The increasing number of cyberattacks has made the cybersecurity situation more serious. Thus, it is urgent to use cyber threat intelligence to deal with the complex and changing cyber environment. However, cyber threat intelligence usually exists in an unstructured form, and a huge amount of data poses a great challenge to security analysts. To this end, this paper proposes a novel threat intelligence information extraction system combining multiple models, which contains four key steps: entity extraction, coreference resolution, relation extraction, and knowledge graph construction. In the entity extraction task, a multihead self-attention mechanism is adopted to extract the dependency relationships between words. In the coreference resolution task, contextual information and mention embedding are fused to improve the mention representation. Meanwhile, features of different dimensions are extracted using a convolutional neural network. In the relation extraction task, additional features such as part of speech, mention width, entity type, and distance of entity pairs are incorporated to improve the embedding representation. Finally, a knowledge graph is constructed to explicitly present entities and their relationships. Experimental results indicate that compared with the baseline model, the F1 score of our model is improved by at least 8.87, 9.82, and 10.56 on entity extraction, coreference resolution, and relation extraction, respectively. The knowledge graph in Neo4j demonstrates the effectiveness of our system.

Federated learning (FL), a promising paradigm for distributed machine learning, facilitates collaborative model training while preserving data locally for enhanced privacy. Nevertheless, its widespread application in edge computing scenarios is increasingly challenged by poisoning attacks, wherein malicious adversaries compromise model convergence through contamination of local datasets or model parameters. Currently, existing Byzantine-robust FL methods predominantly rely on idealized assumption of independent and identically distributed (IID) data or necessitate access to raw gradients—introducing significant privacy risks. To overcome these limitations, we propose privacy-preserving FL method against poisoning attacks on non-IID data (PFLAP). Inspired by community detection principles, we adaptively partition edge nodes with non-IID data into clusters exhibiting similar data distributions. Building upon this foundation, we develop a novel poisoning defense scheme that integrates scaled dot-product attention with linear homomorphic encryption, enabling detection of encrypted poisoning gradients. Security analysis proves that PFLAP provides rigorous privacy preservation, and extensive experiments demonstrate that compared with existing defense schemes, PFLAP improves accuracy by 8.57%–34.42% under highly non-IID settings, exhibits stronger resistance against both targeted and untargeted attacks, and reduces computational overhead by 6.1%–10.2% and communication overhead by 11.4%–17.4%.

Relation extraction in the threat intelligence domain plays an important role in mining the internal association between crucial threat elements and constructing a knowledge graph (KG). This study designed a novel document-level relation extraction model, FEDRE-KD, integrating additional features to take full advantage of the information in documents. The study also introduced a teacher–student model, realizing knowledge distillation, to further improve performance. Additionally, a threat intelligence ontology was constructed to standardize the entities and their relationships. To solve the problem of lack of publicly available datasets for threat intelligence, manual annotation was carried out on the documents collected from social blogs, vendor bulletins, and hacking forums. After training the model, we constructed a threat intelligence knowledge graph in Neo4j. Experimental results indicate the effectiveness of additional features and knowledge distillation. Compared to mainstream models SSAN, GAIN, and ATLOP, FEDRE-KD improved the F1score by 22.07, 20.06, and 22.38, respectively.

Federated Learning (FL) has been widely used in Internet of Things (IoT) environments as a promising decentralized framework capable of collaborative model training without exposing local data. Despite its advantages, FL still encounters significant security challenges. In particular, semi-honest servers can potentially infer private information from the gradients shared by clients. Additionally, FL’s distributed nature opens up vulnerabilities to adversarial behavior, where malicious clients may submit manipulated gradients to degrade the global model's accuracy or hinder its convergence. Addressing privacy and robustness simultaneously is an enormous challenge, as most privacy-preserving approaches focus on securing gradients through encryption or noise injection, which obstructs the identification of malicious clients—an essential step in poisoning defense. To resolve this conflict, this work introduces AP-PPFL, a federated learning framework that integrates both privacy protection and poisoning defense. The proposed approach incorporates a voting-based parameter importance evaluation strategy and a cosine similarity-based mechanism to filter out harmful gradients. Furthermore, it leverages Paillier homomorphic encryption within a dual-server setup to maintain gradient confidentiality while enabling secure computation directly over encrypted data. Compared with conventional methods, AP-PPFL achieves a balanced improvement in both privacy-preserving and attack resilience, with comprehensive security analysis provided.

With the continuous advancement of Internet of Things (IoT), the issue of data privacy in it is also quietly emerging. Federated learning (FL) enables the creation of a potent global model from a consortium of clients, safeguarding sensitive client data while upholding model accuracy. Unlike conventional centralized learning paradigms, federated learning operates without necessitating access to local datasets, thus effectively mitigating data privacy concerns. Nonetheless, malevolent entities can exploit vulnerabilities by introducing subtle perturbations to client-side samples, thereby executing adversarial example (AE) attacks that disrupt model predictions. To address this challenge, we present a novel bijection-backdoor-based adversarial examples defense method in federated learning, termed BAEDFL-CL. Initially, an intricate backdoor mechanism is transmitted from the server to the client, i.e. IoT device, neutralizing the impact of adversarial examples on model outputs while preserving the primary task's performance. Additionally, to bolster the model's defense capabilities within the federated learning for practical scenario, we devise a representation enhancement technique grounded in supervised contrastive learning (CL). This method encourages the model to craft feature representations endowed with enhanced generalization ability. Through comprehensive experiment on diverse datasets spanning IID and Non-IID scenarios, our results reveal that BAEDFL-CL significantly diminishes the attack success rate to 17.66% and 24.35%, respectively. Concurrently, BAEDFL-CL improves main task performance by 0.02% and 2.24% correspondingly, substantiating its efficacy in countering adversarial examples in federated learning environments.

Federated Learning (FL) has been widely used in Internet of Things (IoT) environments as a promising decentralized framework capable of collaborative model training without exposing local data. Despite its advantages, FL still encounters significant security challenges. In particular, semi-honest servers can potentially infer private information from the gradients shared by clients. Additionally, FL’s distributed nature opens up vulnerabilities to adversarial behavior, where malicious clients may submit manipulated gradients to degrade the global model’s accuracy or hinder its convergence. Addressing privacy and robustness simultaneously is an enormous challenge, as most privacy-preserving approaches focus on securing gradients through encryption or noise injection, which obstructs the identification of malicious clients–an essential step in poisoning defense. To resolve this conflict, this work introduces AP-PPFL, a federated learning framework that integrates both privacy protection and poisoning defense. The proposed approach incorporates a voting-based parameter importance evaluation strategy and a cosine similarity-based mechanism to filter out harmful gradients. Furthermore, it leverages Paillier homomorphic encryption within a dual-server setup to maintain gradient confidentiality while enabling secure computation directly over encrypted data. Compared with conventional methods, AP-PPFL achieves a balanced improvement in both privacy-preserving and attack resilience, with comprehensive security analysis provided.