Avoid Adversarial Adaption in Federated Learning by Multi-Metric Investigations

Federated Learning (FL) trains machine learning models on data distributed across multiple devices, avoiding data transfer to a central location. This improves privacy, reduces communication costs, and enhances model performance. However, FL is prone to poisoning attacks, which can be untargeted aiming to reduce the model performance, or targeted, so-called backdoors, which add adversarial behavior that can be triggered with appropriately crafted inputs. Striving for stealthiness, backdoor attacks are harder to deal with. Mitigation techniques against poisoning attacks rely on monitoring certain metrics and filtering malicious model updates. However, previous works didn't consider real-world adversaries and data distributions. To support our statement, we define a new notion of strong adaptive adversaries that can simultaneously adapt to multiple objectives and demonstrate through extensive tests, that existing defense methods can be circumvented in this adversary model. We also demonstrate, that existing defenses have limited effectiveness when no assumptions are made about underlying data distributions. To address realistic scenarios and adversary models, we propose Metric-Cascades (MESAS) a new defense that leverages multiple detection metrics simultaneously for the filtering of poisoned model updates. This approach forces adaptive attackers into a heavy multi-objective optimization problem, and our evaluation with nine backdoors and three datasets shows that even our strong adaptive attacker cannot evade MESAS's detection. We show that MESAS outperforms existing defenses in distinguishing backdoors from distortions originating from different data distributions within and across the clients. Overall, MESAS is the first defense that is robust against strong adaptive adversaries and is effective in real-world data scenarios while introducing a low overhead of 24.37s on average.Comment: 23 pages, 12 figures, 27 tables, 11 equation

ClearMark: Intuitive and Robust Model Watermarking via Transposed Model Training

Due to costly efforts during data acquisition and model training, Deep Neural Networks (DNNs) belong to the intellectual property of the model creator. Hence, unauthorized use, theft, or modification may lead to legal repercussions. Existing DNN watermarking methods for ownership proof are often non-intuitive, embed human-invisible marks, require trust in algorithmic assessment that lacks human-understandable attributes, and rely on rigid thresholds, making it susceptible to failure in cases of partial watermark erasure. This paper introduces ClearMark, the first DNN watermarking method designed for intuitive human assessment. ClearMark embeds visible watermarks, enabling human decision-making without rigid value thresholds while allowing technology-assisted evaluations. ClearMark defines a transposed model architecture allowing to use of the model in a backward fashion to interwove the watermark with the main task within all model parameters. Compared to existing watermarking methods, ClearMark produces visual watermarks that are easy for humans to understand without requiring complex verification algorithms or strict thresholds. The watermark is embedded within all model parameters and entangled with the main task, exhibiting superior robustness. It shows an 8,544-bit watermark capacity comparable to the strongest existing work. Crucially, ClearMark's effectiveness is model and dataset-agnostic, and resilient against adversarial model manipulations, as demonstrated in a comprehensive study performed with four datasets and seven architectures.Comment: 20 pages, 18 figures, 4 table

