Social networks have become one of the most popular platforms for users to interact with each other. Given the huge amount of sensitive data available in social network platforms, user privacy protection on social networks has become one of the most urgent research issues. As a traditional information stealing technique, phishing attacks still work in their way to cause a lot of privacy violation incidents. In a Web-based phishing attack, an attacker sets up scam Web pages (pretending to be an important Website such as a social network portal) to lure users to input their private information, such as passwords, social security numbers, credit card numbers, and so on. In fact, the appearance of Web pages is among the most important factors in deceiving users, and thus, the similarity among Web pages is a critical metric for detecting phishing Websites. In this paper, we present a new solution, called Phishing-Alarm, to detect phishing attacks using features that are hard to evade by attackers. In particular, we present an algorithm to quantify the suspiciousness ratings of Web pages based on the similarity of visual appearance between the Web pages. Since cascading style sheet (CSS) is the technique to specify page layout across browser implementations, our approach uses CSS as the basis to accurately quantify the visual similarity of each page element. As page elements do not have the same influence to pages, we base our rating method on weighted page-component similarity. We prototyped our approach in the Google Chrome browser. Our large-scale evaluation using real-world websites shows the effectiveness of our approach. The proof of concept implementation verifies the correctness and accuracy of our approach with a relatively low performance overhead.

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.

Internet of Things is a critical infrastructure component as well as an enabling technology to support the fast-developing cross-region, cross-application, and diversified collaborative smart city services that require systematic cooperation among multiple smart city systems. Speech recognition-based voice controllable systems become one of the most popular interfaces in smart devices. However, it has been proved that attackers can hide their voice commands via modulating them on ultrasonic carriers and carry out inaudible voice attacks to manipulate voice controllable devices (e.g., mobile phone) unnoticeably. Although there are defense suggestions to enhance the hardware or add new modules of microphones, it is impractical to change the hardware design of all voice-controllable devices developed by different manufactures. In this article, we validate the effectiveness of ultrasonic-based inaudible voice attacks to voice-controllable smart home devices and propose a signal-processing-based hidden voice attack detection approach. Our approach uses an independent device that deploys a two-step lightweight detecting algorithm to identify the attack signals. We simulate our algorithm and make a prototype implementation of the proposed approach. The simulation results illustrate the correctness of the detection algorithm and the experiments show that our approach can detect the ultrasonic-based inaudible voice attack effectively.

With the rapid development of Internet of Things (IoT) technologies, smart home systems are getting more and more popular in our daily life. Besides providing convenient functionality and tangible benefits, smart home systems also expose users to security risks. To enhance the functionality and the security, machine learning algorithms play an important role in a smart home ecosystem, e.g., ensuring biotechnology-based authentication and authorization, anomalous detection, etc. On the other side, attackers also treat learning algorithms as a tool, as well as a target, to exploit the security vulnerabilities in smart home systems. In this paper, we unify the system architectures suggested by the mainstream service providers, e.g., Samsung, Google, Apple, etc. Based on our proposed overall smart home system model, we investigate the application of learning algorithms in smart home IoT system security. Our study includes two angles. First, we discussed the functionality and security enhancing methods based on learning mechanisms; second, we described the security threats exposed by employing learning techniques. We also explored the potential solutions that may address the aforementioned security problems.

JavaScript applications are widely used in a range of scenarios, including Web applications, mobile applications, and server-side applications. On one hand, due to its excellent cross-platform support, Javascript has become the core technology of social network platforms. On the other hand, the flexibility of the JavaScript language makes such applications prone to attacks that inject malicious behaviors. In this paper, we propose a detection technique to identify malicious behaviors in JavaScript applications. Our method models an application's normal behavior on function activation, which is used as a basis to detect attacks. We prototyped our solution on the popular JavaScript engine V8 and used it to detect attacks on the android system. Our evaluation shows the effectiveness of our approach in detecting injection attacks to JavaScript applications.