arXiv subjects

Constantinos Patsakis

Publications and source records attributed to Constantinos Patsakis.

At least 37 records · Page 2Linked to original sources

arXiv2023

Hello Me, Meet the Real Me: Audio Deepfake Attacks on Voice Assistants

The radical advances in telecommunications and computer science have enabled a myriad of applications and novel seamless interaction with computing interfaces. Voice Assistants (VAs) have become a norm for smartphones, and millions of VAs incorporated in smart devices are used to control these devices in the smart home context. Previous research has shown that they are prone to attacks, leading vendors to countermeasures. One of these measures is to allow only a specific individual, the device's owner, to perform possibly dangerous tasks, that is, tasks that may disclose personal information, involve monetary transactions etc. To understand the extent to which VAs provide the necessary protection to their users, we experimented with two of the most widely used VAs, which the participants trained. We then utilised voice synthesis using samples provided by participants to synthesise commands that were used to trigger the corresponding VA and perform a dangerous task. Our extensive results showed that more than 30\% of our deepfake attacks were successful and that there was at least one successful attack for more than half of the participants. Moreover, they illustrate statistically significant variation among vendors and, in one case, even gender bias. The outcomes are rather alarming and require the deployment of further countermeasures to prevent exploitation, as the number of VAs in use is currently comparable to the world population.

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arXiv2022

From NEA and NIA to NESAS and SCAS: Demystifying the 5G Security Ecosystem

Despite the numerous pompous statements regarding 5G, it is indisputable that 5G creates a radical shift in telecommunications. The main reason is that 5G is an enabler of numerous applications we have long envisioned and either simulated or implemented in test environments, partially or on a smaller scale. 5G will soon unlock the potential of smart cities, industry 4.0, and IoT, to name a few. However, a crucial question is how much we can trust this technology. Since this technology will soon become the core infrastructure for all of the above, it is critical to understand the fundamental security mechanisms that comprise this technology and the guarantees they provide to assess the potential risks we are exposed to. This work follows a non-technical yet bottom-up approach to introduce the reader to the core security mechanisms and establish a baseline for the security of 5G, to demystify the principal notions and processes. Based on the above, we streamline future directions and highlight possible threats.

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arXiv2022

AiCEF: An AI-assisted Cyber Exercise Content Generation Framework Using Named Entity Recognition

Content generation that is both relevant and up to date with the current threats of the target audience is a critical element in the success of any Cyber Security Exercise (CSE). Through this work, we explore the results of applying machine learning techniques to unstructured information sources to generate structured CSE content. The corpus of our work is a large dataset of publicly available cyber security articles that have been used to predict future threats and to form the skeleton for new exercise scenarios. Machine learning techniques, like named entity recognition (NER) and topic extraction, have been utilised to structure the information based on a novel ontology we developed, named Cyber Exercise Scenario Ontology (CESO). Moreover, we used clustering with outliers to classify the generated extracted data into objects of our ontology. Graph comparison methodologies were used to match generated scenario fragments to known threat actors' tactics and help enrich the proposed scenario accordingly with the help of synthetic text generators. CESO has also been chosen as the prominent way to express both fragments and the final proposed scenario content by our AI-assisted Cyber Exercise Framework (AiCEF). Our methodology was put to test by providing a set of generated scenarios for evaluation to a group of experts to be used as part of a real-world awareness tabletop exercise.

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arXiv2022

SoK: Cross-border Criminal Investigations and Digital Evidence

Digital evidence underpin the majority of crimes as their analysis is an integral part of almost every criminal investigation. Even if we temporarily disregard the numerous challenges in the collection and analysis of digital evidence, the exchange of the evidence among the different stakeholders has many thorny issues. Of specific interest are cross-border criminal investigations as the complexity is significantly high due to the heterogeneity of legal frameworks which beyond time bottlenecks can also become prohibiting. The aim of this article is to analyse the current state of practice of cross-border investigations considering the efficacy of current collaboration protocols along with the challenges and drawbacks to be overcome. Further to performing a legally-oriented research treatise, we recall all the challenges raised in the literature and discuss them from a more practical yet global perspective. Thus, this article paves the way to enabling practitioners and stakeholders to leverage horizontal strategies to fill in the identified gaps timely and accurately.

