arXiv subjects

Vasilios Mavroudis

Publications and source records attributed to Vasilios Mavroudis.

52 records · Page 3Linked to original sources

arXiv2024

A View on Out-of-Distribution Identification from a Statistical Testing Theory Perspective

We study the problem of efficiently detecting Out-of-Distribution (OOD) samples at test time in supervised and unsupervised learning contexts. While ML models are typically trained under the assumption that training and test data stem from the same distribution, this is often not the case in realistic settings, thus reliably detecting distribution shifts is crucial at deployment. We re-formulate the OOD problem under the lenses of statistical testing and then discuss conditions that render the OOD problem identifiable in statistical terms. Building on this framework, we study convergence guarantees of an OOD test based on the Wasserstein distance, and provide a simple empirical evaluation.

cs.LG↗

arXiv2024

Nearest Neighbour with Bandit Feedback

In this paper we adapt the nearest neighbour rule to the contextual bandit problem. Our algorithm handles the fully adversarial setting in which no assumptions at all are made about the data-generation process. When combined with a sufficiently fast data-structure for (perhaps approximate) adaptive nearest neighbour search, such as a navigating net, our algorithm is extremely efficient - having a per trial running time polylogarithmic in both the number of trials and actions, and taking only quasi-linear space. We give generic regret bounds for our algorithm and further analyse them when applied to the stochastic bandit problem in euclidean space. We note that our algorithm can also be applied to the online classification problem.

cs.LG↗

arXiv2023

A Hierarchical Nearest Neighbour Approach to Contextual Bandits

In this paper we consider the adversarial contextual bandit problem in metric spaces. The paper "Nearest neighbour with bandit feedback" tackled this problem but when there are many contexts near the decision boundary of the comparator policy it suffers from a high regret. In this paper we eradicate this problem, designing an algorithm in which we can hold out any set of contexts when computing our regret term. Our algorithm builds on that of "Nearest neighbour with bandit feedback" and hence inherits its extreme computational efficiency.

cs.LG↗

arXiv2023

Canaries and Whistles: Resilient Drone Communication Networks with (or without) Deep Reinforcement Learning

Communication networks able to withstand hostile environments are critically important for disaster relief operations. In this paper, we consider a challenging scenario where drones have been compromised in the supply chain, during their manufacture, and harbour malicious software capable of wide-ranging and infectious disruption. We investigate multi-agent deep reinforcement learning as a tool for learning defensive strategies that maximise communications bandwidth despite continual adversarial interference. Using a public challenge for learning network resilience strategies, we propose a state-of-the-art expert technique and study its superiority over deep reinforcement learning agents. Correspondingly, we identify three specific methods for improving the performance of our learning-based agents: (1) ensuring each observation contains the necessary information, (2) using expert agents to provide a curriculum for learning, and (3) paying close attention to reward. We apply our methods and present a new mixed strategy enabling expert and learning-based agents to work together and improve on all prior results.

cs.CR↗

arXiv2023

Adaptive Webpage Fingerprinting from TLS Traces

In webpage fingerprinting, an on-path adversary infers the specific webpage loaded by a victim user by analysing the patterns in the encrypted TLS traffic exchanged between the user's browser and the website's servers. This work studies modern webpage fingerprinting adversaries against the TLS protocol; aiming to shed light on their capabilities and inform potential defences. Despite the importance of this research area (the majority of global Internet users rely on standard web browsing with TLS) and the potential real-life impact, most past works have focused on attacks specific to anonymity networks (e.g., Tor). We introduce a TLS-specific model that: 1) scales to an unprecedented number of target webpages, 2) can accurately classify thousands of classes it never encountered during training, and 3) has low operational costs even in scenarios of frequent page updates. Based on these findings, we then discuss TLS-specific countermeasures and evaluate the effectiveness of the existing padding capabilities provided by TLS 1.3.

