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The web technology has become the cornerstone of a wide range of platforms, such as mobile services and smart Internet-of-things (IoT) systems. In such platforms, users' data are aggregated to a cloud-based platform, where web applications are used as a key interface to access and configure user data. Securing the web interface requires solutions to deal with threats from both technical vulnerabilities and social factors. Phishing attacks are one of the most commonly exploited vectors in social engineering attacks. The attackers use web pages visually mimicking legitimate web sites, such as banking and government services, to collect users' sensitive information. Existing phishing defense mechanisms based on URLs or page contents are often evaded by attackers. Recent research has demonstrated that visual layout similarity can be used as a robust basis to detect phishing attacks. In particular, features extracted from CSS layout files can be used to measure page similarity. However, it needs human expertise in specifying how to measure page similarity based on such features. In this paper, we aim to enable automated page-layout-based phishing detection techniques using machine learning techniques. We propose a learning-based aggregation analysis mechanism to decide page layout similarity, which is used to detect phishing pages. We prototype our solution and evaluate four popular machine learning classifiers on their accuracy and the factors affecting their results.

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The rapid development of wellness smart devices and apps, such as Fitbit Coach and FitnessGenes, has triggered a wave of interaction on social networks. People communicate with and follow each other based on their wellness activities. Though such IoT devices and data provide a good motivation, they also expose users to threats due to the privacy leakage of social networks. Anonymization techniques are widely adopted to protect users' privacy during social data publishing and sharing. However, de-anonymization techniques are actively studied to identify weaknesses in current social network data-publishing mechanisms. In this paper, we conduct a comprehensive analysis on the typical structure-based social network de-anonymization algorithms. We aim to understand the de-anonymization approaches and disclose the impacts on their application performance caused by different factors, e.g., topology properties and anonymization methods adopted to sanitize original data. We design the analysis framework and define three experiment environments to evaluate a few factors' impacts on the target algorithms. Based on our analysis architecture, we simulate three typical de-anonymization algorithms and evaluate their performance under different pre-configured environments.