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arXiv2022

On the Effectiveness of Binary Emulation in Malware Classification

Malware authors are continuously evolving their code base to include counter-analysis methods that can significantly hinder their detection and blocking. While the execution of malware in a sandboxed environment may provide a lot of insightful feedback about what the malware actually does in a machine, anti-virtualisation and hooking evasion methods may allow malware to bypass such detection methods. The main objective of this work is to complement sandbox execution with the use of binary emulation frameworks. The core idea is to exploit the fact that binary emulation frameworks may quickly test samples quicker than a sandbox environment as they do not need to open a whole new virtual machine to execute the binary. While with this approach, we lose the granularity of the data that can be collected through a sandbox, due to scalability issues, one may need to simply determine whether a file is malicious or to which malware family it belongs. To this end, we record the API calls that are performed and use them to explore the efficacy of using them as features for binary and multiclass classification. Our extensive experiments with real-world malware illustrate that this approach is very accurate, achieving state-of-the art outcomes with a statistically robust set of classification experiments while simultaneously having a relatively low computational overhead compared to traditional sandbox approaches. In fact, we compare the binary analysis results with a commercial sandbox, and our classification outperforms it at the expense of the fine-grained results that a sandbox provides.

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arXiv2022

An Empirical Assessment of Endpoint Security Systems Against Advanced Persistent Threats Attack Vectors

Advanced persistent threats pose a significant challenge for blue teams as they apply various attacks over prolonged periods, impeding event correlation and their detection. In this work, we leverage various diverse attack scenarios to assess the efficacy of EDRs and other endpoint security solutions against detecting and preventing APTs. Our results indicate that there is still a lot of room for improvement as state of the art endpoint security systems fail to prevent and log the bulk of the attacks that are reported in this work. Additionally, we discuss methods to tamper with the telemetry providers of EDRs, allowing an adversary to perform a more stealth attack.

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arXiv2022

Research trends, challenges, and emerging topics of digital forensics: A review of reviews

Due to its critical role in cybersecurity, digital forensics has received significant attention from researchers and practitioners alike. The ever increasing sophistication of modern cyberattacks is directly related to the complexity of evidence acquisition, which often requires the use of several technologies. To date, researchers have presented many surveys and reviews on the field. However, such articles focused on the advances of each particular domain of digital forensics individually. Therefore, while each of these surveys facilitates researchers and practitioners to keep up with the latest advances in a particular domain of digital forensics, the global perspective is missing. Aiming to fill this gap, we performed a qualitative review of reviews in the field of digital forensics, determined the main topics on digital forensics topics and identified their main challenges. Our analysis provides enough evidence to prove that the digital forensics community could benefit from closer collaborations and cross-topic research, since it is apparent that researchers and practitioners are trying to find solutions to the same problems in parallel, sometimes without noticing it.

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arXiv2021

EtherClue: Digital investigation of attacks on Ethereum smart contracts

Programming errors in Ethereum smart contracts can result in catastrophic financial losses from stolen cryptocurrency. While vulnerability detectors can prevent vulnerable contracts from being deployed, this does not mean that such contracts will not be deployed. Once a vulnerable contract is instantiated on the blockchain and becomes the target of attacks, the identification of exploit transactions becomes indispensable in assessing whether it has been actually exploited and identifying which malicious or subverted accounts were involved. In this work, we study the problem of post-factum investigation of Ethereum attacks using Indicators of Compromise (IoCs) specially crafted for use in the blockchain. IoC definitions need to capture the side-effects of successful exploitation in the context of the Ethereum blockchain. Therefore, we define a model for smart contract execution, comprising multiple abstraction levels that mirror the multiple views of code execution on a blockchain. Subsequently, we compare IoCs defined across the different levels in terms of their effectiveness and practicality through EtherClue, a prototype tool for investigating Ethereum security incidents. Our results illustrate that coarse-grained IoCs defined over blocks of transactions can detect exploit transactions with less computation; however, they are contract-specific and suffer from false negatives. On the other hand, fine-grained IoCs defined over virtual machine instructions can avoid these pitfalls at the expense of increased computation which are nevertheless applicable for practical use.