cs.CR↗

arXiv2023

Reward Shaping for Happier Autonomous Cyber Security Agents

As machine learning models become more capable, they have exhibited increased potential in solving complex tasks. One of the most promising directions uses deep reinforcement learning to train autonomous agents in computer network defense tasks. This work studies the impact of the reward signal that is provided to the agents when training for this task. Due to the nature of cybersecurity tasks, the reward signal is typically 1) in the form of penalties (e.g., when a compromise occurs), and 2) distributed sparsely across each defense episode. Such reward characteristics are atypical of classic reinforcement learning tasks where the agent is regularly rewarded for progress (cf. to getting occasionally penalized for failures). We investigate reward shaping techniques that could bridge this gap so as to enable agents to train more sample-efficiently and potentially converge to a better performance. We first show that deep reinforcement learning algorithms are sensitive to the magnitude of the penalties and their relative size. Then, we combine penalties with positive external rewards and study their effect compared to penalty-only training. Finally, we evaluate intrinsic curiosity as an internal positive reward mechanism and discuss why it might not be as advantageous for high-level network monitoring tasks.

cs.LG↗

arXiv2023

Inroads into Autonomous Network Defence using Explained Reinforcement Learning

Computer network defence is a complicated task that has necessitated a high degree of human involvement. However, with recent advancements in machine learning, fully autonomous network defence is becoming increasingly plausible. This paper introduces an end-to-end methodology for studying attack strategies, designing defence agents and explaining their operation. First, using state diagrams, we visualise adversarial behaviour to gain insight about potential points of intervention and inform the design of our defensive models. We opt to use a set of deep reinforcement learning agents trained on different parts of the task and organised in a shallow hierarchy. Our evaluation shows that the resulting design achieves a substantial performance improvement compared to prior work. Finally, to better investigate the decision-making process of our agents, we complete our analysis with a feature ablation and importance study.

cs.CR↗

arXiv2023

VAMS: Verifiable Auditing of Access to Confidential Data

We propose VAMS, a system that enables transparency for audits of access to data requests without compromising the privacy of parties in the system. VAMS supports audits on an aggregate level and an individual level, by relying on three mechanisms. A tamper-evident log provides integrity for the log entries that are audited. A tagging scheme allows users to query log entries that relate to them, without allowing others to do so. MultiBallot, a novel extension of the ThreeBallot voting scheme, is used to generate a synthetic dataset that can be used to publicly verify published statistics with a low expected privacy loss. We evaluate two implementations of VAMS, and show that both the log and the ability to verify published statistics are practical for realistic use cases such as access to healthcare records and law enforcement access to communications records.

cs.CR↗

arXiv2021

An Interface between Legacy and Modern Mobile Devices for Digital Identity

In developing regions a substantial number of users rely on legacy and ultra-low-cost mobile devices. Unfortunately, many of these devices are not equipped to run the standard authentication or identity apps that are available for smartphones. Increasingly, apps that display Quick Response (QR) codes are being used to communicate personal credentials (e.g., Covid-19 vaccination certificates). This paper describes a novel interface for QR code credentials that is compatible with legacy mobile devices. Our solution, which we have released under open source licensing, allows Web Application Enabled legacy mobile devices to load and display standard QR codes. This technique makes modern identity platforms available to previously excluded and economically disadvantaged populations.

