With the development of automated software testing technology, software developers can get a large number of crash test cases in a short period of time. However, analyzing these crash test cases and finding their root cause is a time-consuming and labor-intensive task. Techniques based on reverse execution and backward taint analysis are proposed to locate the root cause, but can’t provide context information or explanation of the underlying fault. To address these two limitations, researchers have proposed an automated root cause analysis technique called AURORA. Although this technique provides powerful root cause analysis capabilities, it also have two obvious shortcomings. First, the results of root cause analysis are not accurate enough. Second, the efficiency of root cause analysis is not high enough. In order to improve these two shortcomings, we propose OptRCA, a more efficient and accurate approach for root cause analysis and explanation. Like AURORA’s fuzzing strategy, OptRCA is also designed based on AFL’s crash mode. The difference between them is mainly reflected in three points. First of all, the goal pursued by OptRCA is different from that of normal fuzzing technology. OptRCA pursues maximum correlation to ensure that as many crash test cases as possible are related to the same root cause. This test case with maximum correlation can greatly improve the accuracy of root cause analysis. Second, OptRCA proposed a more efficient non-crash test case retention strategy, which we named “Hill-Climbing Retention.” Using the hill-climbing retention method, OptRCA can obtain sufficient root cause information while retaining only a few non-crash test cases. Since the number of test cases is greatly reduced, the efficiency of OptRCA’s subsequent root cause analysis process is also greatly improved. In addition, OptRCA also optimizes the analysis formula to obtain more accurate analysis results. In the evaluation experimental results, OptRCA is significantly better than AURORA in terms of accuracy and efficiency. Quantitative analysis shows that OptRCA is 65% more accurate and 61% more efficient than AURORA.

Web services have brought great convenience to our daily lives. Meanwhile, they are vulnerable to Denial-of-Service (DoS) attacks. DoS attacks launched via vulnerabilities in the services can cause great harm. The vulnerabilities in protocol implementations are especially important because they are the keystones of web services. One vulnerable protocol implementation can affect all the web services built on top of it. Compared to the vulnerabilities that cause the target service to crash, resource exhaustion vulnerabilities are equally if not more important. This is because such vulnerabilities can deplete the system resources, leading to the unavailability of not only the vulnerable service but also other services running on the same machine. Despite the significance of this type of vulnerability, there has been limited research in this area.

In this article, we investigate a fundamental question regarding software security: Is the security of SW releases increasing over time? We approach this question with a detailed analysis of the large body of open-source software packaged in the popular Debian GNU/Linux distribution. Contrary to common intuition, we find no clear evidence that the vulnerability rate of widely used software decreases over time: Even in popular and “stable” releases, the fixing of bugs does not seem to reduce the rate of newly identified vulnerabilities. The intuitive conclusion is worrisome: Commonly employed development and validation procedures do not seem to scale with the increase of features and complexity—they are only chopping pieces off the top of an iceberg of vulnerabilities. To the best of our knowledge, this is the first investigation into the problem that studies a complete distribution of software, spanning multiple versions. Although we can not give a definitive answer, we show that several popular beliefs also cannot be confirmed given our dataset. We publish our Debian Vulnerability Analysis Framework (DVAF) , an automated dataset creation and analysis process, to enable reproduction and further analysis of our results. Overall, we hope our contributions provide important insights into the vulnerability discovery process and help in identifying effective techniques for vulnerability analysis and prevention.

In distributed storage systems, documents are shared among multiple Cloud providers and stored within their respective storage servers. In social secret sharing-based distributed storage systems, shares of the documents are allocated according to the trustworthiness of the storage servers. This paper proposes a trust mechanism using machine learning techniques to compute evidence-based trust values. Our mechanism mitigates the effect of colluding storage servers. More precisely, it becomes possible to detect unreliable evidence and establish countermeasures in order to discourage the collusion of storage servers. Furthermore, this trust mechanism is applied to the social secret sharing protocol AS^3, showing that this new evidence-based trust mechanism enhances the protection of the stored documents.