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arXiv2021

Intercepting Hail Hydra: Real-Time Detection of Algorithmically Generated Domains

A crucial technical challenge for cybercriminals is to keep control over the potentially millions of infected devices that build up their botnets, without compromising the robustness of their attacks. A single, fixed C&C server, for example, can be trivially detected either by binary or traffic analysis and immediately sink-holed or taken-down by security researchers or law enforcement. Botnets often use Domain Generation Algorithms (DGAs), primarily to evade take-down attempts. DGAs can enlarge the lifespan of a malware campaign, thus potentially enhancing its profitability. They can also contribute to hindering attack accountability. In this work, we introduce HYDRAS, the most comprehensive and representative dataset of Algorithmically-Generated Domains (AGD) available to date. The dataset contains more than 100 DGA families, including both real-world and adversarially designed ones. We analyse the dataset and discuss the possibility of differentiating between benign requests (to real domains) and malicious ones (to AGDs) in real-time. The simultaneous study of so many families and variants introduces several challenges; nonetheless, it alleviates biases found in previous literature employing small datasets which are frequently overfitted, exploiting characteristic features of particular families that do not generalise well.We thoroughly compare our approach with the current state-of-the-art and highlight some methodological shortcomings in the actual state of practice. The outcomes obtained show that our proposed approach significantly outperforms the current state-of-the-art in terms of both classification performance and efficiency.

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arXiv2021

The Cynicism of Modern Cybercrime: Automating the Analysis of Surface Web Marketplaces

Cybercrime is continuously growing in numbers and becoming more sophisticated. Currently, there are various monetisation and money laundering methods, creating a huge, underground economy worldwide. A clear indicator of these activities is online marketplaces which allow cybercriminals to trade their stolen assets and services. While traditionally these marketplaces are available through the dark web, several of them have emerged in the surface web. In this work, we perform a longitudinal analysis of a surface web marketplace. The information was collected through targeted web scrapping that allowed us to identify hundreds of merchants' profiles for the most widely used surface web marketplaces. In this regard, we discuss the products traded in these markets, their prices, their availability, and the exchange currency. This analysis is performed in an automated way through a machine learning-based pipeline, allowing us to quickly and accurately extract the needed information. The outcomes of our analysis evince that illegal practices are leveraged in surface marketplaces and that there are not effective mechanisms towards their takedown at the time of writing.

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arXiv2021

Who Watches the New Watchmen? The Challenges for Drone Digital Forensics Investigations

The technological advance of drone technology has augmented the existing capabilities of flying vehicles rendering them a valuable asset of the modern society. As more drones are expected to occupy the airspace in the near future, security-related incidents, either malicious acts or accidents, will increase as well. The forensics analysis of a security incident is essential, as drones are flying above populated areas and have also been weaponised from radical forces and perpetrators. Thus, it is an imperative need to establish a Drone Digital Forensics Investigation Framework and standardise the processes of collecting and processing such evidence. Although there are numerous drone platforms in the market, the same principles apply to all of them; just like mobile phones. Nevertheless, due to the nature of drones, standardised forensics procedures to date do not manage to address the required processes and challenges that such investigations pose. Acknowledging this need, we detail the unique characteristics of drones and the gaps in existing methodologies and standards, showcasing that there are fundamental issues in terms of their forensics analysis from various perspectives, ranging from operational and procedural ones, and escalate to manufacturers, as well as legal restrictions. The above creates a very complex environment where coordinated actions must be made among the key stakeholders. Therefore, this work paves the way to address these challenges by identifying the main issues, their origins, and the needs in the field by performing a thorough review of the literature and a gap analysis.

