A secure distributed estimation method against data integrity attacks is presented for general Internet-of-Things (IoT) systems. By exploiting the spatial sparsity of the attacks, a robust optimal estimation objective to protect the entire IoT system is established. Then, a false data processor is developed to suppress the bad effects of the attacks. The convergence of the presented method is analyzed. It shows that, under accessible conditions, the estimation error will converge to be uniformly bounded for all cases, i.e., all communication links and transmitted data are arbitrarily compromised. Furthermore, by utilizing the property of persistence, a quantitative trust model is established for IoT systems based on the past behaviors of the nodes. Then, a trust-based distributed estimation method is proposed to enhance security. Simulation results by considering an IoT system with 50 nodes and 145 edges are also provided to verify our theoretical results.Note to Practitioners—The increasingly serious attack events that happened in recent years demonstrate the importance of security in the development of Internet-of-Things (IoT) systems. Different from the traditional Internet, IoT systems are usually constrained by computing and storage resources. Then, complex encryption and authentication algorithms or protocols will not be applicable to IoT systems. Distributed estimation plays an important role in the collaborative control, monitoring, and management of IoT systems. Inevitably, attacks to distributed estimation could make false data spread throughout the networks and further cause terrible consequences, while the issues of network security have not been yet taken into consideration in the existing design of distributed estimation. In this article, we propose a secure distributed estimation method. By adding a security mechanism into the distributed estimation algorithm, the ability of resistance to any data integrity attacks can be achieved. Moreover, the possible damage will be completely controllable for arbitrary attacks. That is, our method can guarantee that the estimation performance will always be acceptable even in the worst case. Besides, it is simple and lightweight, and no additional communication consumption and hardware devices are needed for its deployment and operations.

We revisit the problem of Electronic Control Unit (ECU) identification from signal characteristics in the physical layer of in-vehicle Controller Area Network (CAN). Ringing is oscillation of a signal in signal processing and prior work has already noticed the ringing force in identifying ECUs via differ-ential CAN signaling. However, they either count on advanced do-main transformations or turn out to be fragile once CAN topology alters (e.g., the length of the stub lines, the number of connected ECUs, etc). The paper further dissects the connection between the ringing intensity and signal characteristics, based on which we can procure robust ECU identification scheme (and IDS, intrusion detection system) independent of CAN topology without any mathematical transformations. We first look into the factors enlarging signal ringing, and then abstract the characterization of ringing intensity where rising edges (from recessive to dominant states) and falling edges (from dominant to recessive states) make distinct contributions to ECU identification. The rising edges (along with dominant states) suffice to fingerprint the ECUs, whereas the falling edges pitch in shaky performance (correlated strongly to CAN topology). The unique contribution of the former can thereby be isolated to construct robust mechanisms for ECU identification and IDS. Robustness and accuracy of the resulting approach are validated on not only prototype implementations (with diverse portfolio in 3/6/9/13 ECUs and 1/2/3-meter stub lines) but real vehicles. Concrete comparisons among prior work and ours are also conducted and substantiate our argumentation and mechanisms.

A collaborative security detection method is investigated for the Internet of Things (IoT) systems. Consensus protocol is utilized to implement the information sharing and fusion in a collaborative manner. A game theoretical analysis framework is developed for the collaborative security detection by considering the confrontation between the defender and the attacker. The objective is to achieve the maximum security protection for the entire IoT systems. The existence and uniqueness of the Nash equilibrium of game model with complete consensus are analyzed. Then an iteration learning based calculation method is presented to determine the Nash equilibrium. Quantitative analysis is provided for the relationship between the Nash equilibriums of the game models in the cases of complete and incomplete consensus with infinite and finite number of iterations. Simulation results by considering DDoS attack are also provided to verify our theoretical results.