Panagiotis Papadopoulos

Ioannis Arapakis, Panagiotis Papadopoulos, Kleomenis Katevas et al.

Distributed (or Federated) learning enables users to train machine learning models on their very own devices, while they share only the gradients of their models usually in a differentially private way (utility loss). Although such a strategy provides better privacy guarantees than the traditional centralized approach, it requires users to blindly trust a centralized infrastructure that may also become a bottleneck with the increasing number of users. In this paper, we design and implement P4L: a privacy preserving peer-to-peer learning system for users to participate in an asynchronous, collaborative learning scheme without requiring any sort of infrastructure or relying on differential privacy. Our design uses strong cryptographic primitives to preserve both the confidentiality and utility of the shared gradients, a set of peer-to-peer mechanisms for fault tolerance and user churn, proximity and cross device communications. Extensive simulations under different network settings and ML scenarios for three real-life datasets show that P4L provides competitive performance to baselines, while it is resilient to different poisoning attacks. We implement P4L and experimental results show that the performance overhead and power consumption is minimal (less than 3mAh of discharge).

Antonio Nappa, Panagiotis Papadopoulos, Matteo Varvello et al.

Online malware scanners are one of the best weapons in the arsenal of cybersecurity companies and researchers. A fundamental part of such systems is the sandbox that provides an instrumented and isolated environment (virtualized or emulated) for any user to upload and run unknown artifacts and identify potentially malicious behaviors. The provided API and the wealth of information inthe reports produced by these services have also helped attackers test the efficacy of numerous techniques to make malware hard to detect.The most common technique used by malware for evading the analysis system is to monitor the execution environment, detect the presence of any debugging artifacts, and hide its malicious behavior if needed. This is usually achieved by looking for signals suggesting that the execution environment does not belong to a the native machine, such as specific memory patterns or behavioral traits of certain CPU instructions. In this paper, we show how an attacker can evade detection on such online services by incorporating a Proof-of-Work (PoW) algorithm into a malware sample. Specifically, we leverage the asymptotic behavior of the computational cost of PoW algorithms when they run on some classes of hardware platforms to effectively detect a non bare-metal environment of the malware sandbox analyzer. To prove the validity of this intuition, we design and implement the POW-HOW framework, a tool to automatically implement sandbox detection strategies and embed a test evasion program into an arbitrary malware sample. Our empirical evaluation shows that the proposed evasion technique is durable, hard to fingerprint, and reduces existing malware detection rate by a factor of 10. Moreover, we show how bare-metal environments cannot scale with actual malware submissions rates for consumer services.

Panagiotis Papadopoulos, Panagiotis Ilia, Michalis Polychronakis et al.

The proliferation of web applications has essentially transformed modern browsers into small but powerful operating systems. Upon visiting a website, user devices run implicitly trusted script code, the execution of which is confined within the browser to prevent any interference with the user's system. Recent JavaScript APIs, however, provide advanced capabilities that not only enable feature-rich web applications, but also allow attackers to perform malicious operations despite the confined nature of JavaScript code execution. In this paper, we demonstrate the powerful capabilities that modern browser APIs provide to attackers by presenting MarioNet: a framework that allows a remote malicious entity to control a visitor's browser and abuse its resources for unwanted computation or harmful operations, such as cryptocurrency mining, password-cracking, and DDoS. MarioNet relies solely on already available HTML5 APIs, without requiring the installation of any additional software. In contrast to previous browser-based botnets, the persistence and stealthiness characteristics of MarioNet allow the malicious computations to continue in the background of the browser even after the user closes the window or tab of the initial malicious website. We present the design, implementation, and evaluation of a prototype system, MarioNet, that is compatible with all major browsers, and discuss potential defense strategies to counter the threat of such persistent in-browser attacks. Our main goal is to raise awareness regarding this new class of attacks, and inform the design of future browser APIs so that they provide a more secure client-side environment for web applications.