Web-based applications are gaining popularity as they require less client-side resources, and are easier to deliver and maintain. On the other hand, web applications also pose new security and privacy challenges. In particular, recent research revealed that many high profile web applications might cause sensitive user inputs to be leaked from encrypted traffic due to side-channel attacks exploiting unique patterns in packet sizes and timing. Moreover, existing solutions, such as random padding and packet-size rounding, were shown to incur prohibitive overhead while still failing to guarantee sufficient privacy protection. In this paper, we first observe an interesting similarity between this privacy-preserving traffic padding (PPTP) issue and another well studied problem, privacy-preserving data publishing (PPDP). Based on such a similarity, we present a formal PPTP model encompassing the privacy requirements, padding costs, and padding methods. We then formulate PPTP problems under different application scenarios, analyze their complexity, and design efficient heuristic algorithms. Finally, we confirm the effectiveness and efficiency of our algorithms by comparing them to existing solutions through experiments using real-world web applications.

By enabling a direct comparison of different security solutions with respect to their relative effectiveness, a network security metric may provide quantifiable evidences to assist security practitioners in securing computer networks. However, research on security metrics has been hindered by difficulties in handling zero-day attacks exploiting unknown vulnerabilities. In fact, the security risk of unknown vulnerabilities has been considered as something unmeasurable due to the less predictable nature of software flaws. This causes a major difficulty to security metrics, because a more secure configuration would be of little value if it were equally susceptible to zero-day attacks. In this paper, we propose a novel security metric, k-zero day safety, to address this issue. Instead of attempting to rank unknown vulnerabilities, our metric counts how many such vulnerabilities would be required for compromising network assets; a larger count implies more security because the likelihood of having more unknown vulnerabilities available, applicable, and exploitable all at the same time will be significantly lower. We formally define the metric, analyze the complexity of computing the metric, devise heuristic algorithms for intractable cases, and finally demonstrate through case studies that applying the metric to existing network security practices may generate actionable knowledge.

A network security metric is desirable in evaluating the effectiveness of security solutions in distributed systems. Aggregating CVSS scores of individual vulnerabilities provides a practical approach to network security metric. However, existing approaches to aggregating CVSS scores usually cause useful semantics of individual scores to be lost in the aggregated result. In this paper, we address this issue through two novel approaches. First, instead of taking each base score as an input, our approach drills down to the underlying base metric level where dependency relationships have well-defined semantics. Second, our approach interprets and aggregates the base metrics from three different aspects in order to preserve corresponding semantics of the individual scores. Finally, we confirm the advantages of our approaches through simulation.