SECURITY AND PRIVACY ASPECTS OF MOBILE PLATFORMS AND APPLICATIONS

Mobile smart devices (such as smartphones and tablets) emerged to dominant computing platforms for end-users. The capabilities of these convenient mini-computers seem nearly boundless: They feature compelling computing power and storage resources, new interfaces such as Near Field Communication (NFC) and Bluetooth Low Energy (BLE), connectivity to cloud services, as well as a vast number and variety of apps. By installing these apps, users can turn a mobile device into a music player, a gaming console, a navigation system, a business assistant, and more. In addition, the current trend of increased screen sizes make these devices reasonable replacements for traditional (mobile) computing platforms such as laptops. On the other hand, mobile platforms process and store the extensive amount of sensitive information about their users, ranging from the user’s location data to credentials for online banking and enterprise Virtual Private Networks (VPNs). This raises many security and privacy concerns and makes mobile platforms attractive targets for attackers. The rapid increase in number, variety and sophistication of attacks demonstrate that the protection mechanisms offered by mobile systems today are insufficient and improvements are necessary in order to make mobile devices capable of withstanding modern security and privacy threats. This dissertation focuses on various aspects of security and privacy of mobile platforms. In particular, it consists of three parts: (i) advanced attacks on mobile platforms and countermeasures; (ii) online authentication security for mobile systems, and (iii) secure mobile applications and services. Specifically, the first part of the dissertation concentrates on advanced attacks on mobile platforms, such as code re-use attacks that hijack execution flow of benign apps without injecting malicious code, and application-level privilege escalation attacks that allow malicious or compromised apps to gain more privileges than were initially granted. In this context, we develop new advanced code re-use attack techniques that can bypass deployed protection mechanisms (e.g., Address Space Layout Randomization (ASLR)) and cannot be detected by any of the existing security tools (e.g., return address checkers). Further, we investigate the problem of application-level privilege escalation attacks on mobile platforms like Android, study and classify them, develop proof of concept exploits and propose countermeasures against these attacks. Our countermeasures can mitigate all types of application-level privilege escalation attacks, in contrast to alternative solutions proposed in literature. In the second part of the dissertation we investigate online authentication schemes frequently utilized by mobile users, such as the most common web authentication based upon the user’s passwords and the recently widespread mobile 2-factor authentication (2FA) which extends the password-based approach with a secondary authenticator sent to a user’s mobile device or generated on it (e.g, a One-time Password (OTP) or Transaction Authentication Number (TAN)). In this context we demonstrate various weaknesses of mobile 2FA schemes deployed for login verification by global Internet service providers (such as Google, Dropbox, Twitter, and Facebook) and by a popular Google Authenticator app. These weaknesses allow an attacker to impersonate legitimate users even if their mobile device with the secondary authenticator is not compromised. We then go one step further and develop a general attack method for bypassing mobile 2FA schemes. Our method relies on a cross-platform infection (mobile-to-PC or PC-to-mobile) as a first step in order to compromise the Personal Computer (PC) and a mobile device of the same user. We develop proof-of-concept prototypes for a cross-platform infection and show how an attacker can bypass various instantiations of mobile 2FA schemes once both devices, PC and the mobile platform, are infected. We then deliver proof-of-concept attack implementations that bypass online banking solutions based on SMS-based TANs and visual cryptograms, as well as login verification schemes deployed by various Internet service providers. Finally, we propose a wallet-based secure solution for password-based authentication which requires no secondary authenticator, and yet provides better security guaranties than, e.g., mobile 2FA schemes. The third part of the dissertation concerns design and development of security sensitive mobile applications and services. In particular, our first application allows mobile users to replace usual keys (for doors, cars, garages, etc.) with their mobile devices. It uses electronic access tokens which are generated by the central key server and then downloaded into mobile devices for user authentication. Our solution protects access tokens in transit (e.g., while they are downloaded on the mobile device) and when they are stored and processed on the mobile platform. The unique feature of our solution is offline delegation: Users can delegate (a portion of) their access rights to other users without accessing the key server. Further, our solution is efficient even when used with constraint communication interfaces like NFC. The second application we developed is devoted to resource sharing among mobile users in ad-hoc mobile networks. It enables users to, e.g., exchange files and text messages, or share their tethering connection. Our solution addresses security threats specific to resource sharing and features the required security mechanisms (e.g., access control of resources, pseudonymity for users, and accountability for resource use). One of the key features of our solution is a privacy-preserving access control of resources based on FoF Finder (FoFF) service, which provides a user-friendly means to configure access control based upon information from social networks (e.g., friendship information) while preserving user privacy (e.g., not revealing their social network identifiers). The results presented in this dissertation were included in several peer-reviewed publications and extended technical reports. Some of these publications had significant impact on follow up research. For example, our publications on new forms of code re-use attacks motivated researchers to develop more advanced forms of ASLR and to re-consider the idea of using Control-Flow Integrity (CFI). Further, our work on application-level privilege escalation attacks was followed by many other publications addressing this problem. Moreover, our access control solution using mobile devices as access tokens demonstrated significant practical impact: in 2013 it was chosen as a highlight of CeBIT – the world’s largest international computer expo, and was then deployed by a large enterprise to be used by tens of thousands of company employees and millions of customers

Software Grand Exposure: SGX Cache Attacks Are Practical

Side-channel information leakage is a known limitation of SGX. Researchers have demonstrated that secret-dependent information can be extracted from enclave execution through page-fault access patterns. Consequently, various recent research efforts are actively seeking countermeasures to SGX side-channel attacks. It is widely assumed that SGX may be vulnerable to other side channels, such as cache access pattern monitoring, as well. However, prior to our work, the practicality and the extent of such information leakage was not studied. In this paper we demonstrate that cache-based attacks are indeed a serious threat to the confidentiality of SGX-protected programs. Our goal was to design an attack that is hard to mitigate using known defenses, and therefore we mount our attack without interrupting enclave execution. This approach has major technical challenges, since the existing cache monitoring techniques experience significant noise if the victim process is not interrupted. We designed and implemented novel attack techniques to reduce this noise by leveraging the capabilities of the privileged adversary. Our attacks are able to recover confidential information from SGX enclaves, which we illustrate in two example cases: extraction of an entire RSA-2048 key during RSA decryption, and detection of specific human genome sequences during genomic indexing. We show that our attacks are more effective than previous cache attacks and harder to mitigate than previous SGX side-channel attacks

