ACM Transactions on Privacy and Security最新文献

Adversarial attacks against supervised learning algorithms, which necessitates the application of logging while using supervised learning algorithms in software projects. Logging enables practitioners to conduct postmortem analysis, which can be helpful to diagnose any conducted attacks. We conduct an empirical study to identify and characterize log-related coding patterns, i.e., recurring coding patterns that can be leveraged to conduct adversarial attacks and needs to be logged. A list of log-related coding patterns can guide practitioners on what to log while using supervised learning algorithms in software projects.

We apply qualitative analysis on 3,004 Python files used to implement 103 supervised learning-based software projects. We identify a list of 54 log-related coding patterns that map to 6 attacks related to supervised learning algorithms. Using Log Assistant to conductPostmortems forSupervisedLearning (LOPSUL), we quantify the frequency of the identified log-related coding patterns with 278 open source software projects that use supervised learning. We observe log-related coding patterns to appear for 22% of the analyzed files, where training data forensics is the most frequently occurring category.

Autonomous transport receives increasing attention, with research and development activities already providing prototype implementations. In this article we focus on Autonomous Passenger Ships (APS), which are being considered as a solution for passenger transport across urban waterways. The ambition of the authors has been to examine the safety and security implications of such a Cyber Physical System (CPS), particularly focusing on threats that endanger the passengers and the operational environment of the APS. Accordingly, the article presentsa new risk assessment approach based on a Failure Modes Effects and Criticality Analysis (FMECA) that is enriched with selected semantics and components of the MITRE ATT&ACK framework, in order to utilize the encoded common knowledge and facilitate the expression of attacks. Then, the proposed approach is demonstrated through conducting a risk assessment for a communication architecture tailored to the requirements of APSs that were proposed in earlier work. Moreover, we propose a group of graph theory-based metrics for estimating the impact of the identified risks. The use of this method has resulted in the identification of risks and their corresponding countermeasures, in addition to identifying risks with limited existing mitigation mechanisms. The benefits of the proposed approach are the comprehensive, atomic, and descriptive nature of the identified threats, which reduce the need for expert judgment, and the granular impact estimation metrics that reduce the impact of bias. All these features are provided in a semi-automated approach the reduce the required effort and collectively are argued to enrich the design-level risk assessment processes with an updatable industry threat model standard, namely ATT&ACK.

The uniqueness of behavioural biometrics (e.g., voice or keystroke patterns) has been challenged by recent works. Statistical attacks have been proposed that infer general population statistics and target behavioural biometrics against a particular victim. We show that despite their success, these approaches require several attempts for successful attacks against different biometrics due to the different nature of overlap in users’ behaviour for these biometrics. Furthermore, no mechanism has been proposed to date that detects statistical attacks. In this work, we propose a new hypervolumes-based statistical attack and show that unlike existing methods it: 1) is successful against a variety of biometrics; 2) is successful against more users; and 3) requires fewest attempts for successful attacks. More specifically, across five diverse biometrics, for the first attempt, on average our attack is 18 percentage points more successful than the second best (37% vs. 19%). Similarly, for the fifth attack attempt, on average our attack is 18 percentage points more successful than the second best (67% vs. 49%). We propose and evaluate a mechanism that can detect the more devastating statistical attacks. False rejects in biometric systems are common and by distinguishing statistical attacks from false rejects, our defence improves usability and security. The evaluation of the proposed detection mechanism shows its ability to detect on average 94% of the tested statistical attacks with an average probability of 3% to detect false rejects as a statistical attack. Given the serious threat posed by statistical attacks to biometrics that are used today (e.g., voice), our work highlights the need for defending against these attacks.

Data exchange between organizations is becoming an increasingly significant issue due to the great opportunities it presents. However, there is great reluctance to share if data sovereignty is not provided. Providing it calls for not only access control but also usage control implemented in distributed systems. Access control is a research field where there has been a great deal of work, but usage control, especially implemented in distributed systems as Distributed Usage Control (DUC), is a very new field of research that presents great challenges. Moreover, little is known about what challenges must really be faced and how they must be addressed. This is evidenced by the fact that existing research has focused non-specifically on different features of DUC, which are not formalized. Therefore, the path for the development of DUC solutions is unclear and it is difficult to analyze the scope of data sovereignty attained by the wide range of DUC solutions. In this context, this article is based on an initial in-depth analysis of DUC related work. In it, the challenges posed by DUC in terms of data sovereignty and the features that must be provided to address them are identified and analyzed for the first time. Based on these features, an initial DUC framework is proposed to assess in a practical and unified way the extent to which DUC solutions provide data sovereignty. Finally, the assessment framework is applied to compare the scopes of the most widespread DUC solutions and identify their limitations.