ABSTRACT Deep Neural Networks (DNNs) have recently achieved remarkable success in the field of network security, and deep neural network‐based Intrusion Detection Systems (IDSs) are able to automatically learn potential patterns from network traffic and detect malicious traffic. However, IDSs are vulnerable to adversarial network traffic, which weakens their defense capabilities. Therefore, IDSs need high‐quality adversarial traffic for adversarial training to improve robustness. Existing adversarial sample generation algorithms are mainly focused on image‐based applications, overlooking the textual features of network traffic and inadequately considering perturbation magnitude. In response to these challenges, this paper proposes a Loss‐Guided Dynamic Step Adversarial Attack for Network Intrusion Detection Systems (LGDA) and a Discrete Adversarial Rounding (DAR) scheme. The LGDA analyzes model loss trend and dynamically adjusts perturbation step size to balance attack effectiveness and perturbation magnitude. The DAR targets discrete text features in network traffic, using integer encoding and rounding to allow partial preservation of perturbation direction, ensuring compatibility between existing adversarial attack algorithms and network traffic. Compared with state‐of‐the‐art adversarial attacks, experimental results on the NSL‐KDD, UNSW‐NB15, and CIC‐IDS2017 datasets using five models (LR, MLP, CNN, LSTM, and Transformer) show that the LGDA improves the attack success rate by 12.9% and reduces the perturbation magnitude by 19.1%. Additionally, the DAR scheme can be incorporated into any adversarial attack algorithm in the field of network security, increasing adversarial sample's attack success rate by 6.9%.

With the wide use of Internet of Things(IoT) devices, security problems follow. To defend and analyze the security threats of IoT, honeypot and honeynet play an increasingly important role. Existing Honeynet management methods are not suitable for the management of simple IoT due to the high cost and complicated construction environment. This paper proposes a lightweight IoT Honeynet based on the MQTT. It manages the honeypot devices in the IoT network through a custom protocol. At the same time, the number of honeypots can be dynamically adjusted according to the current attacked state of the IoT network to save its resources, and the logs of the honeypots can also be collected by the honeynet as an important basis for analyzing hacker attacks. Finally, the experimental result proved the effectiveness of the proposed framework.

Summary Data center network (DCN) is used for transmission, storage, and processing of big data, which plays an important role in cloud computing and CDN distribution. Network topology and routing algorithm are its core research content and key technical issues. The traditional network topology is difficult to guarantee the quality of service in scalability and fault tolerance. The server‐centric DCN topology can ensure the scale of the DCN by recursively increasing the number of network nodes and links. Relative to the Dcell, BCube, and BCCC typical network topology, PTNet network as a typical representative of a new type of the server‐centric DCN topology, which has more advantages in scalability, fault tolerance, and so on. The PTNet network topology is theoretically analyzed in terms of network diameter, bottleneck throughput, and total number of links in the network. Based on the deep research of PTNet network, this article analyzes and studies the network topology, multicast, and broadcast routing algorithm.