With the rapid development of vehicular networking and intelligence, more interfaces are adopted by cars to interact with the external world. Accordingly, this also brings enormous security risks, which are potentially catastrophic due to communication loopholes. Since the Controller Area Network (CAN) is critical to the transmission of commands among vehicular components, it has become a prime target for hacker research and attack. Considering that the CAN bus is commonly used and its protocol is always flawed, how to efficiently detect the intrusions against it has become an evitable problem. In this paper, we presented an intrusion detection system that can be rapidly deployed inside the vehicle. Aiming at achieving the goal of real-time detection, we devised a feature extraction algorithm with low complexity and thoroughly exploited its advantages via a GRU-based lightweight neural network. The experiment was physically conducted on in-vehicle embedded devices using publicly available datasets. Experiment results illustrated that our intrusion detection system could be rapidly deployed with high classification and real-time performance. Moreover, we also discussed how an intrusion detection system could work with OTA services to improve the intelligence of vehicular operating systems and prevent potential attacks.

KSII Transactions on Internet and Information Systems TIIS Paper Details

With the rapid development and application of intelligent traffic systems, traffic flow prediction has attracted an increasing amount of attention. Accurate and timely traffic flow information is of great significance to improve the safety of transportation. To improve the prediction accuracy of the backward-propagation neural network (BPNN) prediction model, which easily falls into local optimal solutions, this paper proposes an adaptive differential evolution (DE) algorithm-optimized BPNN (DE-BPNN) model for a short-term traffic flow prediction. First, by the mutation, crossover, and selection operations of the DE algorithm, the initial weights and biases of the BPNN are optimized. Then, the initial weights and biases obtained by the aforementioned preoptimization are used to train the BPNN, thereby obtaining the optimal weights and biases. Finally, the trained BPNN is utilized to predict the real-time traffic flow. The experimental results show that the accuracy of the DE-BPNN model is improved about 7.36% as compared with that of the BPNN model. The DE-BPNN is superior to the performance of three classical models for short-term traffic flow prediction.

With the continuous development of science and technology, people’s lifestyle becomes more and more intelligent, especially in intelligent transportation. However, running in a random environment, safety can be affected by various factors during operation. For an intelligent car, guaranteeing its safety in operation is important to the passengers in the vehicle. So, it is vital to verify the safety of the system of the smart car. This study proposes an integration formal method with stochastic Petri nets and Z (SPZN). Stochastic Petri nets can better simulate the occurrence of random events in the driving process of intelligent cars. With the advantages of the frame structure of Z language, the concurrent process and state before and after the system at different times can be better described. In addition, the frame structure of Z language can solve the problem of state explosion in Petri nets. Using this method, the random events that may occur during the operation can be formally modeled, and the subsequent behavior of the vehicle can be analyzed and predicted effectively. Using the reinforcement learning, the parameter in the stochastic Petri nets can be optimized, which can reduce the probability of bad states and ensure the stability and security of the system. Moreover, a case study of the intelligent car modeled by stochastic Petri nets and Z is given. The results show that it can improve the safety and effectiveness of the smart vehicle driving system.

Immutability and traceability are the main reasons for the popularity of blockchain. Nevertheless, its transparency also makes the transactions visible to all participants, which seriously violates the privacy of some dealers. In order to transfer accounts over blockchain, all verifiers should be empowered with the ability to confirm the transactions, leading to the conflict between extensive consensus and individual privacy. Orienting to privacy issues of UTXO (Unspent Transaction Output), this paper exploited the unconditional security of Shamir’s secret sharing to construct a logic-physical map for public chain, which enabled noninteractive transaction verification without privacy infringement. A comprehensive analysis indicated that the proposed scheme is secure under UC (Universal Composability) framework with practical computation and communication overheads.