Website Fingerprinting (WF) attacks aim to infer the websites visited by Tor users by analyzing patterns in encrypted network traffic. However, most existing WF attacks are evaluated on traffic collected in controlled environments with fixed configurations, failing to reflect the complexity and variability of real-world conditions. In practice, traffic is far more dynamic and diverse due to heterogeneous network conditions, the large number of subpages within individual websites, and continuous evolution of website content. These factors increase intra-class variability and induce temporal feature drift, which ultimately degrades the long-term effectiveness of existing attacks. In this paper, we propose TraVerse, an LLM-based representation learning framework designed to achieve robust WF attacks under real-world conditions. TraVerse applies architectural adaptation and large-scale fine-tuning on diverse unlabeled traffic to learn generalizable and resilient representations that remain effective in dynamic and evolving environments. Furthermore, TraVerse integrates a lightweight classifier atop the LLM-derived representations, enabling accurate website identification and efficient few-shot adaptation with minimal model updates. We prototype TraVerse and conduct comprehensive evaluations using real-user traffic. Experimental results show that TraVerse improves Accuracy@3 by an average of 176.3% and weighted F1 by 343.3% over state-of-the-art baselines, while maintaining strong performance throughout a three-month longitudinal evaluation.

Session-based Social Recommendation (SSR) enhances item recommendations by incorporating both session interactions and social network data. Despite recent progress, existing SSR methods-primarily based on Graph Neural Networks-are highly susceptible to session noise (irrelevant or unintentional interactions) and social noise (misleading signals from connected users). Prior denoising strategies often rely on heuristic resampling or reweighting techniques, which lack generalizability and robustness across diverse datasets. In this work, we explore a novel direction by introducing diffusion models for denoising in SSR. However, applying diffusion to SSR presents unique challenges due to heterogeneous data modalities, incompatible noise patterns, and the absence of semantic guidance during the reverse process. To overcome these challenges, we propose D3MRec, a Dual Denoising Diffusion Model specifically designed for SSR. D3MRec employs a dual-branch architecture that independently models session sequences and social graphs, applying denoising diffusion in their respective hidden representation spaces. This decoupled design preserves the structural integrity of each modality while enabling modality-specific denoising. Moreover, we introduce cross-modal guidance by leveraging collaborative signals from the other branch during the reverse diffusion process, enhancing alignment between session intents and social preferences. The dual denoising processes not only mitigate noise within each modality but also serve as mutual priors, facilitating robust and consistent representation learning across modalities. Extensive experiments on multiple benchmarks show that D3MRec significantly outperforms state-of-the-art models, particularly under noisy conditions, demonstrating its effectiveness and robustness.

Traffic encryption has been widely adopted to protect the confidentiality and integrity of Internet traffic. However, attackers can also abuse such mechanism to deliver malicious traffic. Particularly, existing methods detecting encrypted malicious traffic are not robust against evasion attacks that manipulate traffic to obfuscate traffic features. Robust detection against evasion attacks remains an open problem. To the end, we develop Wedjat, which utilizes a causal network to model benign packet interactions among relevant flows, such that it recognizes abnormal causality that represents malicious traffic and disrupted causality incurred by evasion attacks. We extensively evaluate Wedjat with millions of flows collected from a real-world enterprise. The experimental results demonstrate that Wedjat achieves an accuracy of 0.957 F1-score when detecting various advanced attacks. Notably, five sophisticated evasion attacks, which have successfully evaded all existing methods, are accurately detected by Wedjat with over 0.915 F1. It demonstrates that Wedjat achieves exceptional robustness against evasions. Meanwhile, Wed- jat maintains an outstanding detection latency, i.e., it can predict each packet in less than 0.125 seconds.

In the era of deep learning, it is critical to protect the intellectual property of high-performance deep neural network (DNN) models. Existing proposals, however, are subject to adversarial ownership forgery (e.g., methods based on watermarks or fingerprints) or require full access to the original training dataset for ownership verification (e.g., methods requiring the replay of the learning process). In this paper, we propose a novel Provenance of Training (PoT) scheme, the first empirical study towards verifying DNN model ownership without accessing any original dataset while being robust against existing attacks. At its core, PoT relies on a coherent model chain built from the intermediate checkpoints saved during model training to serve as the ownership certificate. Through an in-depth analysis of model training, we propose six key properties that a legitimate model chain shall naturally hold. In contrast, it is difficult for the adversary to forge a model chain that satisfies these properties simultaneously without performing actual training. We systematically analyze PoT’s robustness against various possible attacks, including the adaptive attacks that are designed given the full knowledge of PoT’s design, and further perform extensive empirical experiments to demonstrate our security analysis.

