Knowledge Tracing (KT) diagnoses students’ concept mas- tery through continuous learning state monitoring in education. Existing methods primarily focus on studying behavioral sequences based on ID or textual information. While existing methods rely on ID-based sequences or shallow textual features, they often fail to capture (1) the hierarchical evolution of cognitive states and (2) individualized prob- lem difficulty perception due to limited semantic modeling. Therefore, this paper proposes a Large Language Model Hyperbolic Aligned Knowledge Tracing(L-HAKT). First, the teacher agent deeply parses question semantics and explicitly constructs hierarchical dependencies of knowledge points; the student agent simulates learning behaviors to generate synthetic data. Then, contrastive learning is performed between synthetic and real data in hyperbolic space to reduce distribution differences in key features such as question difficulty and forgetting patterns. Finally, by optimizing hyperbolic curvature, we explicitly model the tree-like hierarchical structure of knowledge points, precisely characterizing differences in learning curve morphology for knowledge points at different levels. Extensive experiments on four real-world educational datasets validate the effectiveness of our Large Language Model Hyperbolic Aligned Knowledge Tracing (L-HAKT) framework.

Federated Noisy Labels Learning (FNLL) allows global model to be jointly trained on multiple clients with varying degrees of noisy labels while preserving privacy, and despite recent research advances, distinguishing between client clean and noisy samples is still tricky since the distribution of labels among clients is always both noisy and class-imbalanced, leading to the poor performance of existing FNLL methods. To address this problem, we propose a novel framework called FedPN, the first framework to utilize Contrastive Language-Image Pre-training (CLIP) for federated noisy labels tasks. Then, to achieve higher performance for the global model, we introduce an attention based Prototype Adapter to identify more plausible local data for local model training, further improving training stability. We validate the effectiveness of FedPN by conducting extensive experiments on benchmark datasets under both Independently and Identically Distributed (IID) and Non-IID data partitions. The experimental results show that FedPN can effectively filter noisy samples from different clients, and compared with the state of-the-art FNLL method, the FedPN achieves at most and at least 8.39% and 0.88% performance improvement in the case of highly heterogeneous noisy labels.

With the advancement of vehicle-to-vehicle (V2V) ad hoc networks and wireless communication technologies, mobile edge caching has become a key enabler for enhancing network performance and user experience. However, traditional federated learning-based collaborative caching approaches in vehicular scenarios suffer from inadequate client selection mechanisms and limited prediction accuracy, which result in suboptimal cache hit ratios and increased content transmission latency. To address these challenges, we propose a Digital Twin-based Asynchronous Federated Learning-driven Predictive Edge Caching with Deep Reinforcement Learning (DAPR) framework. DAPR employs an intelligent client selection strategy based on asynchronous federated learning, which leverages mobility prediction and data quality assessment to avoid selecting highly mobile clients or clients with low-quality data, thereby significantly improving model convergence efficiency. In addition, we design a GRU-VAE prediction model that uses a Variational Autoencoder (VAE) to capture latent data distribution features and Gated Recurrent Units (GRUs) to model temporal dependencies, thereby substantially enhancing the accuracy of content request prediction. The predicted content popularities are then fed into a deep reinforcement learning-driven caching decision engine to dynamically optimize edge caching resource allocation. Extensive experiments demonstrate that DAPR achieves superior performance in terms of average reward, cache hit ratio, and transmission latency, thereby effectively improving the overall efficiency of vehicular edge caching systems.

Blockchain technology has driven the development of Decentralized Applications (DApps) in areas such as decentralized finance. However, as application scenarios become more complex, the limitations of computational resources and costs gradually lead to insufficient performance. Large Language Models (LLMs), as a promising technology, have the potential to enhance blockchain’s capabilities in complex task governance. However, due to factors such as consensus mechanisms, it is challenging to directly integrate them with blockchain. To address this issue, this article proposes and implements a general framework for integrating LLMs with blockchain data, C-LLM, which successfully overcomes interoperability barriers between the two. By combining semantic relevance evaluation and truth discovery techniques, this article presents an innovative data aggregation method, SenteTruth, which effectively improves the correctness and credibility of data generated by LLMs. To validate the framework’s effectiveness, we construct a dataset containing three types of questions, covering Q&A records between 10 oracle nodes and 5 LLM models. Experimental results show that, in the presence of 40% malicious nodes, the proposed method improves data correctness by an average of 17.74% compared with the optimal baseline. This research not only provides an innovative solution for the intelligent application of smart contracts but also demonstrates the potential for deep integration of LLMs and blockchain, driving the development of smarter and more complex application scenarios for smart contracts.