cs.CR↗

arXiv2020

Snappy: Fast On-chain Payments with Practical Collaterals

Permissionless blockchains offer many advantages but also have significant limitations including high latency. This prevents their use in important scenarios such as retail payments, where merchants should approve payments fast. Prior works have attempted to mitigate this problem by moving transactions off the chain. However, such Layer-2 solutions have their own problems: payment channels require a separate deposit towards each merchant and thus significant locked-in funds from customers; payment hubs require very large operator deposits that depend on the number of customers; and side-chains require trusted validators. In this paper, we propose Snappy, a novel solution that enables recipients, like merchants, to safely accept fast payments. In Snappy, all payments are on the chain, while small customer collaterals and moderate merchant collaterals act as payment guarantees. Besides receiving payments, merchants also act as statekeepers who collectively track and approve incoming payments using majority voting. In case of a double-spending attack, the victim merchant can recover lost funds either from the collateral of the malicious customer or a colluding statekeeper (merchant). Snappy overcomes the main problems of previous solutions: a single customer collateral can be used to shop with many merchants; merchant collaterals are independent of the number of customers; and validators do not have to be trusted. Our Ethereum prototype shows that safe, fast (<2 seconds) and cheap payments are possible on existing blockchains.

cs.CR↗

arXiv2019

Bounded Temporal Fairness for FIFO Financial Markets

Financial exchange operators cater to the needs of their users while simultaneously ensuring compliance with the financial regulations. In this work, we focus on the operators' commitment for fair treatment of all competing participants. We first discuss unbounded temporal fairness and then investigate its implementation and infrastructure requirements for exchanges. We find that these requirements can be fully met only under ideal conditions and argue that unbounded fairness in FIFO markets is unrealistic. To further support this claim, we analyse several real-world incidents and show that subtle implementation inefficiencies and technical optimizations suffice to give unfair advantages to a minority of the participants. We finally introduce, ε-fairness, a bounded definition of temporal fairness and discuss how it can be combined with non-continuous market designs to provide equal participant treatment with minimum divergence from the existing market operation.

q-fin.TR↗

arXiv2019

Libra: Fair Order-Matching for Electronic Financial Exchanges

While historically, economists have been primarily occupied with analyzing the behaviour of the markets, electronic trading gave rise to a new class of unprecedented problems associated with market fairness, transparency and manipulation. These problems stem from technical shortcomings that are not accounted for in the simple conceptual models used for theoretical market analysis. They, thus, call for more pragmatic market design methodologies that consider the various infrastructure complexities and their potential impact on the market procedures. First, we formally define temporal fairness and then explain why it is very difficult for order-matching policies to ensure it in continuous markets. Subsequently, we introduce a list of system requirements and evaluate existing "fair" market designs in various practical and adversarial scenarios. We conclude that they fail to retain their properties in the presence of infrastructure inefficiencies and sophisticated technical manipulation attacks. Based on these findings, we then introduce Libra, a "fair" policy that is resilient to gaming and tolerant of technical complications. Our security analysis shows that it is significantly more robust than existing designs, while Libra's deployment (in a live foreign currency exchange) validated both its considerably low impact on the operation of the market and its ability to reduce speed-based predatory trading.

cs.CR↗

arXiv2019

Market Manipulation as a Security Problem

Order matching systems form the backbone of modern equity exchanges, used by millions of investors daily. Thus, their operation is strictly controlled through numerous regulatory directives to ensure that markets are fair and transparent. Despite these efforts, market manipulation remains an open problem. In this work, we focus on a class of market manipulation techniques that exploit technical details and glitches in the operation of the exchanges (i.e., mechanical arbitrage). Such techniques are used by predatory traders with deep knowledge of the exchange's structure to gain an advantage over the other market participants. We argue that technical solutions to the problem of mechanical arbitrage have the potential to significantly thwart these practices. Our work provides the first overview of the threat landscape, models fair markets and their security assumptions, and discusses various mitigation measures.

