Analysis of Network Security Technology of Smart Power Internet of Things Under the Background of Ubiquitous Internet of Things Based on Intelligent Algorithms

Along with the rapid development of Internet of Things (IOT), the power system is confronted with more and more threats to network security. Traditional network security protection methods are not able to deal with the dynamic, heterogeneous and real time demand in the power IOT. In this paper, an IPW security model based on Adaptive Reinforcement Learning (ARL) and Game Theory is proposed. The model implements adaptive protection strategy through the ARL algorithm, which can dynamically adjust defense measures according to real-time network data and attack behavior to improve protection efficiency. At the same time, the game theory model simulates the game process between attackers and defenders, optimizes the decision-making strategy of the system., and enables the protection system to predict attack behavior and take corresponding countermeasures. The experiment results show that the new algorithm can significantly increase the detection rate., the response time and the false alarm rate than the conventional IDS. In particular., the detection precision is increased by 20%., the response time is decreased by 15%., and the false alarm rate is decreased remarkably. This model not only can deal with the security threat of IPW., but also has the ability of adaptive and flexible., and can make intelligent adjustment of protective strategies in accordance with different attack patterns.