Real-Time Intrusion Detection in Connected Autonomous Vehicles

Kumar, Anjanee; Das, Tanmoy Kanti

doi:10.1007/978-981-99-1624-5_12

Cited by 1 publication

(1 citation statement)

References 17 publications

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“…Decision tree, K-means, K-Nearest Neighbors (K-NN), Support Vector Machine (SVM), Density-based Spatial Clustering Algorithm (DBSCAN), and Enhanced-Density based Spatial Clustering Algorithm (E-DBSCAN) are samples of resilient and successful learning approaches Al-Hawawreh, M et al [17]. The investigators of Kumar A et al [18] developed an IDS employing Support Vector Machine-Radial Basis Function (SVM-RBF) kernel to discriminate between normal and deviant behaviors. The resultant algorithm can intercept the majority of the abnormalities as intended.…”

Section: Related Workmentioning

confidence: 99%

A Novel Constructive Unceasement Conditional Random Field and Dynamic Bayesian Network Model for Attack Prediction on Internet of Vehicle

Mahendran,

Rajendran,

Pandian

et al. 2024

IEEE Access

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Today's Internet of Vehicles (IoV) has soared by leveraging data gathered from transportation systems, yet it grapples with security concerns stemming from network vulnerabilities, exposing it to cyber threats. This study proposes an innovative method to anticipate anomalies and exploit IoV services related to road traffic. Using the Unceasement Conditional Random Field Dynamic Bayesian Network Model (U-CRF-DDBN), this approach predicts the impact of network attacks, strategically managing vulnerable nodes and attackers. Through experimentation and comparisons with existing methods, our model demonstrates its effectiveness in mitigating IoV vulnerabilities. The U-CRF-DDBN strikes a superior balance, outperforming other approaches in intrusion detection for Internet of Vehicles systems. Evaluating its performance on the NSL-KDD dataset reveals a promising average Detection Rate of 93.512% and a low False Acceptance Rate of 0.125% for known attacks, highlighting its robustness. However, with unknown attacks, while the Detection Rate remains at 74.157%, there is an increased FAR of 16.47%, resulting in a slightly lower F1score of 0.822.

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Section: Related Workmentioning

confidence: 99%