The widespread use of encryption protocols and increasing privacy demands have significantly increased encrypted traffic, creating new challenges for network monitoring and threat detection. Current methods struggle with diverse scenarios and distinguish between subtle traffic patterns within webpages of the same application. To address these challenges, we introduce ANT-ET, an end-to-end multimodal framework designed for fine-grained encrypted webpage traffic fingerprinting. ANT-ET leverages a transformer to model payload semantics and constructs a traffic interaction graph to capture both temporal and spatial characteristics of packet interactions. Additionally, ANT-ET incorporates a gradient reversal layer to improve generalization by facilitating domain-invariant feature learning across related webpages. Experimental results demonstrate ANT-ET’s superior performance compared to various baseline models, which were evaluated using a proprietary encrypted webpage traffic dataset and three public datasets. Ablation studies confirm the effectiveness of different framework components, while sensitivity and complexity analyses further validate ANT-ET’s robustness and flexibility.

The evolution of cloud computing and microservices is gradually supplanting traditional network deployment schemes within data centers. As applications deploy substantial computing resources in data centers, a fierce competition for network services ensues, marked by stringent quality requirements. Simultaneously, safeguarding the time sensitivity of the main flow becomes imperative. However, prevailing container network solutions primarily ensure service quality through scheduling and orchestration, neglecting the influence of computing resources on network service quality under intense resource competition. Consequently, our focus revolves around exploring the preservation of time-sensitive attributes of primary service network links, aiming to enhance the service quality of container networks in highly competitive computing resource environments. This paper introduces a novel container network solution designed to meet the quality of service requirements for time-sensitive data in container networks. Implemented on the Kubernetes platform, this solution establishes an underlay network structure based on Cilium for transmitting network packets requiring performance guarantees and exhibiting time sensitivity. Utilizing eBPF programs with adjusted CPU affinity for packet forwarding, the solution records packets necessitating quality of service guarantees. Network service quality is ensured through algorithms such as Multiqueue Priority, Earliest TxTime First, Enhancements for Scheduled Traffic, etc. The network packets requiring performance guarantees and time sensitivity refer to the TSN (Time-Sensitive Networking) standard. To assess the solution’s effectiveness, we deployed Kubernetes on two directly connected physical servers. Measurements were conducted in scenarios of both idle and highly competitive computing resources, evaluating bandwidth, latency, and jitter of container access packets across different hosts. The results confirm a noteworthy enhancement in container network service quality under highly competitive computing resource environments.

The Transport Layer Security (TLS) protocol has been widely used in software-defined perimeter (SDP) to establish secure, encrypted connections between distributed SDP components. To improve communication efficiency of its handshake protocol, the latest TLS standard (i.e., TLS 1.3) introduces a zero round-trip-time (0-RTT) handshake. However, traditional 0-RTT handshake protocols lack a forward secure key exchange scheme, so encrypted data that have already been transmitted could be potentially leaked to attackers after the pre-shared key (PSK) is compromised. To achieve secure TLS handshake with minimal communication cost, several forward secure 0-RTT key exchange schemes based on puncturable encryption were proposed. However, they are not applicable to real world SDP environments, because they either need to pre-store a large number of secret keys in the host onboard phase, or require a large number of complex cryptography operations (e.g., bilinear-pairing) in the access phase. Therefore, to avoid high computational overhead while still maintaining communication efficiency and forward security, a novel 0-RTT key exchange scheme based on efficient puncturable key encapsulation mechanism is proposed in this paper. Experimental results show that, with reasonable (and configurable) memory consumption, the latency performance of the proposed scheme is about 30% better than FFDHE3072, which is a practical 1-RTT key exchange scheme in TLS 1.3.

The rapid rise of social networks has led to a proliferation of fake news, especially those with images. The combination of images and text may confuse users and cause even more negative impact. Exisiting methods for fake news detection either require expert knowledge or large amounts of labeled data. In addition, these methods fails to clarify which part of the multimodal information is misleading or why. In this paper, we present a simple yet efficient Chain-of-thought Prompting method for Multimodal Fake News Detection (CP-FEND). It first finds the closest demonstration samples of the news posts to be detected by a KNN-based approach. Afterwards, we design a logical prompt method including Examination, Inference and Determination stages to guide Large Language Model (LLM) to automatically construct reasoning processes for the authenticity of the samples. Finally, LLM are prompted to derive the authenticity of multimodal news with the guidance of samples and Chain-of-Thought reasoning. A reflective verification is performed to further improve the detection performance through comprehensive evaluation of the original responses. Extentsive experiments on two public datasets have demonstrated the superiority of our method over existing methods.

Existing studies for rumor detection rely heavily on a large number of labeled data to operate in a fully-supervised manner. However, manual data annotation in realistic cases is very expensive and time-consuming. In this paper, we propose a novel self-supervised Relation-Event based Contrastive Learning (RECL) framework for rumor detection to address the above issue. Specifically, we present both the relation-level and event-level augmentation strategies to generate contrastive samples, which capture both the semantics revealed by repost relations and the structural features of rumor events. Moreover, contrastive learning tasks are devised to generate informative graph representations by utilizing self-supervision signals of unlabeled data. Extensive experimental results on real-world datasets demonstrate the effectiveness of our model, especially with limited labeled data.