Distributed storage allows to outsource a document to the cloud such that multiple users can easily access the file. The protection of the document stored relies on secret sharing, which generates and distributes shares of the document to the storage servers. However, the users have to trust that a certain amount of storage servers behaves honestly and do not lose (retrievability) or reveal (confidentiality) the document. To address this so called social secret sharing schemes were developed that allow to adjust the distribution of shares according to the experience made with the involved storage servers. In this work, we provide a framework called AS3that allows to build social secret sharing schemes based on dynamic secret sharing. The resulting protocol has more freedom in adjusting the parameters of the shares distribution and therefore leads to more efficient and accurate solutions as well as an optimal storage consumption. Furthermore, we provide measures to detect and to prevent that the document is lost or accidentally revealed to individual storage servers. We also demonstrate how to compute trust values for storage servers, how to initialize trust values for newcomers, and provide a proof of concept implementation.

Common conceptualizations of trust concern expectations about the future behavior of an entity. We focus on expectations about fair future behavior: this aspect was insufficiently formalized to date, prohibiting formal reasoning about fairness. In this paper, we extend our previous binary fairness model to a gradual one. We demonstrate the capabilities of gradual fairness in two scenarios: "distributing revenue in open-ended funds" and "e-business retailing". In both scenarios, independent partners decide on business collaboration under incomplete information. Furthermore, we contribute an approach to determining an optimal distribution under different types and grades of fairness expectations.

ABSTRACT Cloud computing enables information technology related services in a more dynamic and scalable way than before—more cost‐effective than before due to the economy of scale and of sharing resources. Usually, cloud providers describe their promised behaviour—regarding functional and non‐functional aspects of the service provision—by way of service level agreements (SLAs). For different providers offering similar functionality, SLAs are often insufficiently claimable and inconsistent with the aspects considered important by customers. Therefore, customers face problems identifying a trustworthy cloud provider solely on the basis of its SLA. To support customers in reliably identifying trustworthy cloud providers, we propose a multi‐faceted trust management system architecture for cloud computing marketplaces and related approaches. This system provides the means for identifying trustworthy cloud providers in terms of different attributes, for example, compliance, data governance and information security. In this article, we present the first realization of our proposed trust management system using the Consensus Assessment Initiative Questionnaire, initiated by the Cloud Security Alliance, as one of the sources of trust information. In particular, our proposed approach contributes to the challenge of extracting trust information from Consensus Assessment Initiative Questionnaires completed by cloud providers. Finally, our implemented system and related approaches are experimented using real datasets. Copyright © 2013 John Wiley & Sons, Ltd.

The fusion of trust relevant information provided by multiple sources is one of the major challenges of trust establishment, which in turn is a key research topic in the growing field of cloud computing. We present a novel fusion operator for combining information from different sources, representing propositions under uncertainty. The operator especially extend the state-of-the-art by explicitly considering weights and the handling of conflicting dependent opinions. We provide a use case that demonstrates the applicability of our approach and shows the capability of the novel operator to a more reliable and transparent assessment of the trustworthiness of cloud providers.

Cloud computing provides cost-efficient opportunities for enterprises by offering a variety of dynamic, scalable, and shared services. Usually, cloud providers provide assurances by specifying technical and functional descriptions in Service Level Agreements (SLAs) for the services they offer. The descriptions in SLAs are not consistent among the cloud providers even though they offer services with similar functionality. Therefore, customers are not sure whether they can identify a trustworthy cloud provider only based on its SLA. To support the customers in reliably identifying trustworthy cloud providers, we propose a multi-faceted Trust Management (TM) system architecture for a cloud computing marketplace. This system provides means to identify the trustworthy cloud providers in terms of different attributes (e.g., security, performance, compliance) assessed by multiple sources and roots of trust information.