arXiv subjects

Georgios Karopoulos

Publications and source records attributed to Georgios Karopoulos.

8 recordsLinked to original sources

arXiv2026

Protecting On-Device AI Inference: A Systematic Review of Attacks and Defence Mechanisms

The need for secure and private Artificial Intelligence (AI) and Machine Learning (ML) on edge and mobile devices has increased the necessity of protecting the architecture of these systems from threats to both security and privacy. With an ever-increasing number of pre-trained AI models being used on mobile platforms for client-side inference, there are rising concerns about the risks associated with the theft/extraction of AI models, adversarial attacks on AI models, and data breaches. As a result of this trend, a variety of defence mechanisms have been proposed to protect against these threats. These include Trusted Execution Environments (TEEs), homomorphic encryption, obfuscation, and differential privacy, among others. However, current surveys largely focus on edge intelligence, which includes distributed training, and thus overlook security and privacy issues that are specific to on-device AI inference. To the best of our knowledge, this paper presents the first comprehensive review of threats and corresponding defence mechanisms targeting on-device inference. Our results show that the attack and defence literature are unbalanced: approximately one quarter of the surveyed attack papers focus on Intellectual Property (IP) attacks, whereas half of the defence solutions tackle the same issue. More importantly, some attack categories have no defence paper associated to them, such as adversarial attacks that account for roughly one third of the attack literature. This asymmetry between known attacks and available mitigations highlights clear opportunities for future research on securing on-device AI inference.

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arXiv2024

Assessing the Effectiveness of LLMs in Android Application Vulnerability Analysis

The increasing frequency of attacks on Android applications coupled with the recent popularity of large language models (LLMs) necessitates a comprehensive understanding of the capabilities of the latter in identifying potential vulnerabilities, which is key to mitigate the overall risk. To this end, the work at hand compares the ability of nine state-of-the-art LLMs to detect Android code vulnerabilities listed in the latest Open Worldwide Application Security Project (OWASP) Mobile Top 10. Each LLM was evaluated against an open dataset of over 100 vulnerable code samples, including obfuscated ones, assessing each model's ability to identify key vulnerabilities. Our analysis reveals the strengths and weaknesses of each LLM, identifying important factors that contribute to their performance. Additionally, we offer insights into context augmentation with retrieval-augmented generation (RAG) for detecting Android code vulnerabilities, which in turn may propel secure application development. Finally, while the reported findings regarding code vulnerability analysis show promise, they also reveal significant discrepancies among the different LLMs.

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arXiv2024

Keep your memory dump shut: Unveiling data leaks in password managers

Password management has long been a persistently challenging task. This led to the introduction of password management software, which has been around for at least 25 years in various forms, including desktop and browser-based applications. This work assesses the ability of two dozen password managers, 12 desktop applications, and 12 browser-plugins, to effectively protect the confidentiality of secret credentials in six representative scenarios. Our analysis focuses on the period during which a Password Manager (PM) resides in the RAM. Despite the sensitive nature of these applications, our results show that across all scenarios, only three desktop PM applications and two browser plugins do not store plaintext passwords in the system memory. Oddly enough, at the time of writing, only two vendors recognized the exploit as a vulnerability, reserving CVE-2023-23349, while the rest chose to disregard or underrate the issue.

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arXiv2023

Intrusion Detection based on Federated Learning: a systematic review

The evolution of cybersecurity is undoubtedly associated and intertwined with the development and improvement of artificial intelligence (AI). As a key tool for realizing more cybersecure ecosystems, Intrusion Detection Systems (IDSs) have evolved tremendously in recent years by integrating machine learning (ML) techniques for the detection of increasingly sophisticated cybersecurity attacks hidden in big data. However, these approaches have traditionally been based on centralized learning architectures, in which data from end nodes are shared with data centers for analysis. Recently, the application of federated learning (FL) in this context has attracted great interest to come up with collaborative intrusion detection approaches where data does not need to be shared. Due to the recent rise of this field, this work presents a complete, contemporary taxonomy for FL-enabled IDS approaches that stems from a comprehensive survey of the literature in the time span from 2018 to 2022. Precisely, our discussion includes an analysis of the main ML models, datasets, aggregation functions, as well as implementation libraries, which are employed by the proposed FL-enabled IDS approaches. On top of everything else, we provide a critical view of the current state of the research around this topic, and describe the main challenges and future directions based on the analysis of the literature and our own experience in this area.

