nevertheless, it is imperative to discern the optimal features within the dataset for training the intrusion detection model. In the present study, we proffer a novel framework for feature selection and intrusion detection within industrial networks, employing Ensemble Learning to achieve commendable performance in terms of both high predictive accuracy and efficient learning duration. The outcomes evince that the proposed model exhibits an accuracy of 99.93%, with a mere one h and 34 min required for comprehensive training. In contrast, a model trained without the framework presented in this paper attains an accuracy of 99.94%, necessitating an extensive training period of 156 h. Notably, the detection model derived from the proposed solution demonstrates superior results in prediction time, accomplishing predictions within 0.0009 seconds, compared to the alternative model which requires 0.0076 seconds for predictions.

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MADCS: A Middleware for Anomaly Detection and Content Sharing for Blockchain-Based Systems

et al. 2023

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An Attacks Detection Mechanism for Intelligent Transport System

Cited by 7 publications

References 18 publications

Multi-BSM: An Anomaly Detection and Position Falsification Attack Mitigation Approach in Connected Vehicles

Multi-BSM: An Anomaly Detection and Position Falsification Attack Mitigation Approach in Connected Vehicles

TENNER: intrusion detection models for industrial networks based on ensemble learning

MADCS: A Middleware for Anomaly Detection and Content Sharing for Blockchain-Based Systems

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