Recent hardware advances have led to the development and consumerization of mobile devices, which mainly include smartphones and various wearable devices. To protect the privacy of users, various user authentication mechanisms have been proposed. In particular, biometrics has been widely used for multi-factor authentication. However, biometrics-based authentication mechanisms usually require costly sensors deployed on devices, and rely on explicit user input and Internet connection for performing user authentication. In this article, we propose a system, called RiskCog, which can authenticate the ownership of mobile devices unobtrusively and in a real time manner by adopting a learning-based approach. Unlike previous studies on user authentication, for cross-platform deployment, maximum user privacy protection, and unobtrusive authentication, RiskCog only relies on those widely available and privacy-insensitive motion sensors to capture the data related to the users' daily device usage. It requires no users' explicit input and has no requirement on the users' motion state or the device placement. RiskCog is also usable in the environment without Internet access by performing offline user identity verification. We conduct comprehensive experiments on smartphones and smartwatches, which show that RiskCog can authenticate device users rapidly and with high accuracy.

The website fingerprinting (WF) attack enables a local eavesdropper to identify which website a client is visiting under encrypted network connections. By leveraging deep neural networks, the state-of-the-art WF attacks achieve high accuracy in classic experimental scenes. However, due to the high variance of neural networks, those attacks are sensitive to the specific information in the data and would result in less-than-ideal performance on data outside the training set or on data that is impacted by the effect of concept drift. In this paper, we present snWF, a novelWF attack, which leverages an out-of-the ordinary ensemble to reduce the variance of neural networks and improve the robustness of the attack. In a large open-world setting with 400,000 websites, snWF manages to determine whether a user is visiting a monitored website, with a true positive rate of 98.1% and a false positive rate of 5.7%. We also evaluated snWF in a more realistic attack scenario, termed as wide-world , to examine whether snWF can correctly classify websites that even an adversary has not seen before, and we found that snWF achieves a higher classification accuracy than the state-of-the-art attacks in this new setting. In addition, in the face of concept drift, snWF is found to be more resilient than any other attacks. Moreover, we are the first to reveal that under concept drift WF attacks suffer more severe performance degradation in a open-world setting than in a closed-world setting.

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.

Phishing attacks have always been a security issue that has attracted great attention in the cyber security community. Recently, the famous pre-trained models is being used as an anti-phishing solution. However, existing studies either simply transfer models pre-trained on text to phishing detection task, or pre-train models using only extremely small phishing samples. In this paper, we propose PhishBERT, a veritable pretrained deep transformer network model for phishing URL detection. Using a tailor pre-training objective, PhishBERT obtained a general understanding of various URLs by being pretrained on a corpus of more than 3 billion unlabeled URL data. It is then transferred to the detection task of benign and malicious URL data, with supervised fine-tuning using adversarial methods. Extensive and rigorous benchmark studies verify that PhishBERT is significantly superior to the current state-of-the-art methods in terms of efficiency, robustness and accuracy on the task of phishing website detection.

Remote Access Trojan (RAT) attacks have become an extensively prevailing and serious threat to enterprise security. A forensic system targeting RAT attacks is needed to record and reconstruct fine-grained semantic behaviors of RATs. However, existing forensic systems suffer from various issues such as intrusive instrumentation, nontrivial recording overhead, and RAT behavior blindness. In this paper, we first conduct a large-scale study of a representative set of real-world RAT families active from 1999 to 2016. This is the first study to understand the landscape of RATs in the literature. Based on the study, we then propose RATScope, an instrumentation-free RAT forensic system targeting Windows platform. Specifically, RATScope offers an audit logging module to efficiently record system logs by leveraging Event Tracing for Windows (ETW), and provides a novel program behavior modeling technique to accurately reconstruct semantic behaviors of RATs. We implemented a prototype of RATScope and evaluated the recording overhead and the behavior identification accuracy. The results show that the audit logging module only incurred 3.7% runtime overhead on average. Our system can achieve around 90% true positive rate in the cross-family experiment, around 80% true positive rate in the two-year spanning temporal experiment, and near zero false positive rate.