Fault injection attacks can compromise cryptographic operations with out the necessity of physical alteration, thereby potentially disclosing private keys, which poses a grave threat to the security of elliptic curve digital signature algorithms. This letter presents an efficient and prac tical lattice-based fault injection attack on the SM2 digital signature algorithm (SM2-DSA), along with the corresponding defense strategy. The proposed attack method significantly reduces the moment precision requirements compared to existing lattice-based fault attacks, while also demonstrating computational advantages over other fault attacks. From the defensive perspective, the proposed strategy effectively defend against the lattice-based fault injection attacks while minimizing both time and spatial overheads. Specifically, our countermeasure incurs only a 0.8% time overhead and the area overhead for secure design does not exceed 1%. Our research provides valuable insights for evaluating the security of hardware implementations of SM2-DSA.

In this paper, a novel reinforcement learning (RL) approach, continuous dynamic policy programming (CDPP) is proposed to tackle the issues of both learning stability and sample efficiency in the current RL methods with continuous actions. The proposed method naturally extends the relative entropy regularization from the value function-based framework to the actor-critic (AC) framework of deep deterministic policy gradient (DDPG) to stabilize the learning process in continuous action space. It tackles the intractable softmax operation over continuous actions in the critic by Monte Carlo estimation and explores the practical advantages of the Mellowmax operator. A Boltzmann sampling policy is proposed to guide the exploration of actor following the relative entropy regularized critic. Evaluated by several benchmark tasks, the proposed method clearly illustrates the positive impact of the relative entropy regularization including efficient exploration behavior and stable policy update in RL with continuous action space and successfully outperforms the related baseline approach in both sample efficiency and learning stability.

In this article, we address the issues of stability and data-efficiency in reinforcement learning (RL). A novel RL approach, Kullback–Leibler divergence-regularized distributional RL (KLC51) is proposed to integrate the advantages of both stability in the distributional RL and data-efficiency in the Kullback-Leibler (KL) divergence-regularized RL in one framework. KLC51 derived the Bellman equation and the TD errors regularized by KL divergence in a distributional perspective and explored the approximated strategies of properly mapping the corresponding Boltzmann softmax term into distributions. Evaluated by several benchmark tasks with different complexity, the proposed method clearly illustrates the positive effect of the KL divergence regularization to the distributional RL including exclusive exploration behaviors and smooth value function update, and successfully demonstrates its significant superiority in both learning stability and data-efficiency compared with the related baseline approaches.

Environmental perception system is the premise of the safety and stability of the autonomous vehicle system. However, studies have shown that the on-board sensors included in the perception system are extremely vulnerable to external attacks and interference, leading to incorrect driving strategies and bringing great security threats. Aiming at the problem, this paper divides the vehicle-mounted sensors into a positioning group and an identification group according to their role in the perception system. Then, based on the information correlation between sensors in the same group and the information correlation of a single sensor on adjacent time series, the distance metric model between sensors in a group and the distance metric model for each sensor of this group on time series is established. And the normal distance intervals corresponding to the confidence interval are calculated respectively. According to the distance metric model between sensors, we can detect anomalies in the perception system in real-time. Further, according to the distance metric model for each sensor on adjacent time series, we can identify anomaly sensors. Our experimental results quantitatively show that the method achieves real-time anomaly recognition, and demonstrate the effectiveness and robustness of the method on the open-source KITTI dataset.