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arXiv2023

Bypassing antivirus detection: old-school malware, new tricks

Being on a mushrooming spree since at least 2013, malware can take a large toll on any system. In a perpetual cat-and-mouse chase with defenders, malware writers constantly conjure new methods to hide their code so as to evade detection by security products. In this context, focusing on the MS Windows platform, this work contributes a comprehensive empirical evaluation regarding the detection capacity of popular, off-the-shelf antivirus and endpoint detection and response engines when facing legacy malware obfuscated via more or less uncommon but publicly known methods. Our experiments exploit a blend of seven traditional AV evasion techniques in 16 executables built in C++, Go, and Rust. Furthermore, we conduct an incipient study regarding the ability of the ChatGPT chatbot in assisting threat actors to produce ready-to-use malware. The derived results in terms of detection rate are highly unexpected: approximately half of the 12 tested AV engines were able to detect less than half of the malware variants, four AVs exactly half of the variants, while only two of the rest detected all but one of the variants.

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arXiv2022

A hands-on gaze on HTTP/3 security through the lens of HTTP/2 and a public dataset

Following QUIC protocol ratification on May 2021, the third major version of the Hypertext Transfer Protocol, namely HTTP/3, was published around one year later in RFC 9114. In light of these consequential advancements, the current work aspires to provide a full-blown coverage of the following issues, which to our knowledge have received feeble or no attention in the literature so far. First, we provide a complete review of attacks against HTTP/2, and elaborate on if and in which way they can be migrated to HTTP/3. Second, through the creation of a testbed comprising the at present six most popular HTTP/3-enabled servers, we examine the effectiveness of a quartet of attacks, either stemming directly from the HTTP/2 relevant literature or being entirely new. This scrutiny led to the assignment of at least one CVE ID with a critical base score by MITRE. No less important, by capitalizing on a realistic, abundant in devices testbed, we compiled a voluminous, labeled corpus containing traces of ten diverse attacks against HTTP and QUIC services. An initial evaluation of the dataset mainly by means of machine learning techniques is included as well. Given that the 30 GB dataset is made available in both pcap and CSV formats, forthcoming research can easily take advantage of any subset of features, contingent upon the specific network topology and configuration.

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arXiv2021

Sharing pandemic vaccination certificates through blockchain: Case study and performance evaluation

This work proposes a scalable, blockchain-based platform for the secure sharing of COVID-19 or other disease vaccination certificates. As an indicative use case, we simulate a large-scale deployment by considering the countries of the European Union. The proposed platform is evaluated through extensive simulations in terms of computing resource usage, network response time and bandwidth. Based on the results, the proposed scheme shows satisfactory performance across all major evaluation criteria, suggesting that it can set the pace for real implementations. Vis-à-vis the related work, the proposed platform is novel, especially through the prism of a large-scale, full-fledged implementation and its assessment.

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arXiv2020

Demystifying COVID-19 digital contact tracing: A survey on frameworks and mobile apps

The coronavirus pandemic is a new reality and it severely affects the modus vivendi of the international community. In this context, governments are rushing to devise or embrace novel surveillance mechanisms and monitoring systems to fight the outbreak. The development of digital tracing apps, which among others are aimed at automatising and globalising the prompt alerting of individuals at risk in a privacy-preserving manner is a prominent example of this ongoing effort. Very promptly, a number of digital contact tracing architectures has been sprouted, followed by relevant app implementations adopted by governments worldwide. Bluetooth, and specifically its Low Energy (BLE) power-conserving variant has emerged as the most promising short-range wireless network technology to implement the contact tracing service. This work offers the first to our knowledge, full-fledged review of the most concrete contact tracing architectures proposed so far in a global scale. This endeavour does not only embrace the diverse types of architectures and systems, namely centralised, decentralised, or hybrid, but it equally addresses the client side, i.e., the apps that have been already deployed in Europe by each country. There is also a full-spectrum adversary model section, which does not only amalgamate the previous work in the topic, but also brings new insights and angles to contemplate upon.

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