CrowdGuard: Federated Backdoor Detection in Federated Learning

Federated Learning (FL) is a promising approach enabling multiple clients to train Deep Neural Networks (DNNs) collaboratively without sharing their local training data. However, FL is susceptible to backdoor (or targeted poisoning) attacks. These attacks are initiated by malicious clients who seek to compromise the learning process by introducing specific behaviors into the learned model that can be triggered by carefully crafted inputs. Existing FL safeguards have various limitations: They are restricted to specific data distributions or reduce the global model accuracy due to excluding benign models or adding noise, are vulnerable to adaptive defense-aware adversaries, or require the server to access local models, allowing data inference attacks. This paper presents a novel defense mechanism, CrowdGuard, that effectively mitigates backdoor attacks in FL and overcomes the deficiencies of existing techniques. It leverages clients' feedback on individual models, analyzes the behavior of neurons in hidden layers, and eliminates poisoned models through an iterative pruning scheme. CrowdGuard employs a server-located stacked clustering scheme to enhance its resilience to rogue client feedback. The evaluation results demonstrate that CrowdGuard achieves a 100% True-Positive-Rate and True-Negative-Rate across various scenarios, including IID and non-IID data distributions. Additionally, CrowdGuard withstands adaptive adversaries while preserving the original performance of protected models. To ensure confidentiality, CrowdGuard uses a secure and privacy-preserving architecture leveraging Trusted Execution Environments (TEEs) on both client and server sides.Comment: To appear in the Network and Distributed System Security (NDSS) Symposium 2024. Phillip Rieger and Torsten Krau{\ss} contributed equally to this contribution. 19 pages, 8 figures, 5 tables, 4 algorithms, 5 equation

SmartTokens: Delegable Access Control with NFC-enabled Smartphones (Full Version)

Today\u27s smartphones and tablets offer compelling computing and storage capabilities enabling a variety of mobile applications with rich functionality. The integration of new interfaces, in particular near field communication~(NFC) opens new opportunities for new applications and business models, as the most recent trend in industry for payment and ticketing shows. These applications require storing and processing security-critical data on smartphones, making them attractive targets for a variety of attacks. The state of the art to enhance platform security concerns outsourcing security-critical computations to hardware-isolated Trusted Execution Environments~(TrEE). However, since these TrEEs are used by software running in commodity operating systems, malware could impersonate the software and use the TrEE in an unintended way. Further, existing NFC-based access control solutions for smartphones are either not public or based on strong assumptions that are hard to achieve in practice. We present the design and implementation of a generic access control system for NFC-enabled smartphones based on a multi-level security architecture for smartphones. Our solution allows users to delegate their access rights and addresses the bandwidth constraints of NFC. Our prototype captures electronic access to facilities, such as entrances and offices, and binds NFC operations to a software-isolated TrEE established on the widely used Android smartphone operating system. We provide a formal security analysis of our protocols and evaluated the performance of our solution

Contact Discovery in Mobile Messengers: Low-cost Attacks, Quantitative Analyses, and Efficient Mitigations

Contact discovery allows users of mobile messengers to conveniently connect with people in their address book. In this work, we demonstrate that severe privacy issues exist in currently deployed contact discovery methods and propose suitable mitigations. Our study of three popular messengers (WhatsApp, Signal, and Telegram) shows that large-scale crawling attacks are (still) possible. Using an accurate database of mobile phone number prefixes and very few resources, we queried 10% of US mobile phone numbers for WhatsApp and 100% for Signal. For Telegram we find that its API exposes a wide range of sensitive information, even about numbers not registered with the service. We present interesting (cross-messenger) usage statistics, which also reveal that very few users change the default privacy settings. Furthermore, we demonstrate that currently deployed hashing-based contact discovery protocols are severely broken by comparing three methods for efficient hash reversal. Most notably, we show that with the password cracking tool JTR we can iterate through the entire world-wide mobile phone number space in <150s on a consumer-grade GPU. We also propose a significantly improved rainbow table construction for non-uniformly distributed input domains that is of independent interest. Regarding mitigations, we most notably propose two novel rate-limiting schemes: our incremental contact discovery for services without server-side contact storage strictly improves over Signal\u27s current approach while being compatible with private set intersection, whereas our differential scheme allows even stricter rate limits at the overhead for service providers to store a small constant-size state that does not reveal any contact information