Detection and Isolation of Sensor Attacks for Autonomouvehicles |
Author(s): |
| Polasa Ankush , Joginpally BR Engineering College; Pyarasani Rishika, Joginpally BR Engineering College; Manne Arun Sagar, Joginpally BR Engineering College; Upputuri Chandramouli , Joginpally BR Engineering College; Suresh Kampe, Joginpally BR Engineering College |
Keywords: |
| Autonomous Vehicles, Sensor Attacks, Anomaly Detection, Sensor Fusion, Cybersecurity |
Abstract |
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Autonomous vehicles rely heavily on multi-sensor systems to perceive and interact with their environment. However, these sensors are vulnerable to malicious attacks such as spoofing, jamming, and adversarial manipulation. This study proposes a hybrid framework integrating residual-based anomaly detection and machine learning classification for effective detection and isolation of sensor attacks. Using a simulation-based experimental design in the CARLA environment, the proposed model achieved high detection accuracy (96.4%) and isolation accuracy (93.1%) across multiple attack scenarios. The findings highlight the importance of integrating statistical and learning-based approaches to enhance system resilience. The study contributes to both theoretical and practical advancements in autonomous vehicle security. |
Other Details |
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Paper ID: IJSRDV14I20158 Published in: Volume : 14, Issue : 2 Publication Date: 01/05/2026 Page(s): 123-127 |
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