The autonomous driving industry has mushroomed over the past decade. Although autonomous driving has undoubtedly become one of the most promising technologies of this century, its development faces multiple challenges, of which security is the major concern. In this article, we present a thorough analysis of autonomous driving security. First, the attack surface of autonomous driving is presented. After an analysis of the operation of autonomous driving in terms of key components and technologies, the security of autonomous driving is elaborated in four dimensions: 1) sensors; 2) operating system; 3) control system; and 4) vehicle-to-everything (V2X) communication. Sensor security is examined from five components, which are mainly responsible for self-positioning and environmental perception. The analysis of operating system security, the second dimension, is concentrated on the robot operating system. Concerning the control system security, the controller area network is approached mainly from vulnerabilities and protection measures. The fourth dimension, V2X communication security, is probed from four categories of attacks: 1) authenticity/identification; 2) availability; 3) data integrity; and 4) confidentiality with corresponding solutions. Moreover, the drawbacks of existing methods adopted in the four dimensions are also provided. Finally, a conceptual multilayer defense framework is proposed to secure the information flow from external communication to the physical autonomous vehicle.

With large deployment of wireless sensor networks, anomaly detection for sensor data is becoming increasingly important in various fields. As a vital data form of sensor data, time series has three main types of anomaly: point anomaly, pattern anomaly, and sequence anomaly. In production environments, the analysis of pattern anomaly is the most rewarding one. However, the traditional processing model cloud computing is crippled in front of large amount of widely distributed data. This paper presents an edge-cloud collaboration architecture for pattern anomaly detection of time series. A task migration algorithm is developed to alleviate the problem of backlogged detection tasks at edge node. Besides, the detection tasks related to long-term correlation and short-term correlation in time series are allocated to cloud and edge node, respectively. A multi-dimensional feature representation scheme is devised to conduct efficient dimension reduction. Two key components of the feature representation trend identification and feature point extraction are elaborated. Based on the result of feature representation, pattern anomaly detection is performed with an improved kernel density estimation method. Finally, extensive experiments are conducted with synthetic data sets and real-world data sets.

This paper addresses the robust angle-of-arrival (AOA) localization problem for three dimensional (3D) sensor networks in adversarial environment. The problem becomes more challenging in practical scenarios where attack information is unknown and background noise has heavy tailed property. With reasonable assumptions on the attack model and non-Gaussian noise, we propose a robust sparse regularization formulation by adopting the l ₁ -norm as loss function to meet the need for heavy-tailed noise and nonconvex Cauchy function as penalty due to the sparsity of attack perturbations. To solve this nonconvex and nonsmoothed source localization problem, we develop a new smoothed Cauchy-regularized least absolution (SCLA) estimator based on the alternating direction of multipliers method (ADMM), and provide the sufficient convergence condition for SCLA. Unfortunately, the solution of SCLA is biased due to the correlation between noise vector and measurement matrix in the pseudolinear measurement equations. To handle this bias problem, an instrumental-variable based Cauchy-regularized least absolution (IVSCLA) estimator is finally proposed by introducing the instrumental-variable estimator into SCLA. Extensive simulation experiments demonstrate the performance of IVSCLA has advantages over state-of-the-art algorithms in robustness and effectiveness for different noise distribution.