The widespread adoption of encrypted communication protocols has significantly enhanced network security and user privacy, simultaneously elevating the importance of encrypted traffic analysis across various domains, including network anomaly detection. The Transport Layer Security (TLS) 1.3 protocol, introduced in 2018, has gained rapid popularity due to its enhanced security features and improved performance. However, TLS 1.3’s security enhancements, such as encrypting more of the handshake process, present unprecedented challenges for encrypted traffic analysis, rendering traditional methods designed for TLS 1.2 and earlier versions ineffective and necessitating the development of novel analytical techniques. This comprehensive survey provides a thorough review of the latest advancements in TLS 1.3 traffic analysis. First, we examine the impact of TLS 1.3’s new features, including Encrypted ClientHello (ECH), 0-RTT session resumption, and Perfect Forward Secrecy (PFS), on existing traffic analysis techniques. We then present a systematic overview of state-of-the-art methods for analyzing TLS 1.3 traffic, encompassing middlebox-based interception, searchable encryption, and machine learning-based approaches. For each method, we provide a critical analysis of its advantages, limitations, and applicable scenarios. Furthermore, we compile and review key datasets utilized in machine learning-based TLS 1.3 traffic analysis research. Finally, we discuss the main challenges and potential future research directions for TLS 1.3 traffic analysis. Given that TLS 1.3 is still in the early stages of widespread deployment, research in this field remains nascent. This survey aims to provide researchers and practitioners with a comprehensive reference, facilitating the development of more effective TLS 1.3 traffic analysis techniques that balance network security requirements with user privacy protection.

Reasoning over Temporal Knowledge Graphs (TKGs) aims to forecast future events based on historical ones. Existing approaches typically enforce strict temporal order constraints among past events; however, such rigidity limits the effective exploitation of path information during reasoning, thereby reducing both model flexibility and predictive performance. To address this limitation, this paper introduces SR-RTR (Sub-graph Reasoning based on Relaxed Temporal Relation), an interpretable subgraph reasoning framework designed to fully harness path information within TKGs. By incorporating a relaxed temporal factor, the proposed method softens the chronological constraints on historical events, broadens the sampling scope of candidate nodes during subgraph reasoning, and enhances the efficiency of path information utilization. This mechanism reflects human cognitive intuition: when the temporal gap between two events falls within a certain threshold, their sequence can be considered interchangeable. SR-RTR constructs a query-specific inference subgraph from the TKG and iteratively performs two core operations—subgraph expansion and pruning—until the entity with the highest attention score is identified as the prediction result. Extensive experiments on four benchmark datasets demonstrate that SR-RTR uncovers a greater number of reasoning paths relevant to the target prediction, leading to substantial improvements in both reasoning accuracy and computational efficiency.