ACM Transactions on Privacy and Security最新文献

Network reconnaissance and measurements play a central role in improving Internet security and are important for understanding the current deployments and trends. Such measurements often require coordination with the measured target. This limits the scalability and the coverage of the existing proposals. IP Identification (IPID) provides a side channel for remote measurements without requiring the targets to install agents or visit the measurement infrastructure. However, current IPID-based techniques have technical limitations due to their reliance on the idealistic assumption of stable IPID changes or prior knowledge, making them challenging to adopt for practical measurements.

In this work, we aim to tackle the limitations of existing techniques by introducing a novel approach: predictive analysis of IPID counter behavior. This involves utilizing a machine learning (ML) model to understand the historical patterns of IPID counter changes and predict future IPID values. To validate our approach, we implement six ML models and evaluate them on realistic IPID data collected from 4,698 Internet sources. Our evaluations demonstrate that among the six models, the GP (Gaussian Process) model has superior accuracy in tracking and predicting IPID values.

Using the GP-based predictive analysis, we implement a tool, called ZPredict, to infer various favorable information about target networks or servers. Our evaluation on a large dataset of public servers demonstrates its effectiveness in idle port scanning, measuring Russian censorship, and inferring Source Address Validation (SAV).

Our study methodology is ethical and was developed to mitigate any potential harm, taking into account the concerns associated with measurements.

Recently, several researchers motivated the need to integrate Zero Trust (ZT) principles when designing and implementing authentication and authorization systems for IoT. An integrated Zero Trust IoT system comprises the network infrastructure (physical and virtual) and operational policies in place for IoT as a product of a ZT architecture plan. This paper proposes a novel Zero Trust architecture for IoT systems called ZTA-IoT. Additionally, based on different types of interactions between various layers and components in this architecture, we present ZTA-IoT-ACF, an access control framework that recognizes different interactions that need to be controlled in IoT systems. Within this framework, the paper then refines its focus to object-level interactions, i.e., interactions where the target resource is a device (equivalently a thing) or an information file generated or stored by a device. Building on the recently proposed Zero Trust score-based authorization framework (ZT-SAF) we develop the object-level Zero Trust score-based authorization framework for IoT systems, denoted as ZTA-IoT-OL-SAF, to govern access requests in this context. With this machinery in place, we finally develop a novel usage control model for users-to-objects and devices-to-objects interactions, denoted as UCON_IoT. We give formal definitions, illustrative use cases, and a proof-of-concept implementation of UCON_IoT. This paper is a first step toward establishing a rigorous formally-defined score-based access control framework for Zero Trust IoT systems.

Remote SIM provisioning (RSP) for consumer devices is the protocol specified by the GSM Association for downloading SIM profiles into a secure element in a mobile device. The process is commonly known as eSIM, and it is expected to replace removable SIM cards. The security of the protocol is critical because the profile includes the credentials with which the mobile device will authenticate to the mobile network. In this paper, we present a formal security analysis of the consumer RSP protocol. We model the multi-party protocol in applied pi calculus, define formal security goals, and verify them in ProVerif. The analysis shows that the consumer RSP protocol protects against a network adversary when all the intended participants are honest. However, we also model the protocol in realistic partial compromise scenarios where the adversary controls a legitimate participant or communication channel. The security failures in the partial compromise scenarios reveal weaknesses in the protocol design. The most important observation is that the security of RSP depends unnecessarily on it being encapsulated in a TLS tunnel. Also, the lack of pre-established identifiers means that a compromised download server anywhere in the world or a compromised secure element can be used for attacks against RSP between honest participants. Additionally, the lack of reliable methods for verifying user intent can lead to serious security failures. Based on the findings, we recommend practical improvements to RSP implementations, future versions of the specification, and mobile operator processes to increase the robustness of eSIM security.

