Detection and Identification of Malicious Cyber-Attacks in Connected and Automated Vehicles’ Real-Time Sensors

Abstract

Connected and automated vehicles (CAVs) as a part of Intelligent Transportation Systems (ITS) are projected to revolutionise the transportation industry, primarily by allowing real-time and seamless information exchange of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I). However, these connectivity and automation are expected to offer vast numbers of benefits, new challenges in terms of safety, security and privacy also emerge. CAVs continue to rely heavily on their sensor readings, the input obtained from other vehicles and the road side units to inspect roadways. Consequently, anomalous reading of sensors triggered by malicious cyber attacks may lead to fatal consequences. Hence, like all other safety-critical applications, in CAVs also, reliable and secure information dissemination is of utmost importance. As a result, real time detection of anomaly along with identifying the source is a pre-requisite for mass deployment of CAVs. Motivated by this safety concerns in CAVs, we develop an efficient anomaly detection method through the combination of Bayesian deep learning (BDL) with discrete wavelet transform (DWT) to improve the safety and security in CAVs. In particular, DWT is used to smooth sensor reading of a CAV and then feed the data to a BDL module for analysis of the detection and identification of anomalous sensor behavior/data points caused by either malicious cyber attacks or faulty vehicle sensors. Our numerical experiments show that the proposed method demonstrates significant improvement in detection anomalies in terms of accuracy, sensitivity, precision, and F1-score evaluation metrics. For these metrics, the proposed method shows an average performance gain of 7.95%, 9%8.77% and 7.33%, respectively when compared with Convolutional Neural Network (CNN-1D), and when compared with BDL, the corresponding numbers are 5%, 7.9%7.54% and 4.1% respectively.

QoIoTX: A QoE-Aware IoT Service Discovery Framework

“…a) Quality of Things Experience QoTX Sub-Layer -Evaluating Quality of Data QoD. Erroneous data can arise from different hardware and/or software sources leading to faulty decisions, business losses and maybe life-threatening situations [14]. In the IoT environment, a sensor's outlier can be defined as" an irregularity or a divergence in sensor behaviour when compared to its previous behaviour or readings" [15].…”

Naturalistic Driving Data-Based Anomalous Driving Behavior Detection Using Hypertuned Deep Autoencoders

“…The author of [27] provides a preference system that provides path recommendations to the driver to understand their behavior. According to [28], the safety of roadway inspections by connected and automated vehicles (CAVs) is at risk due to their heavy reliance on sensor readings, information from other vehicles, and roadside units. To address these concerns, the authors developed an efficient approach that identifies inconsistencies in CAVs using Bayesian deep learning (BDL) with discrete wavelet transform (DWT), which enhances security and safety in CAVs.…”

“…According to numerical experiments, the proposed method in [28] significantly improves the accuracy, sensitivity, precision, and F1-score evaluation metrics for detecting anomalies. On average, the proposed method in [28] demonstrates performance gains of 7.95%, 9%, 8.77%, and 7.33% compared to the convolutional neural network (CNN). The corresponding gains are 5%, 7.9%, 7.54%, and 4.1% when compared to BDL.…”

Exploring Vehicle Security Improvement Framework in the Safety Messaging System to Protect Against Hackers

“…The authors provide a probability-based feature vector that can extract network packets from the in-vehicular system. In return, the DNN can provide a probability of each class discriminating normal and attack packets (Deng, Gao, Lu, and Gao, 2018), thus, the sensor can identify malicious attacks on the vehicle (Deng, Gao, Lu, & Gao, 2018;Eziama, Awin, Ahmed, Santos-Jaimes, Pelumi, & Corral-De-Witt, 2020). The research conducted an unsupervised pre-training method of deep belief networks (Wang, Qiao, Liu, and Shen, 2021), to include the stochastic gradient descent method (Sun, Qiao, Liao, and Li, 2020) can extract in-vehicular network packets (Wang, Qiao, Liu, and Shen, 2021;Sun, Quio, Liao, & Li, 2020).…”