Online developer communities like GitHub allow a massive number of developers to collaborate. However, the openness of the communities makes them vulnerable to different types of malicious attacks, since attackers can easily join these communities and interact with legitimate users. In this work, we propose GitSec, a deep learning-based solution for detecting malicious accounts in online developer communities. GitSec distinguishes malicious accounts from legitimate ones based on the account profiles, dynamic activity characteristics, as well as social interactions. First, GitSec introduces two user activity sequences and applies a parallel neural network design with an attention mechanism to process the sequences. Second, GitSec constructs two graphs to represent the interactions between users according to their repository operations. Especially, graph neural networks and structural hole theory are employed to deal with the two constructed graphs. Third, GitSec makes use of the descriptive features to enhance the detection performance. The final judgement is made by a decision maker implemented by a supervised machine learning-based classifier. Based on the real-world data of GitHub users, our comprehensive evaluations show that GitSec achieves a better performance than state-of-the-art solutions, with an AUC value of 0.916.

Abnormal data access operations in database systems always hap-pen, which are typically incurred by misoperations or attacks, though these systems are enforced with strict access control policies. However, prior arts only focus on detecting abnormal data accesses by utilizing known attack patterns or identifying behaviors significantly deviated from normal behaviors. They cannot capture stealthy abnormal data access operations that are similar to normal ones. In this paper, we propose a novel unsupervised anomaly detection system UCAD, which aims to detect abnormal data access operations, by comparing operation's semantics with their contextual intent. However, it is non-trivial to obtain accurate semantics of operations for intent analysis because (i) the same operation may exhibit diverse semantics under different operation contexts and (ii) different operation sequences could have identical semantics due to heterogeneous user access patterns. To address this issue, we develop a new transformer model called Trans-DAS for UCAD. Trans-DAS learns the semantics of individual operations by utilizing the attention mechanism that analyzes the relevance between any pair of operations in sequence, and captures the contextual intent of operations inferred from the contexts. Specifically, Trans-DAS utilizes a particular embedding layer to embed the semantics of individual operations without the operation order information and a masking mechanism that allows Trans-DAS to learn the semantics according to the bidirectional contexts. Also, we define a new training objective for Trans-DAS to enlarge the difference among the embedded semantics. Furthermore, in order to effectively utilize Trans-DAS for detection, we develop two modules in UCAD, i.e., a data preprocessing module that allows Trans-DAS to accurately learn the normal semantic information by removing noisy data, and an anomaly detection module that learns the semantic information for intent comparison. We evaluate the performance of UCAD on real-world data traces under different settings (e.g., varied parameters and hybrid datasets). The results demonstrate that UCAD achieves the average F1-score of 0.94 in two scenarios, which significantly outperform baselines, and shows robustness to hybrid data and good transferability to different tasks.

Spatial crowdsourcing (SC) allows requesters to crowdsource tasks to workers based on location proximity. To preserve privacy, the location should not be disclosed to untrustworthy entities (even the SC platform). Previous solutions to preserve workers’ location privacy require an online trusty third party (TTP), which is not practical in reality. In this paper, we design a framework that allows the SC platform to assign tasks to nearest workers in an online manner without knowing their actual locations. We propose an encryption algorithm to encrypt the locations of tasks and workers, and design an indexing method that assigns tasks to workers without losing too much privacy. We prove that there exists a trade-off between efficiency and security theoretically, which can be controlled based on user preference. We verify our method on real-world datasets and experimental results show that our method is efficient, effective and practical.

Online social networks (OSNs) are plagued by fake accounts. Existing fake account detection methods either require a manually labeled training set, which is time-consuming and costly, or rely on rich information of OSN accounts, e.g., content and behaviors, which incurs significant delay in detecting fake accounts. In this work, we propose UFA (Unveiling Fake Accounts) to detect fake accounts immediately after they are registered in an unsupervised fashion. First, through a measurement study on the registration patterns on a real-world registration dataset, we observe that fake accounts tend to cluster on outlier registration patterns, e.g., IP and phone numbers. Then, we design an unsupervised learning algorithm to learn weights for all registration accounts and their features that reveal outlier registration patterns. Next, we construct a registration graph to capture the correlation between registration accounts, and utilize a community detection method to detect fake accounts via analyzing the registration graph structure. We evaluate UFA using real-world WeChat datasets. Our results demonstrate that UFA achieves a precision 94% with a recall ~80%, while a supervised variant requires 600K manual labels to obtain the comparable performance. Moreover, UFA has been deployed by WeChat to detect fake accounts for more than one year. UFA detects 500K fake accounts per day with a precision ~93% on average, via manual verification by the WeChat security team.