q-fin.TR↗

arXiv2018

Towards Low-level Cryptographic Primitives for JavaCards

JavaCard is a multi-application security platform deployed to over twenty billion smartcards, used in applications ranging from secure payments to telecommunications. While the platform is a popular choice for established commercial use cases (e.g., SIM cards in telecommunication networks), it has notably low adoption rates in: 1) application scenarios requiring recently-standardized cryptographic algorithms, 2) research projects, and 3) open source initiatives. We attribute this to the restricted access to low-level cryptographic primitives (e.g., elliptic curve operations) and the lack of essential data types (e.g., Integers). While the underlying hardware has those capabilities, the JavaCard API does not provide calls for the corresponding functionality. Until now, the only available workaround was manufacturer-specific proprietary APIs that come with very restrictive non-disclosure agreements. In this paper, we introduce a methodology to efficiently derive essential data types and low-level cryptographic primitives from high-level operations. Our techniques are ideal for resource-constrained platforms, and make optimal use of the underlying hardware, while having a small memory footprint. We also introduce JCMathLib, which, to the best of our knowledge, is the first generic library for low-level cryptographic operations in JavaCards that does not rely on a proprietary API. Without any disclosure limitations, JCMathLib enables open code sharing, release of research prototypes and public and third-party code audits.

cs.CR↗

arXiv2018

Eavesdropping Whilst You're Shopping: Balancing Personalisation and Privacy in Connected Retail Spaces

Physical retailers, who once led the way in tracking with loyalty cards and `reverse appends', now lag behind online competitors. Yet we might be seeing these tables turn, as many increasingly deploy technologies ranging from simple sensors to advanced emotion detection systems, even enabling them to tailor prices and shopping experiences on a per-customer basis. Here, we examine these in-store tracking technologies in the retail context, and evaluate them from both technical and regulatory standpoints. We first introduce the relevant technologies in context, before considering privacy impacts, the current remedies individuals might seek through technology and the law, and those remedies' limitations. To illustrate challenging tensions in this space we consider the feasibility of technical and legal approaches to both a) the recent `Go' store concept from Amazon which requires fine-grained, multi-modal tracking to function as a shop, and b) current challenges in opting in or out of increasingly pervasive passive Wi-Fi tracking. The `Go' store presents significant challenges with its legality in Europe significantly unclear and unilateral, technical measures to avoid biometric tracking likely ineffective. In the case of MAC addresses, we see a difficult-to-reconcile clash between privacy-as-confidentiality and privacy-as-control, and suggest a technical framework which might help balance the two. Significant challenges exist when seeking to balance personalisation with privacy, and researchers must work together, including across the boundaries of preferred privacy definitions, to come up with solutions that draw on both technology and the legal frameworks to provide effective and proportionate protection. Retailers, simultaneously, must ensure that their tracking is not just legal, but worthy of the trust of concerned data subjects.

cs.CY↗

arXiv2017

A Touch of Evil: High-Assurance Cryptographic Hardware from Untrusted Components

The semiconductor industry is fully globalized and integrated circuits (ICs) are commonly defined, designed and fabricated in different premises across the world. This reduces production costs, but also exposes ICs to supply chain attacks, where insiders introduce malicious circuitry into the final products. Additionally, despite extensive post-fabrication testing, it is not uncommon for ICs with subtle fabrication errors to make it into production systems. While many systems may be able to tolerate a few byzantine components, this is not the case for cryptographic hardware, storing and computing on confidential data. For this reason, many error and backdoor detection techniques have been proposed over the years. So far all attempts have been either quickly circumvented, or come with unrealistically high manufacturing costs and complexity. This paper proposes Myst, a practical high-assurance architecture, that uses commercial off-the-shelf (COTS) hardware, and provides strong security guarantees, even in the presence of multiple malicious or faulty components. The key idea is to combine protective-redundancy with modern threshold cryptographic techniques to build a system tolerant to hardware trojans and errors. To evaluate our design, we build a Hardware Security Module that provides the highest level of assurance possible with COTS components. Specifically, we employ more than a hundred COTS secure crypto-coprocessors, verified to FIPS140-2 Level 4 tamper-resistance standards, and use them to realize high-confidentiality random number generation, key derivation, public key decryption and signing. Our experiments show a reasonable computational overhead (less than 1% for both Decryption and Signing) and an exponential increase in backdoor-tolerance as more ICs are added.

cs.CR↗