The combination of self-supervised learning and adversarial training (AT) can significantly improve the adversarial robustness of self-supervised models. However, the robustness of self-supervised adversarial training (self-AT) still lags behind that of state-of-the-art (SOTA) supervised AT (sup-AT), even though the performance of current self-supervised learning models has already matched or even surpassed that of SOTA supervised learning models. This issue raises concerns about the secure application of self-supervised learning models. The inclusion of adversarial training turns self-AT into a challenging joint optimization problem, and recent studies have shown that the data augmentation methods necessary for constructing positive pairs in self-supervised learning negatively impact the robustness improvement in self-AT. Inspired by this, we propose 3SAT, a simple self-supervised adversarial training framework. 3SAT conducts adversarial training on original, unaugmented samples, reducing the difficulty of optimizing the adversarial training subproblem and fundamentally eliminating the negative impact of data augmentation on robustness improvement. Additionally, 3SAT introduces a dynamic training objective scheduling strategy to address the issue of model training collapse during the joint optimization process when using original samples directly. 3SAT is not only structurally simple and computationally efficient, reducing self-AT training time by half, but it also improves the SOTA self-AT robustness accuracy by 16.19\% and standard accuracy by 11.41\% under Auto-Attack on the CIFAR-10 dataset. Even more impressively, 3SAT surpasses the SOTA sup-AT method in robust accuracy by a significant margin of 11.25\%. This marks the first time that self-AT has outperformed SOTA sup-AT in robustness, indicating that self-AT is a superior method for improving model robustness.

The growing flexibility of 5G network architectures increases the risk of network faults. Such network fault types are diverse and variability, as 5G networks have been integrated in various vertical industries, e.g., power grids, resulting in these faults being widespread and challenging to diagnose. To reduce the costs associated with fault identification and remediation, we introduce Rsm-RCA as a novel framework for automated root cause analysis (RCA) in 5G networks. By training on massive amounts of data, Rsm-RCA is capable of identifying fault types from the fault signalling messages collected. Specifically, this framework efficiently collects and processes radio resource control (RRC) fault signalling messages from commercial networks to extract multidimensional attributes and KPI parameters. The processed signalling messages are then fed into a decision tree model, which enables accurate fault classification with minimal time expenditure after training. For power utilities private 5G networks, fast and accurate RCA is highly beneficial. Experimental results demonstrate that Rsm-RCacan classify faults in an extremely short time, achieving an average accuracy of 99 %.

The growing complexity and diversity of 5G network architecture (e.g., power grid 5G network) have made security risk assessment and root cause analysis increasingly challenging. Recent advances in large language models (LLMs) have the potential to transform this landscape. However, existing LLMs-based solutions primarily focus on understanding the language of 5G telecommunications, while overlooking potential security vulnerabilities in the data flows. To facilitate LLMs' in-depth application, this paper presents RCA-LLM, a novel fault root cause analysis framework for 5G networks developed from tailored LLMs-based solutions. In explicit terms, RCA-LLM is trained by inputting processed and organized fault information for fine-tuning, and combined with retrieval-augmented generation (RAG) technology to significantly improve the accuracy of 5G fault analysis. Our experimental results indicate that RCA-LLM performs well in fault analysis, effectively supporting users in diagnosing and resolving fault issues. Model evaluation results further demonstrate that the model significantly improves fault analysis accuracy and has high practical value. In addition, RCA-LLM provides important reference value for efficient operation and maintenance management of 5G and future power grid networks, while also offering new ideas for advancing intelligent fault analysis.

Self-supervised learning (SSL) is a technique used to learn feature representations from unlabeled data. However, existing SSL frameworks either rely too heavily on domain knowledge due to their design based on feature invariance, leading to a lack of domain transferability, or they are based on autoencoder designs, which generate features with redundant low-level semantics, resulting in suboptimal model representations. In this work, we introduce a novel Domain Agnostic Self-Supervised Learning framework called DASSL, which learns superior high-level feature representations of samples by reconstructing the samples’ missing information in the representation space. DASSL does not require additional domain priors, and compared to successful SSL methods, DASSL achieves competitive representation quality. Moreover, when DASSL incorporates domain-related data augmentation techniques, it outperforms successful methods across multiple datasets and evaluation protocols.