Online developer communities like GitHub provide services such as distributed version control and task management, which allow a massive number of developers to collaborate online. However, the openness of the communities makes themselves vulnerable to different types of malicious attacks, since the attackers can easily join and interact with legitimate users. In this work, we formulate the malicious account detection problem in online developer communities, and propose GitSec, a deep learning-based solution to detect malicious accounts. GitSec distinguishes malicious accounts from legitimate ones based on the account profiles as well as dynamic activity characteristics. On one hand, GitSec makes use of users' descriptive features from the profiles. On the other hand, GitSec processes users' dynamic behavioral data by constructing two user activity sequences and applying a parallel neural network design to deal with each of them, respectively. An attention mechanism is used to integrate the information generated by the parallel neural networks. The final judgement is made by a decision maker implemented by a supervised machine learning-based classifier. Based on the real-world data of GitHub users, our extensive evaluations show that GitSec is an accurate detection system, with an F1-score of 0.922 and an AUC value of 0.940.

Data outsourcing to cloud has been a common IT practice nowadays due to its significant benefits. Meanwhile, security and privacy concerns are critical obstacles to hinder the further adoption of cloud. Although data encryption can mitigate the problem, it reduces the functionality of query processing, e.g., disabling SQL queries. Several schemes have been proposed to enable one-dimensional query on encrypted data, but multi-dimensional range query has not been well addressed. In this paper, we propose a secure and scalable scheme that can support multi-dimensional range queries over encrypted data. The proposed scheme has three salient features: (1) Privacy: the server cannot learn the contents of queries and data records during query processing. (2) Efficiency: we utilize hierarchical cubes to encode multi-dimensional data records and construct a secure tree index on top of such encoding to achieve sublinear query time. (3) Verifiability: our scheme allows users to verify the correctness and completeness of the query results to address server's malicious behaviors. We perform formal security analysis and comprehensive experimental evaluations. The results on real datasets demonstrate that our scheme achieves practical performance while guaranteeing data privacy and result integrity.

Malicious accounts are one of the biggest threats to the security and privacy of online social networks (OSNs). In this work, we study a new type of OSN, called privacy-centric mobile social network (PC-MSN), such as KakaoTalk and LINE, which has attracted billions of users recently. The design of PC-MSN is inspired to protect their users' privacy from strangers: (1) a stranger is not easy to send a friend request to a user who does not want to make friends with strangers; and (2) strangers cannot view a user's post. Such a design mitigates the security issue of malicious accounts. At the same time, it also brings the battleground between attackers and defenders to an earlier stage, i.e., making friendship, than the one studied in previous works. Also, previous defense proposals mostly rely on certain assumptions on the attacker, which may not be robust in the new PC-MSNs. As a result, previous malicious accounts detection approaches are less effective on a PC-MSN.

Crowdsourcing platforms have been widely deployed to solve many computer-hard problems, e.g., image recognition and entity resolution. Quality control is an important issue in crowdsourcing, which has been extensively addressed by existing quality-control algorithms, e.g., voting-based algorithms and probabilistic graphical models. However, these algorithms cannot ensure quality under sybil attacks, which leverages a large number of sybil accounts to generate results for dominating answers of normal workers. To address this problem, we propose a sybil defense framework for crowdsourcing, which can help crowdsourcing platforms to identify sybil workers and defense the sybil attack. We develop a similarity function to quantify worker similarity. Based on worker similarity, we cluster workers into different groups such that we can utilize a small number of golden questions to accurately identify the sybil groups. We also devise online algorithms to instantly detect sybil workers to throttle the attacks. Our method also has ability to detect multi-attackers in one task. To the best of our knowledge, this is the first framework for sybil defense in crowdsourcing. Experimental results on real-world datasets demonstrate that our method can effectively identify and throttle sybil workers.