Recently, the cyberattack surface has expanded significantly due to the proliferation of Internet of Things (IoT) devices, and intrusion detection remains a vital component of modern network security. Traditional Intrusion Detection Systems (IDSs) often cannot detect complex and newly generated threats due to the heterogeneous characteristics and constrained computing capabilities of IoT devices. In this paper, we utilise ensemble learning techniques, specifically Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM), in combination with a Feed-Forward Neural Network (FFNN) to develop a hybrid IDS. To address class imbalance and complex feature interactions that can be managed by Gradient Boosting (GB) techniques, the proposed model combines the robustness of gradient boosting with that of deep learning in learning complex nonlinear patterns in network traffic. The experimental results show the hybrid model achieves strong performance, such as an accuracy of 99.42%, precision of 98.43%, recall of 99.65%, F1-score of 99.04%, and Area Under the receiver operating characteristic Curve (AUC) of 0.9994. These findings indicate the effectiveness of the proposed hybrid IDS in securing the IoT from diverse online threats.
The automation of penetration testing has long been constrained by the semantic-operational divide between human expert cognition and machine-executable workflows. We present COAPT, a novel architecture that bridges this gap through large language model (LLM) driven cognitive planning, enabling autonomous execution of full-cycle penetration testing aligned with the penetration testing execution standard (PTES) and MITRE adversarial tactics, techniques, and common knowledge (ATT&CK) framework. COAPT introduces three key innovations: (1) A cognitive planning architecture combining retrieval-augmented generation with chain-of-thought reasoning, grounding decisions in verified cybersecurity knowledge; (2) Formal semantic interfaces that translate strategic intent into executable commands for security tools through machine-actionable contracts; (3) Hierarchical rnulti-agent coordination that maintains tactical consistency across reconnaissance, exploitation, privilege escalation, and defense recommendation phases. Evaluated across diverse penetration testing scenarios, COAPT demonstrates superior performance over state-of-the-art tools in vulnerability exploitation success rates and operational efficiency, while significantly reducing LLM hallucination risks through its knowledge-grounded reasoning approach. The architecture's ability to autonomously chain multi-stage attacks and generate ATT&CK-mapped defense strategies establishes a new paradigm for cybersecurity automation that preserves human expert workflows at machine execution scales.
Deep learning offers notable promise for computational pathology, but its performance is constrained by the need for extensively annotated datasets, which are costly and laborious to produce. Self-Supervised Learning (SSL) provides an effective paradigm for learning discriminative representations from unannotated pathological images. However, existing SSL methods often overlook domain-specific characteristics of pathological images and suffer from the adverse effects of low-quality negative samples, leading to sub-optimal feature representations for downstream tasks. To overcome these limitations, we propose a novel Domain-Specific Self-supervised Contrastive Learning (DSSCL) framework, which incorporates two novel components: (1) a Stain-Separation Based Data Augmentation (SSDA) module that enhances stain-aware representation learning by fusing stain-separated components with original hematoxylin and eosin images, and (2) a Contrast-Aware Pair Refinement (CAPR) module that improves feature discriminability by filtering potential positives and mining hard negatives, thereby mitigating the influence of low-quality negatives. Extensive experiments demonstrate that DSSCL achieves comparable accuracy in classification tasks using only 0.1% labeled data compared to a network fine-tuned from ImageNet with 10% labeled data, while also delivering competitive performance in detection and segmentation tasks, underscoring its effectiveness in learning transferable and robust feature representations across diverse downstream tasks. The code is available at https://github.com/junjianli106/DSSCL.
Artificial Intelligence of Things (AIoT)-enabled Edge Collaboration systems (AEC) are characterized by openness, heterogeneity, and constrained device resources, which pose severe challenges to achieving trusted collaboration within AEC. While blockchain provides a trusted foundation for multi-party coordination, establishing robust inter-device trust relationships at low cost and promoting the collaborative participation of heterogeneous devices remain critical challenges that need to be addressed. To address the aforementioned challenges, this paper introduces DPRP, a lightweight and scalable multilayer blockchain model based on delegated proof of swarm reputation with hierarchical Practical Byzantine Fault Tolerance (PBFT). The model introduces a lightweight consensus protocol, DPoRP, which constructs swarm reputation by evaluating node contributions within collaborative swarms, thereby establishing a trust foundation. This enables a swarm-reputation-driven multilayer consensus mechanism, structured around meta-nodes functioning as autonomous units. To effectively incentivize swarm collaboration and constrain node behavior, DPoRP incorporates a hierarchical reward and penalty scheme tailored to swarm collaboration. Moreover, to reduce the participation threshold for heterogeneous devices, DPRP designs a dynamic, redundancy-aware hierarchical storage mechanism that adjusts redundancy factors based on actual storage demands, thus improving storage efficiency and supporting multilayer deployment. Simulation results and analysis demonstrate that DPRP significantly enhances system robustness and scalability while reducing communication complexity.
The objective of multidimensional graph fraud detection is to identify fraudulent entities within a graph. Graph Neural Network (GNN) models leverage graph structures to propagate messages from neighbors to target nodes, thereby obtaining precise representations of the target nodes. However, anomaly detection methods based on GNNs face challenges, such as structural inconsistency and over-smoothing, which reduce the suspicion scores of fraudulent nodes and hinder the effectiveness of anomaly detection models. To address these issues, we propose an end-to-end anomaly node detection model named Feature Decomposition Graph Neural Network (FD-GNN). In FD-GNN, a feature amplification module is first employed to enhance the differences between node representations. Then, a feature decomposition module is used to distinguish the natural attributes of nodes from their neighborhood attributes, with constraints applied to both types of attributes. Finally, a representation aggregation module utilizes differential aggregation operations to further distinguish normal nodes from anomaly ones. Experiments conducted on two real-world datasets, Amazon and YelpChi, demonstrate that FD-GNN achieves Area Under Curve (AUC) scores of 94.45% and 91.62%, respectively, outperforming existing multidimensional graph anomaly detection models.
