A better way to detect sybil attacks in vehiuclar ad hoc networks

Year 2025, Volume: 16 Issue: 1, 81 - 87

Ziya Cihan Taysi

https://doi.org/10.24012/dumf.1578650

Abstract

Sybil attacks, enabled by the anonymous nature of peer-to-peer broadcast communication in vehicular private networks (VANETs), pose a serious security threat. These attacks can significantly disrupt traffic flow, reduce efficiency, and potentially endanger traffic safety. Detecting Sybil attacks in VANETs is particularly challenging due to the dynamic network topology, real-time constraints, and decentralized nature of these networks. This paper proposes a novel Sybil attack detection method for VANETs, leveraging deep learning analysis of received signal strength indicator (RSSI) time series. The proposed system is designed to deliver effective results, even in brief interactions. Experimental results demonstrate the efficacy of our LSTM-based and CNN-based approaches, achieving 93.45% and 94.28% sensitivity in detecting attack messages, respectively.

Keywords

Sybil Attack, Inter Vehicle Communication (IVC), Received Signal Strength Indicator (RSSI), Deep Learning

Tasarsız araç ağlarında sybil saldırılarının Tespiti için daha iyi bir yol

Abstract

Tasarsız araç ağlardaki (VANET) eşler arası yayın iletişiminin anonim doğası gereği ortaya gerçekleştirilebilen sybil saldırıları ciddi bir güvenlik tehdidi oluşturur.
Bu saldırılar trafik akışını önemli ölçüde bozabilir, verimliliği azaltabilir ve potansiyel olarak trafik güvenliğini tehlikeye atabilir. VANET'lerde sybil saldırılarını tespit etmek,
bu ağların dinamik ağ topolojisi, uygulamaların gerçek zamanlı kısıtlamaları ve merkezi olmayan ağ yapısı nedeniyle özellikle zordur. Bu makale VANET'ler için alınan sinyal gücü göstergesi (RSSI) ile oluşturulan zaman serilerinin
derin öğrenme ile analiz edilmesine dayanan yeni bir Sybil saldırısı tespit yöntemi önermektedir. Önerilen sistem, kısa etkileşimlerde bile etkili sonuçlar sunmak üzere tasarlanmıştır. Deneysel sonuçlar, LSTM tabanlı ve CNN tabanlı yaklaşımlarımızın etkinliğini göstererek saldırı mesajlarını tespit etmede sırasıyla %93,45 ve %94,28 hassasiyet elde etmiştir.

Keywords

Sybil saldırı, araçlar arası haberleşme, RSSI, derin öğrenme

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