Phishing poses a significant threat to the financial and privacy security of internet users and often serves as the starting point for cyberattacks. Many machine-learning-based methods for detecting phishing websites rely on URL analysis, offering simplicity and efficiency. However, these approaches are not always effective due to the following reasons: (1) highly concealed phishing websites may employ tactics such as masquerading URL addresses to deceive machine learning models, and (2) phishing attackers frequently change their phishing website URLs to evade detection. In this study, we propose a robust, multi-view Transformer model with an expert-mixture mechanism for accurate phishing website detection utilizing website URLs, attributes, content, and behavioral information. Specifically, we first adapted a pretrained language model for URL representation learning by applying adversarial post-training learning in order to extract semantic information from URLs. Next, we captured the attribute, content, and behavioral features of the websites and encoded them as vectors, which, alongside the URL embeddings, constitute the website’s multi-view information. Subsequently, we introduced a mixture-of-experts mechanism into the Transformer network to learn knowledge from different views and adaptively fuse information from various views. The proposed method outperforms state-of-the-art approaches in evaluations of real phishing websites, demonstrating greater performance with less label dependency. Furthermore, we show the superior robustness and enhanced adaptability of the proposed method to unseen samples and data drift in more challenging experimental settings.

Deep Neural Networks (DNNs) have made significant progress in addressing the Non-Intrusive Load Monitoring (NILM) task, which aims to disaggregate appliance-level power signals from aggregated meter readings. Despite these advancements, existing DNN-based NILM approaches rely on training with power signals from individual appliances, which are obtained intrusively through sensor installations. This method is not only expensive but also poses a risk of damaging the original circuits. To overcome these limitations, we introduce the State-based Supervised NILM (SS-NILM) problem. Instead of using appliance power signal labels, we leverage on-off state information, which can be collected in a non-intrusive manner. However, solving SS-NILM presents a challenge, as it requires developing a model that maps on-off state labels to the corresponding appliance power signals in an indirectly supervised setting. In this work, we propose a state-based DNN that predicts the power signals of multiple target appliances simultaneously. The model is trained by minimizing the discrepancy between the aggregated prediction and the true aggregated power signal. Additionally, the model predicts the on-off states of appliances, which are used as auxiliary information to improve the accuracy of power signal predictions. Extensive experiments conducted on real-world datasets demonstrate that our model, trained using non-intrusive on-off state information, achieves performance comparable to that of traditional NILM models.

The increasing proliferation of GPS-enabled mobile devices, including Unmanned Aerial Vehicles (UAVs), smartphones, and laptops, has resulted in a significant upsurge in the outsourcing of spatial data to cloud servers for storage and computation purposes, such as task allocation and location-based services. However, the reliance on untrusted cloud servers introduces the risk of privacy breaches, as these servers possess the ability to deduce and access users’ private information based on task content and query requirements. Existing privacy-preserving task-allocation schemes offer only coarse-grained and non-temporal access control, which restricts their applicability in scenarios involving multiple users and time-series data, such as trajectory and time-related routes. To overcome these challenges, this paper proposes an Efficient and Privacy-Preserving Task Allocation with Temporal Access Control (EPTA-T) scheme for mobile crowdsensing. By leveraging the techniques of Gray code and randomizable matrix multiplication, EPTA-T achieves efficient and privacy-preserving task allocation in mobile crowdsensing. Specifically, EPTA-T supports fine-grained and temporal access control through the utilization of an attribute-based access tree and function integration. The formal security analysis demonstrated that EPTA-T effectively guarantees data privacy and query privacy throughout the task allocation process. Extensive experiments conducted using a real-world dataset indicated that the EPTA-T scheme surpassed the performance of the state-of-the-art scheme.

Community detection has been a subject of extensive research due to its broad applications across social media, computer science, biology, and complex systems. Modularity stands out as a predominant metric guiding community detection, with numerous algorithms aimed at maximizing modularity. However, modularity encounters a resolution limit problem when identifying small community structures. To tackle this challenge, this paper presents a novel approach by defining community structure information from the perspective of encoding edge information. This pioneering definition lays the foundation for the proposed fast community detection algorithm CSIM, boasting an average time complexity of only $O\left(n\text{log}n\right)$. Experimental results showcase that communities identified via the CSIM algorithm across various graph data types closely resemble ground truth community structures compared to those revealed via modularity-based algorithms. Furthermore, CSIM not only boasts lower time complexity than greedy algorithms optimizing community structure information but also achieves superior optimization results. Notably, in cyclic network graphs, CSIM surpasses modularity-based algorithms in effectively addressing the resolution limit problem.

Malicious URL attacks are becoming more domain-specific, for example, phishing tactics varying greatly between sectors, such as education and e-commerce. Therefore, cross-domain collaboration and knowledge sharing are essential for developing effective solutions to combat these threats. However, such collaboration is often hindered by privacy concerns and business sensitivities. Federated learning addresses these challenges by enabling multi-client collaboration without direct data exchange. Unfortunately, if highly expressive Transformer models are used, clients may face intolerable computational burdens, and the exchange of weights could quickly deplete network bandwidth. In this paper, we propose Fed-urlBERT, a federated learning method for malicious URL detection using pretrained Transformers. It leverages split learning to partition the Transformer encoder into client-side embeddings and server-side encoding. In this setup, only generate initial embeddings from local data, while the server handles the computationally intensive encoding. Our approach addresses privacy concerns and cross-domain collaboration needs in malicious URL defense while minimizing the client computational burden. We employ two aggregation strategies, FedAvg and FedALA, with the latter designed to mitigate client heterogeneity by integrating both local and global model information during each training round. We evaluate our approach across three distinct data distribution scenarios, including Independent and Identically Distributed (IID data) and two types of Non-IID data settings. The experimental results demonstrate that our method consistently achieves performance comparable to that of centralized models. Significantly, under balanced data conditions, our federated model also demonstrates a notable reduction in the False Positive Rate (FPR), highlighting a potential regularization benefit of the federated approach. The source code is accessible at https://github.com/Davidup1/FedURLBERT.