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- Exploring Emerging Trends in 5G Malicious Traffic Analysis and Incremental Learning Intrusion Detection Strategies(arXiv)
Author : Zihao Wang, Kar Wai Fok, Vrizlynn L. L. Thing
Abstract : The popularity of 5G networks poses a huge challenge for malicious traffic detection technology. The reason for this is that as the use of 5G technology increases, so does the risk of malicious traffic activity on 5G networks. Malicious traffic activity in 5G networks not only has the potential to disrupt communication services, but also to compromise sensitive data. This can have serious consequences for individuals and organizations. In this paper, we first provide an in-depth study of 5G technology and 5G security. Next we analyze and discuss the latest malicious traffic detection under AI and their applicability to 5G networks, and compare the various traffic detection aspects addressed by SOTA. The SOTA in 5G traffic detection is also analyzed. Next, we propose seven criteria for traffic monitoring datasets to confirm their suitability for future traffic detection studies. Finally, we present three major issues that need to be addressed for traffic detection in 5G environment. The concept of incremental learning techniques is proposed and applied in the experiments, and the experimental results prove to be able to solve the three problems to some extent.
2.Towards Robust Graph Incremental Learning on Evolving Graphs (arXiv)
Author : Junwei Su, Difan Zou, Zijun Zhang, Chuan Wu
Abstract : Incremental learning is a machine learning approach that involves training a model on a sequence of tasks, rather than all tasks at once. This ability to learn incrementally from a stream of tasks is crucial for many real-world applications. However, incremental learning is a challenging problem on graph-structured data, as many graph-related problems involve prediction tasks for each individual node, known as Node-wise Graph Incremental Learning (NGIL). This introduces non-independent and non-identically distributed characteristics in the sample data generation process, making it difficult to maintain the performance of the model as new tasks are added. In this paper, we focus on the inductive NGIL problem, which accounts for the evolution of graph structure (structural shift) induced by emerging tasks. We provide a formal formulation and analysis of the problem, and propose a novel regularization-based technique called Structural-Shift-Risk-Mitigation (SSRM) to mitigate the impact of the structural shift on catastrophic forgetting of the inductive NGIL problem. We show that the structural shift can lead to a shift in the input distribution for the existing tasks, and further lead to an increased risk of catastrophic forgetting. Through comprehensive empirical studies with several benchmark datasets, we demonstrate that our proposed method, Structural-Shift-Risk-Mitigation (SSRM), is flexible and easy to adapt to improve the performance of state-of-the-art GNN incremental learning frameworks in the inductive setting.