Detection, control and mitigation system for secure vehicular communication

Hidalgo, Carlos; Vaca, Myriam; Nowak, Mateusz; Frölich, Piotr; Reed, Martin J.; Al-Naday, Mays; Mpatziakas, Asterios; Protogerou, Aikaterini; Drosou, Anastasios; Tzovaras, Dimitrios

doi:10.1016/j.vehcom.2021.100425

Cited by 12 publications

(4 citation statements)

References 44 publications

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“…Among them, we may mention its application for the protection of IoT infrastructures on automated vehicles, which represent a class of typical high-risk systems when receiving a cyber attack. In this scenario, we used our solution to allow rerouting tests in vehicular communication [57]. The overall aim in this case was to ensure secure and reliable communication among various components of connected intelligent transportation systems (C-ITS).…”

Section: Discussionmentioning

confidence: 99%

Time Series Network Data Enabling Distributed Intelligence—A Holistic IoT Security Platform Solution

et al. 2022

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The Internet of Things (IoT) encompasses multiple fast-emerging technologies controlling and connecting millions of new devices every day in several application domains. The increased number of interconnected IoT devices, their limited computational power, and the evolving sophistication of cyber security threats, results in increased security challenges for the IoT ecosystem. The diversity of IoT devices, and the variety of QoS requirements among several domains of IoT application, impose considerable challenges in designing and implementing a robust IoT security solution. The aim of this paper is to present an efficient, robust, and easy-to-use system, for IoT cyber security operators. Following a by-design security approach, the proposed system is a platform comprising four distinct yet cooperating components; a distributed AI-enhanced detection of potential threats and anomalies mechanisms, an AI-based generation of effective mitigation strategies according to the severity of detected threats, a system for the verification of SDN routing decisions along with network- and resource-related policies, and a comprehensive and intuitive security status visualization and analysis. The distributed anomaly detection scheme implementing multiple AI-powered agents is deployed across the IoT network nodes aiming to efficiently monitor the entire network infrastructure. Network traffic data are fed to the AI agents, which process consecutive traffic samples from the network in a time series analysis manner, where consecutive time windows framing the traffic of the surrounding nodes are processed by a graph neural network algorithm. Any detected anomalies are handled by a mitigation engine employing a distributed neural network algorithm, which exploits the recorded anomalous events and deploys appropriate responses for optimal threat mitigation. The implemented platform also includes the hypothesis testing module, and a multi-objective optimization tool for the quick verification of routing decisions. The system incorporates visualization and analytics functionality and a customizable user interface.

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Section: Discussionmentioning

confidence: 99%

Time Series Network Data Enabling Distributed Intelligence—A Holistic IoT Security Platform Solution

et al. 2022

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“…Globally number of injuries and deaths are reduced by increasing protection and seclusion of automated vehicles against precarious cyber-attack. In [8] SerIoT project recognizes distrustful patterns and mitigates using framework that exchanges traffic through IoT platforms and ensures reliable and secured communication. Smart intersection point is chosen nodes are equipped with interacting on board unit (OBU) and road side unit (RSU) for optimistic traffic flow.…”

Section: Introductionmentioning

confidence: 99%

Developing a pervasive edge computing environment for Vehicular Communication using modified Reinforcement Learning in Routing and Dynamic Traffic Flow Prediction

Alqahtani,

Ramakrishnan,

Saravanan

et al. 2023

Preprint

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Vehicular networking in smart autonomous connected vehicle communications evolved with high mobility and due to high dynamics in an urban environment, new challenges are addressed by academicians and researchers for providing better support. Dynamic changes of vehicular nodes position, routing in Vehicular Adhoc Network (VANET) using existing traditional networking routing algorithms may not provide optimal solution for efficient communication. Also predicting or forecasting traffic flow in VANET can be improved through sharing traffic information in real-time using intelligent transportation systems. In this paper we proposed modified reinforcement learning algorithm that supports for optimal route identification for dynamically disconnected vehicles in urban environment by considering its previous state and predicts flow of traffic generated by vehicles in various time interval. Experimental result shows better performance in routing parameters like packet delivery ratio, routing overhead, latency and predicting traffic flow by proposed algorithms achieves significant accomplishments comparing to existing algorithms.

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“…During severe climatic conditions in sub-urban zones of Pakistan, the selection of an automobile plays a vital role for a safe journey. Hidalgo et al (2022) proposed SerIoT System: SerIoT emerges as a potential option, offering a valuable transparent and standard architecture for real-time tracking of data transmitted across heterogeneous IoT devices. This proposed framework is capable of detecting abnormal occurrences, evaluating them, and subsequently taking appropriate measures.…”

mentioning

confidence: 99%

Accident Prevention-Based Analysis Using IoT-Interfaced LabVIEW Model

Sowjanya,

Krishna,

Madhav

et al. 2023

International Journal of Healthcare Information Systems and Informatics

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In this paper, the authors used LabView to design a sensor-based module and analyse it for an accident prevention system. For the purpose of forecasting potential combinations of accident occurrence, various sensor nodes are integrated into a single system. A LabView-based simulation has been performed to address additional potential conditions after a real-time hardware module with a constrained number of sensors was used to examine various conditions. With the help of an IoT interface, this design will allow a new model in the vehicular communication system to identify different accident occurrences and provide the relevant information to the underprivileged. The proposed model will cover all potential combinations, provide comparative analysis between low- and high-end vehicles, and provide a strategic framework for IoT-enabled vehicular communication systems in the future.

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Detection, control and mitigation system for secure vehicular communication

Cited by 12 publications

References 44 publications

Time Series Network Data Enabling Distributed Intelligence—A Holistic IoT Security Platform Solution

Time Series Network Data Enabling Distributed Intelligence—A Holistic IoT Security Platform Solution

Developing a pervasive edge computing environment for Vehicular Communication using modified Reinforcement Learning in Routing and Dynamic Traffic Flow Prediction

Accident Prevention-Based Analysis Using IoT-Interfaced LabVIEW Model

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