Few-shot Named Entity Recognition (NER) aims to recognize unseen name entities based on a tiny support set that consists of seen name entities and labels, which is obviously different from traditional supervised NER methods. Contrastive learning has become a popular solution for few-shot NER, which improves the robustness of NER to handle unlabeled entities by learning a similarity metric to measure the semantic similarity between test samples and entity labels. However, existing contrastive learning based NER methods individually learn the word embedding in source and target domains, ignoring connections between entities with the same label and limiting the effectiveness of contrast learning. In this paper, we propose a novel few-shot NER framework that jointly models different domain texts and optimizes a generalized objective of differentiating between words in all stages. The proposed model builds the cross-domain attention layer to enhance the feature representations of words and transfer the entity similarity information from the source domain to the target domain. This significantly reduces the divergence between entities with same label. Experimental results on the largest Few-shot NER dataset show that CDANER significantly outperforms all baseline methods, which verifies the effectiveness and robustness of the proposed model.

In recent years, the concept of Zero Trust Networks (ZTN) has been proposed to overcome unrealistic security assumptions, e.g., what lies in private networks (such as data centers) is always trusted and safe. In ZTN, no device or user is assumed to be secure, instead all connections have to be authenticated and authorized before being established. Software Defined Perimeter (SDP) is one of the most promising solution for ZTN, where the gateway allows clients to access services only after receiving legitimate Single Packet Authorization (SPA) data. However, existing SDP solutions either (1) need to decouple the SPA from the connection request, resulting in redundant communication processes and impersonation attacks; or (2) need to copy the SPA data to the user space from sniffers, causing the packets to enter the protocol stack repeatedly. Due to the large number of short-lived streams in the data center, inefficiency and insecurity of the SPA process lead to severe connection delays and network attacks (e.g., DDoS). To this end, we propose an eBPF-based Dynamic Perimeter (EDP) to enhance the security and performance of SDP. By using EDP, authentication data can be efficiently embedded into every packet and checked before entering the receiver's protocol stack. Experimental results show that the connection delay of EDP is 80% less than that of the existing state-of-the-art solutions.

Social relations are closely related to each of us and are a crucial part of society. Recognizing the social relationships of people in pictures can improve AI’s understanding of human behavior, thereby facilitating collaborative interactions between computers and humans. Previous work only focused on a single picture, so too little information can be obtained. In this paper, we proposed Picture Reasoning Model(PRM) to achieve relationship classification, which innovatively uses the self-attention method to learn the association between relationships. The association between relationships is at the social level, thus using it to assist relationship recognition can get rid of the problem of insufficient information in a single picture. In addition, the model also adopts a two-stream approach, extracting both characters and global features for getting multiple perspectives information. We conduct extensive experiments on two benchmark datasets PIPA and PISC. Experimental results show that our model has improved the accuracy metric of the datasets compared with SOTA. On the PIPA dataset, the accuracy increases from 64.4% to 65.6%, and on the PISC dataset, the mAP raises from 72.7% to 73.2%, which validates the effectiveness of our proposals.

The Digital Twin Network (DTN) is a key enabling technology for efficient and intelligent network management in modern communication networks. Learning dynamic net-work behaviors at the flow granularity is a core element for realizing DTN with accurate network modelling. However, it is challenging due to the complexity of network architectures and the proliferation of emerging network applications. In this paper, we devise a Packet-Action Sequence Model to represent all possible packets behaviors in a unified way. Besides, we propose a novel and effective algorithm to assess whether the behavior pattern is time dependent or independent by using the temporal characteristics of packets in a network flow, so as to learn the key factors of packets that contribute to network behaviors. Based on two typical scenarios, i.e., packet caching and routing, the experimental results verify that the proposed algorithm can identify network behavior patterns and learn key factors affecting the behaviors with over 99 % accuracy.

Due to the ever-increasing complexity of modern communication networks, network operators are making tremendous efforts on achieving objectives for the network to meet the diversified requirements of many real-world applications. However, network operators are repeatedly taking a lot of time on some common tasks shared by different networks. In order to reduce repetitive human efforts on network management, advanced machine learning paradigms, such as deep reinforcement learning, has received numerous attention in the networking community. Nevertheless, it encounters great difficulty in transferring learned policies to new environments, resulting in new model training and testing for each changed environment setting. To tackle this important issue, in this paper we propose a new framework that is the first of its kind to enable an agent to have transferable knowledge for network management, specifically, for network path selection tasks. Through this framework, an agent can efficiently learn and express the transferable network knowledge for achieving task objectives. Extensive experimental results show that the learned knowledge through the proposed framework can realize some common objectives of path selection tasks across different network environments. In addition, the knowledge learned from one network task can significantly improve the learning performance of another similar but different task.