Sound-squatting is a squatting technique that exploits similarities in word pronunciation to trick users into accessing malicious resources. It is an understudied threat that has gained traction with the popularity of smart speakers and audio-only content, such as podcasts. The picture gets even more complex when multiple languages are involved. We here introduce X-squatter, a multi- and cross-language AI-based system that relies on a Transformer Neural Network for generating high-quality sound-squatting candidates. We illustrate the use of X-squatter by searching for domain name squatting abuse across hundreds of millions of issued TLS certificates, alongside other squatting types. Key findings unveil that approximately 15% of generated sound-squatting candidates have associated TLS certificates, well above the prevalence of other squatting types (7%). Furthermore, we employ X-squatter to assess the potential for abuse in PyPI packages, revealing the existence of hundreds of candidates within a three-year package history. Notably, our results suggest that the current platform checks cannot handle sound-squatting attacks, calling for better countermeasures. We believe X-squatter uncovers the usage of multilingual sound-squatting phenomenon on the Internet and it is a crucial asset for proactive protection against the threat.

Automatic speech recognition (ASR) systems are vulnerable to audio adversarial examples, which aim to deceive ASR systems by adding perturbations to benign speech signals. These audio adversarial examples appear indistinguishable from benign audio waves, but the ASR system decodes them as intentional malicious commands. Previous studies have demonstrated the feasibility of such attacks in simulated environments (over-line) and have further showcased the creation of robust physical audio adversarial examples (over-air). Various defense techniques have been proposed to counter these attacks. However, most of them have either failed to handle various types of attacks effectively or have resulted in significant time overhead.

In this paper, we propose a novel method for detecting audio adversarial examples. Our approach involves feeding both smoothed audio and original audio inputs into the ASR system. Subsequently, we introduce noise to the logits before providing them to the decoder of the ASR. We demonstrate that carefully selected noise can considerably influence the transcription results of audio adversarial examples while having minimal impact on the transcription of benign audio waves. Leveraging this characteristic, we detect audio adversarial examples by comparing the altered transcription, resulting from logit noising, with the original transcription. The proposed method can be easily applied to ASR systems without requiring any structural modifications or additional training. Experimental results indicate that the proposed method exhibits robustness against both over-line and over-air audio adversarial examples, outperforming state-of-the-art detection methods.

Big data has proven to be a very useful tool for companies and users, but companies with larger datasets have ended being more competitive than the others thanks to machine learning or artificial inteligence. Secure multi-party computation (SMPC) allows the smaller companies to jointly train arbitrary models on their private data while assuring privacy, and thus gives data owners the ability to perform what are currently known as federated learning algorithms. Besides, with a blockchain it is possible to coordinate and audit those computations in a decentralized way. In this document, we consider a private data marketplace as a space where researchers and data owners meet to agree the use of private data for statistics or more complex model trainings. This document presents a candidate architecure for a private data marketplace by combining SMPC and a public, general-purpose blockchain. Such a marketplace is proposed as a smart contract deployed in the blockchain, while the privacy preserving computation is held by SMPC.

The field of cybersecurity is evolving fast. Security professionals are in need of intelligence on past, current and - ideally - on upcoming threats, because attacks are becoming more advanced and are increasingly targeting larger and more complex systems. Since the processing and analysis of such large amounts of information cannot be addressed manually, cybersecurity experts rely on machine learning techniques. In the textual domain, pre-trained language models like BERT have proven to be helpful as they provide a good baseline for further fine-tuning. However, due to the domain-knowledge and the many technical terms in cybersecurity, general language models might miss the gist of textual information. For this reason, we create a high-quality dataset and present a language model specifically tailored to the cybersecurity domain which can serve as a basic building block for cybersecurity systems. The model is compared on 15 tasks: Domain-dependent extrinsic tasks for measuring the performance on specific problems, intrinsic tasks for measuring the performance of the internal representations of the model as well as general tasks from the SuperGLUE benchmark. The results of the intrinsic tasks show that our model improves the internal representation space of domain words compared to the other models. The extrinsic, domain-dependent tasks, consisting of sequence tagging and classification, show that the model performs best in cybersecurity scenarios. In addition, we pay special attention to the choice of hyperparameters against catastrophic forgetting, as pre-trained models tend to forget the original knowledge during further training.

