Endogenous Security Formal Definition,Innovation Mechanisms,and Experiment Research in Industrial Internet

With the rapid development of information technologies,industrial Internet has become more open,and security issues have become more challenging.The endogenous security mechanism can achieve the autonomous immune mechanism without prior knowledge.However,endogenous security lacks a scientific and formal definition in industrial Internet.Therefore,firstly we give a formal definition of endogenous security in industrial Internet and propose a new industrial Internet endogenous security architecture with cost analysis.Secondly,the endogenous security innovation mechanism is clearly defined.Thirdly,an improved clone selection algorithm based on federated learning is proposed.Then,we analyze the threat model of the industrial Internet identity authentication scenario,and propose cross-domain authentication mechanism based on endogenous key and zero-knowledge proof.We conduct identity authentication experiments based on two types of blockchains and compare their experimental results.Based on the experimental analysis,Ethereum alliance blockchain can be used to provide the identity resolution services on the industrial Internet.Internet of Things Application(IOTA)public blockchain can be used for data aggregation analysis of Internet of Things(IoT)edge nodes.Finally,we propose three core challenges and solutions of endogenous security in industrial Internet and give future development directions.

Dynamical analysis,circuit realization,and application in pseudorandom number generators of a fractional-order laser chaotic system

A new five-dimensional fractional-order laser chaotic system(FOLCS)is constructed by incorporating complex variables and fractional calculus into a Lorentz-Haken-type laser system.Dynamical behavior of the system,circuit realization and application in pseudorandom number generators are studied.Many types of multi-stable states are discovered in the system.Interestingly,there are two types of state transition phenomena in the system,one is the chaotic state degenerates to a periodical state,and the other is the intermittent chaotic oscillation.In addition,the complexity of the system when two parameters change simultaneously is measured by the spectral entropy algorithm.Moreover,a digital circuit is design and the chaotic oscillation behaviors of the system are verified on this circuit.Finally,a pseudo-random sequence generator is designed using the FOLCS,and the statistical characteristics of the generated pseudo-random sequence are tested with the NIST-800-22.This study enriches the research on the dynamics and applications of FOLCS.

Decision-Making Models Based on Meta-Reinforcement Learning for Intelligent Vehicles at Urban Intersections

Behavioral decision-making at urban intersections is one of the primary difficulties currently impeding the development of intelligent vehicle technology.The problem is that existing decision-making algorithms cannot effectively deal with complex random scenarios at urban intersections.To deal with this,a deep deterministic policy gradient(DDPG)decision-making algorithm(T-DDPG)based on a time-series Markov decision process(T-MDP)was developed,where the state was extended to collect observations from several consecutive frames.Experiments found that T-DDPG performed better in terms of convergence and generalizability in complex intersection scenarios than a traditional DDPG algorithm.Furthermore,model-agnostic meta-learning(MAML)was incorporated into the T-DDPG algorithm to improve the training method,leading to a decision algorithm(T-MAML-DDPG)based on a secondary gradient.Simulation experiments of intersection scenarios were carried out on the Gym-Carla platform to verify and compare the decision models.The results showed that T-MAML-DDPG was able to easily deal with the random states of complex intersection scenarios,which could improve traffic safety and efficiency.The above decision-making models based on meta-reinforcement learning are significant for enhancing the decision-making ability of intelligent vehicles at urban intersections.