Detection of DDoS attacks in SDN-based VANET using optimized TabNet

Computer Standards & Interfaces, Год журнала: 2024, Номер 90, С. 103845 - 103845

Опубликована: Фев. 18, 2024

Язык: Английский

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Artificial Intelligence, Internet of things and 6G methodologies in the context of Vehicular Ad-hoc Networks (VANETs): Survey

ICT Express, Год журнала: 2024, Номер 10(4), С. 959 - 980

Опубликована: Май 22, 2024

Recent developments in the fields of communications, smart transportation systems and computer have significantly expanded potential for intelligent solutions domains traffic safety, convenience efficiency. The utilization Artificial Intelligence (AI) is presently prevalent across diverse sectors application due to its significant capacity augment conventional data-driven methodologies. In domain Vehicular Ad hoc NETworks (VANETs), data regularly gathered from several sources. collected serves multiple goals, such as facilitating efficient routing, enhancing driver awareness, forecasting mobility patterns prevent risks, ultimately passenger comfort, safety overall road experience. Internet thing (IoT) can be a good solution fro many issues that VANETs. order make complete system VANETs communications are very essential like 5G 6G. This study provides detailed examination AI, IoT 5G/6G methodologies currently being investigated by research endeavors field merits demerits AI-based suggested VANET been analyzed with using 5G/6G. conclusion, forthcoming prospects ascertained.

Язык: Английский

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Traffic Prevention and Security Enhancement in VANET Using Deep Learning With Trusted Routing Aided Blockchain Technology

Transactions on Emerging Telecommunications Technologies, Год журнала: 2024, Номер 35(11)

Опубликована: Ноя. 1, 2024

ABSTRACT Vehicular ad hoc networks (VANETs) in portable broadband are a revolutionary concept with enormous potential for developing safe and efficient transportation systems. Because VANETs open that require regular information sharing, it might be difficult to ensure the security of data delivered through as well driver privacy. This paper proposes blockchain technology supports trusted routing deep learning traffic prevention enhancement VANETs. Initially, proposed Feature Attention‐based Extended Convolutional Capsule Network (FA_ECCN) model predicts driver's behaviors such normal, drowsy, distracted, fatigued, aggressive, impaired. Next, Binary Fire Hawks‐based Optimized Link State Routing Protocol (BFH_OLSRP) is used route after trust values have been assessed. Furthermore, Hawks Optimization (BFHO) determines best path based on criteria link stability node degree. Finally, storage supported by Interplanetary File System (IPFS) improve VANET data. Additionally, validation process established using Delegated Practical Byzantine Fault Tolerance (DPBFT). As result, study employs system securely send neighboring vehicles via trust‐based routing, thereby accurately predicting behavior. The method achieves better outcome terms latency, packet delivery ratio (PDR), overhead packets, throughputs, end‐to‐end delay, transmission overhead, computational cost. According simulation results efficiency evaluation, approach outperforms existing approaches enhances vehicle communication an effective manner.

Язык: Английский

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Optimization techniques for IDS‐Generated traffic congestion control in VANET

Internet Technology Letters, Год журнала: 2024, Номер unknown

Опубликована: Апрель 12, 2024

Abstract Vehicular Ad‐hoc Network (VANET) is an emerging field of wireless networks that enables a variety vehicle safety and convenience applications. It employs Intrusion Detection System (IDS) frameworks in its different tiers to ensure reliable secure communication among nodes. However, IDS requires significant amount data process for monitoring intrusive activities the network. As result, volume traffic increases, resulting network congestion. Motivated by this fact, study provides overview optimization techniques VANET congestion control. discusses state‐of‐the‐art analysis along with requirements IDS‐generated highlights control approaches generated identifies challenges domain. This also proposes novel framework reducing combining Local Outlier Factor Random Forest classifier. The proposed achieved high precision while yielding low false positive negative rates. outperformed existing studies increase accuracy 1.16% reduction attack detection time 1.1869 seconds. Additionally, it possible future research directions can be applied address issues Overall, serves as comprehensive guide current status diverse lessen employed academicians researchers.

Язык: Английский

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Integrating Sandpiper Optimization Algorithm and Secure Aware Routing Protocol for Efficient Cluster Head Selection in Wireless Sensor Networks

Lecture notes in networks and systems, Год журнала: 2024, Номер unknown, С. 109 - 120

Опубликована: Янв. 1, 2024

Язык: Английский

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