Satellite-based cloud computing cybersecurity threats have long posed significant challenges, particularly for cloud infrastructure operators.While prior research has partially addressed these issues by mitigating threats and enhancing human response efficiency, this paper proposes a novel AI-Driven Threat Analysis and Response (TAR) framework.The study progresses in three main phases: (1) redefining urgent threats through a novel formula; (2) implementing a triage and analysis framework using augmented Large Language Models (LLMs); and (3) automating incident response via a Security Orchestration, Automation, and Response (SOAR) platform.Our prototype, tested in a simulated public cloud environments using real production threats, demonstrated a 17% improvement in handling low-and medium-urgency threats.Experimental results show our approach achieves 97.8% coverage in automatic threat classification, significantly outperforming traditional manual methods, which achieve 77.8% coverage.With high recall and precision in managing low-and medium-urgency threats, our method enhances manual efficiency through SOAR-enabled automation.Furthermore, * Corresponding Author.our augmented method surpasses the state-of-the-art GPT-4 Turbo model in addressing security threats containing Chinese characters.

The signaling collected in mobile communication networks can intuitively display the operational status of the system, which can use to locate faults. This paper proposes a novel signaling-based end-to-end fine-grained troubleshooting (simFGT) method for 6G NTN networks. First, the signaling collected from the core network is analyzed to extract multidimensional KPIs and attribute information. Second, a root cause localization algorithm is employed for fine-grained fault localization. Third, a lightweight data-driven ensemble learning method is adopted, with the abnormal KPI of the localized root cause node as inputs, and precise fault classification is achieved through data-driven weight optimization. Experiments results show that the proposed lightweight SimFGT method achieves best balance between precision and recall, resulting in highest F1 score, outperforming current state-of-the-art solutions.

The ever-increasing complexity and scale of 5G communication networks pose huge challenges to network operations. Root cause analysis is considered as a promising method for fault detection. However, it still suffers challenges of severely uneven distribution of fault data, low accuracy in root cause detection, and long time consumption due to a large search space in 5G cellular networks. To address the above challenges, we introduce SimRCA to effectively analyze the faults’ root causes in 5G networks using signalling messages. By designing a novel confidence threshold value and pruning technique, SimRCA can significantly reduce the search space of signalling messages while maintaining the accuracy of root cause analysis. Moreover, SimRCA is proven to be able to handle unbalanced data distribution in 5G networks. We collected over 10GB of signalling data from Huawei 5G commercial network and conducted extensive experiments on this dataset. Experimental results demonstrate that SimRCA can complete root cause localization and fault classification within 11 seconds with an average F1-score over 0.93 which outperforms the current state-of-the-art solutions.

With the advancements in Machine Learning (ML) and edge computing, increasing efforts have been devoted toedge video analytics. However, most of the existing works fail to consider the cooperation of edge nodes for ML model caching and video frame scheduling, thus less efficient in practical scenarios with diverse requirements. In this article, we propose a novel approach named ESMO (joint framEScheduling andMOdel caching) to jointly optimize Frame Scheduling and Model Caching (FSMC), aiming at enhancing the performance of edge video analytics. In detail, we decompose the FSMC as three sub-problems, where the first two sub-problems (i.e., user's transmit power and edge computing resources allocation problems) are proven to be quasi-convex and strictly convex, respectively; while the third main sub-problem (i.e., trade-off among the video analytics (VA) accuracy, service delay and energy consumption) is NP-hard. Therefore, an efficient Two-layers Genetic Algorithm based algorithm (i.e., TGA-FSMC) is designed to find the close-to-optimal frame scheduling and the model caching decisions in an iterative manner. Finally, we deploy a target recognition prototype to comprehensively evaluate the practical performance in diverse edge nodes and CNN models. Extensive experiments demonstrate the empirical superiority of the ESMO over alternatives on real-world edge video analytics platforms, and it achieves 37.5%$\sim$87.2% performance improvement.