In recent years, blockchain oracle, as the key link between blockchain and real-world data interaction, has greatly expanded the application scope of blockchain. In particular, the emergence of the Multi-Data Source (MDS) oracle has greatly improved the reliability of the oracle in the case of untrustworthy data sources. However, the current MDS oracle scheme requires nodes to obtain data redundantly from multiple data sources to guarantee data reliability, which greatly increases the resource overhead and response time of the system. Therefore, in this paper, we propose a Secure and Efficient Multi-data Source Oracle framework (SEMSO), where nodes only need to access one data source to ensure the reliability of final data. First, we design a new off-chain data aggregation protocol TBLS, to guarantee data source diversity and reliability at low cost. Second, according to the rational man assumption, the data source selection task of nodes is modeled and solved based on the Bayesian game under incomplete information to maximize the node's revenue while improving the success rate of TBLS aggregation and system response speed. Security analysis verifies the reliability of the proposed scheme, and experiments show that under the same environmental assumptions, SEMSO takes into account data diversity while reducing the response time by 23.5%.

Blockchain technology ensures secure and trust-worthy data flow between multiple participants on the chain, but interoperability of on-chain and off-chain data has always been a difficult problem that needs to be solved. To solve the problem that blockchain systems cannot access off-chain data, oracle is introduced. However, existing research mainly focuses on the consistency and integrity of data, but ignores the problem that oracle nodes may be externally attacked or provide false data for selfish motives, resulting in the unresolved problem of data accuracy. In this paper, we introduce a new Decentralized Testing architecture (DecTest) that aims to improve data accuracy. A blockchain oracle random secret testing mechanism is first proposed to enhance the monitoring and verification of nodes by introducing a dynamic anonymized question-verification committee. Based on this, a comprehensive evaluation incentive mechanism is designed to incentivize honest work performance by evaluating nodes based on their reputation scores. The simulation results show that we successfully reduced the discrete entropy value of the acquired data and the real value of the data by 61.4 %.

The Practical Byzantine Fault Tolerance (PBFT) protocol-supported blockchain can provide decentralized security and trust mechanisms for the Internet of Things (IoT). However, the PBFT protocol is not specifically designed for IoT applications. Consequently, adapting PBFT to the dynamic changes of an IoT environment with incomplete information represents a challenge that urgently needs to be addressed. To this end, we introduce DA-PBFT, a PBFT dynamic adaptive framework based on a multi-agent architecture. DAPBFT divides the dynamic adaptive process into two sub-processes: optimality-seeking and optimization decision-making. During the optimality-seeking process, a PBFT optimization model is constructed based on deep reinforcement learning. This model is designed to generate PBFT optimization strategies for consensus nodes. In the optimization decision-making process, a PBFT optimization decision consensus mechanism is constructed based on the Borda count method. This mechanism ensures consistency in PBFT optimization decisions within an environment characterized by incomplete information. Furthermore, we designed a dynamic adaptive incentive mechanism to explore the Nash equilibrium conditions and security aspects of DA-PBFT. The experimental results demonstrate that DA-PBFT is capable of achieving consistency in PBFT optimization decisions within an environment of incomplete information, thereby offering robust and efficient transaction throughput for IoT applications.

Foretelling ongoing malware attacks in real time is challenging due to the stealthy and polymorphic nature of their executive behavior patterns. In this paper, we present MalAF, a novelMalwareAttackForetelling framework that utilizes run-time behavior (i.e., sequences of API events) of malware to foretell the attack that has not yet executed. MalAF first samples suspicious API events by assessing the sensitivity of the parameters of each API event and dividing them into multiple attack time slots by calculating the strong correlation. Following that, MalAF employs dynamic heterogeneous graph sequences to incrementally model contextual semantics for each attack time slot, generating malware state sequences in real time. Moreover, MalAF proposes a greedy adaptive dictionary (GAD)-optimized IRL preference learning method to automate the capture of families' intrinsic attack preferences, which achieves higher performance than the existing inverse reinforcement learning (IRL). Additionally, with the guidance of families' attack preferences, MalAF trains an LSTM to foretell the future path of the target malware. Finally, MalAF matches the identified APIs' paths with a malicious capability base and reports the comprehensible attacks to an analyst. The experiments on real-world datasets demonstrate that our proposed MalAF outperforms the state-of-the-art methods, which improves the baseline by 3.01%$\sim$4.73% of accuracy in terms of path foretell.

Malware attacks, especially “few-shot” malware, have profoundly harmed the cyber ecosystem. Recently, malware detection models based on graph neural networks have achieved remarkable success. However, these efforts over-rely on sufficient labeled data for model training and thus may be brittle in few-shot malware detection because of the label scarcity. To this end, we propose a self-supervised malware detection framework based on graph contrastive learning and adversarial augmentation, termed A2-CLM, to address the challenge of few-shot malware detection. Particularly, A2-CLM first depicts the malware execution context with a sensitivity heterogeneous graph by assessing the security semantic of each behavior. Afterwards, A2-CLM designs multiple adversarial attacks to generate more practical contrastive pairs, including the PGD attack, attribute masking attack, meta-graph-guide sampling attack, direct system calls attack, and obfuscation attack, which is beneficial to strengthening the model’s effectiveness and robustness. To alleviate the training workload of contrastive learning, we introduce a momentum strategy to train the multiple graph encoders in A2-CLM. Especially on 1-shot detection tasks, A2-CLM achieves performance gains of up to 24.63% and 4.58% against supervised and self-supervised detection methods, respectively.