Adaptive authentication enables smartphones and enterprise apps to decide when and how to authenticate users based on contextual and behavioral factors. In practice, a system may employ multiple policies to adapt its authentication mechanisms and access controls to various scenarios. However, existing approaches suffer from contradictory or insecure adaptations, which may enable attackers to bypass the authentication system. Besides, most existing approaches are inflexible and do not provide desirable access controls. We design and build a multi-stage risk-aware adaptive authentication and access control framework (MRAAC), which provides the following novel contributions: Multi-stage:MRAAC organizes adaptation policies in multiple stages to handle different risk types and progressively adapts authentication mechanisms based on context, resource sensitivity, and user authenticity. Appropriate access control:MRAAC provides libraries to enable sensitive apps to manage the availability of their in-app resources based on MRAAC’s risk awareness. Extensible:While existing proposals are tailored to cater to a single use case, MRAAC supports a variety of use cases with custom risk models. We exemplify these advantages of MRAAC by deploying it for three use cases: an enhanced version of Android Smart Lock, guest-aware continuous authentication, and corporate app for BYOD. We conduct experiments to quantify the CPU, memory, latency, and battery performance of MRAAC. Our evaluation shows that MRAAC enables various stakeholders (device manufacturers, enterprise and secure app developers) to provide complex adaptive authentication workflows on COTS Android with low processing and battery overhead.

Modern vehicles increasingly rely on electronics, software, and communication technologies (cyber space) to perform their driving task. Over-The-Air (OTA) connectivity further extends the cyber space by creating remote access entry points. Accordingly, the vehicle is exposed to security attacks that are able to impact road safety. A profound understanding of security attacks, vulnerabilities, and mitigations is necessary to protect vehicles against cyber threats. While automotive threat descriptions, such as in UN R155, are still abstract, this creates a risk that potential vulnerabilities are overlooked and the vehicle is not secured against them. So far, there is no common understanding of the relationship of automotive attacks, the concrete vulnerabilities they exploit, and security mechanisms that would protect the system against these attacks. In this paper, we aim at closing this gap by creating a mapping between UN R155, Microsoft STRIDE classification, Common Attack Pattern Enumerations and Classifications (CAPEC™), and Common Weakness Enumeration (CWE™). In this way, already existing detailed knowledge of attacks, vulnerabilities, and mitigations is combined and linked to the automotive domain. In practice, this refines the list of UN R155 threats and therefore supports vehicle manufacturers, suppliers, and approval authorities to meet and assess the requirements for vehicle development in terms of cybersecurity. Overall, 204 mappings between UN threats, STRIDE, CAPEC attack patterns, and CWE weaknesses were created. We validated these mappings by applying our Automotive Attack Database (AAD) that consists of 361 real-world attacks on vehicles. Furthermore, 25 additional attack patterns were defined based on automotive-related attacks.

In recent years, the widespread adoption of Machine Learning (ML) at the core of complex IT systems has driven researchers to investigate the security and reliability of ML techniques. A very specific kind of threats concerns the adversary mechanisms through which an attacker could induce a classification algorithm to provide the desired output. Such strategies, known as Adversarial Machine Learning (AML), have a twofold purpose: to calculate a perturbation to be applied to the classifier’s input such that the outcome is subverted, while maintaining the underlying intent of the original data. Although any manipulation that accomplishes these goals is theoretically acceptable, in real scenarios perturbations must correspond to a set of permissible manipulations of the input, which is rarely considered in the literature. In this paper, we present AdverSPAM, an AML technique designed to fool the spam account detection system of an Online Social Network (OSN). The proposed black-box evasion attack is formulated as an optimization problem that computes the adversarial sample while maintaining two important properties of the feature space, namely statistical correlation and semantic dependency. Although being demonstrated in an OSN security scenario, such an approach might be applied in other context where the aim is to perturb data described by mutually related features. Experiments conducted on a public dataset show the effectiveness of AdverSPAM compared to five state-of-the-art competitors, even in the presence of adversarial defense mechanisms.