Biblio

In recent years, as an important part of the Internet, web applications have gradually penetrated into life. Now enterprises, units and institutions are using web applications regardless of size. Intrusion detection to effectively identify malicious traffic has become an inevitable requirement for the development of network security technology. In addition, the proportion of deserialization vulnerabilities is increasing. Traditional intrusion detection mostly focuses on the identification of SQL injection, XSS, and command execution, and there are few studies on the identification of deserialization attack traffic. This paper use a method to extracts relevant features from the deserialized traffic or even the obfuscated deserialized traffic by reorganizing the traffic and running the relevant content through simulation, and combines deep learning technology to make judgments to efficiently identify deserialization attacks. Finally, a prototype system was designed to capture related attacks in real-world. The technology can be used in the field of malicious traffic detection and help combat Internet crimes in the future.

The Internet plays an increasingly important role in people's lives, but it also brings security problems. CAPTCHA, which stands for Completely Automated Public Turing Test to Tell Computers and Humans Apart, has been widely used as a security mechanism. This paper outlines the scientific and technological progress in both the design and attack of CAPTCHAs related to these three CAPTCHA categories. It first presents a comprehensive survey of recent developments for each CAPTCHA type in terms of usability, robustness and their weaknesses and strengths. Second, it summarizes the attack methods for each category. In addition, the differences between the three CAPTCHA categories and the attack methods will also be discussed. Lastly, this paper provides suggestions for future research and proposes some problems worthy of further study.

With the rapid development of the contemporary society, wide use of smart phone and vehicle sensing devices brings a huge influence on the extensive data collection. Network coding can only provide weak security privacy protection. Aiming at weak secure feature of network coding, this paper proposes an information transfer mechanism, Weak Security Network Coding with Homomorphic Encryption (HE-WSNC), and it is integrated into routing policy. In this mechanism, a movement model is designed, which allows information transmission process under Wi-Fi and Bluetooth environment rather than consuming 4G data flow. Not only does this application reduce the cost, but also improve reliability of data transmission. Moreover, it attracts more users to participate.

Integrated Clinical Environment (ICE) is a standardized framework for achieving device interoperability in medical cyber-physical systems. The ICE utilizes high-level supervisory apps and a low-level communication middleware to coordinate medical devices. The need to design complex ICE systems that are both safe and effective has presented numerous challenges, including interoperability, context-aware intelligence, security and privacy. In this paper, we present a data flow analysis framework for the ICE systems. The framework performs the combination of static and dynamic analysis for the sensitive data and operations in the ICE systems. Our experiments demonstrate that the data flow analysis framework can record how the medical devices transmit sensitive data and perform misuse detection by tracing the runtime context of the sensitive operations.

While email plays a growingly important role on the Internet, we are faced with more severe challenges brought by compromised email accounts, especially for the administrators of institutional email service providers. Inspired by the previous experience on spam filtering and compromised accounts detection, we propose several criteria, like Success Outdegree Proportion, Reverse Pagerank, Recipient Clustering Coefficient and Legitimate Recipient Proportion, for compromised email accounts detection from the perspective of graph topology in this paper. Specifically, several widely used social network analysis metrics are used and adapted according to the characteristics of mail log analysis. We evaluate our methods on a dataset constructed by mining the one month (30 days) mail log from an university with 118,617 local users and 11,460,399 mail log entries. The experimental results demonstrate that our methods achieve very positive performance, and we also prove that these methods can be efficiently applied on even larger datasets.