Design and optimization of multi-level distributed attack defense algorithm in Internet of Things environment

The security challenges in the Internet of Things (IoT) environment are becoming increasingly severe. Distributed attacks such as distributed denial of service (DDoS) and malware spread threaten the normal operation of IoT system and user privacy. This paper designs and optimizes a multi-level distributed attack defense algorithm, which makes full use of the distributed characteristics of IoT devices to build a three-dimensional defense system of network layer, device layer and data layer. The network layer adopts a traffic analysis model based on convolutional neural network (CNN), which can monitor and identify abnormal traffic patterns in real time. In the device layer, the behavior analysis model based on support vector machine (SVM) is used to monitor the behavior of devices and isolate infected devices in time. The data layer uses the private chain version of Ethereum to build a blockchain network, and uses AES algorithm to encrypt data storage and transmission. The algorithm introduces AI technology to realize active defense and dynamic monitoring, and improve the accuracy and efficiency of defense. In addition, this paper also puts forward dynamic update strategy and cross-platform compatibility strategy to ensure that the algorithm can resist new attacks continuously and effectively and run stably on different operating systems and hardware platforms. The experimental results show that the proposed algorithm is superior to traditional defense algorithms in attack detection rate, false alarm rate and response time, showing good adaptability and cross-platform compatibility, which provides an effective solution for distributed attack defense in IoT environment.