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arXiv2021

Python and Malware: Developing Stealth and Evasive Malware Without Obfuscation

With the continuous rise of malicious campaigns and the exploitation of new attack vectors, it is necessary to assess the efficacy of the defensive mechanisms used to detect them. To this end, the contribution of our work is twofold. First, it introduces a new method for obfuscating malicious code to bypass all static checks of multi-engine scanners, such as VirusTotal. Interestingly, our approach to generating the malicious executables is not based on introducing a new packer but on the augmentation of the capabilities of an existing and widely used tool for packaging Python, PyInstaller but can be used for all similar packaging tools. As we prove, the problem is deeper and inherent in almost all antivirus engines and not PyInstaller specific. Second, our work exposes significant issues of well-known sandboxes that allow malware to evade their checks. As a result, we show that stealth and evasive malware can be efficiently developed, bypassing with ease state of the art malware detection tools without raising any alert.

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arXiv2021

Analysis and Correlation of Visual Evidence in Campaigns of Malicious Office Documents

Many malware campaigns use Microsoft (MS) Office documents as droppers to download and execute their malicious payload. Such campaigns often use these documents because MS Office is installed in billions of devices and that these files allow the execution of arbitrary VBA code. Recent versions of MS Office prevent the automatic execution of VBA macros, so malware authors try to convince users into enabling the content via images that, e.g. forge system or technical errors. In this work, we leverage these visual elements to construct lightweight malware signatures that can be applied with minimal effort. We test and validate our approach using an extensive database of malware samples and identify correlations between different campaigns that illustrate that some campaigns are either using the same tools or that there is some collaboration between them.

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arXiv2021

Exploiting Statistical and Structural Features for the Detection of Domain Generation Algorithms

Nowadays, malware campaigns have reached a high level of sophistication, thanks to the use of cryptography and covert communication channels over traditional protocols and services. In this regard, a typical approach to evade botnet identification and takedown mechanisms is the use of domain fluxing through the use of Domain Generation Algorithms (DGAs). These algorithms produce an overwhelming amount of domain names that the infected device tries to communicate with to find the Command and Control server, yet only a small fragment of them is actually registered. Due to the high number of domain names, the blacklisting approach is rendered useless. Therefore, the botmaster may pivot the control dynamically and hinder botnet detection mechanisms. To counter this problem, many security mechanisms result in solutions that try to identify domains from a DGA based on the randomness of their name. In this work, we explore hard to detect families of DGAs, as they are constructed to bypass these mechanisms. More precisely, they are based on the use of dictionaries so the domains seem to be user-generated. Therefore, the corresponding generated domains pass many filters that look for, e.g. high entropy strings. To address this challenge, we propose an accurate and efficient probabilistic approach to detect them. We test and validate the proposed solution through extensive experiments with a sound dataset containing all the wordlist-based DGA families that exhibit this behaviour and compare it with other state-of-the-art methods, practically showing the efficacy and prevalence of our proposal.

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arXiv2020

Analysing the fall 2020 Emotet campaign

In this report, we analyse the latest campaign of Emotet that had a significant impact in several countries worldwide. We leverage the data of a specifically crafted dataset, which contains emails, documents, executables and domains from the latest campaign. The goal is to analyse the attack vector, map the infrastructure used in various stages of the campaign and perform a surface analysis of Emotet's malicious payloads to assess their potential impact.

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arXiv2020

Inside the x-rated world of "premium" social media accounts

During the last few years, there has been an upsurge of social media influencers who are part of the adult entertainment industry, referred to as Performers. To monetize their online presence, Performers often engage in practices which violate community guidelines of social media, such as selling subscriptions for accessing their private "premium" social media accounts, where they distribute adult content. In this paper, we collect and analyze data from FanCentro, an online marketplace where Performers can sell adult content and subscriptions to private accounts in platforms like Snapchat and Instagram. Our work aims to shed light on the semi-illicit adult content market layered on the top of popular social media platforms and its offerings, as well as to profile the demographics, activity and content produced by Performers.

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arXiv2020

A traceability and auditing framework for electronic equipment reverse logistics based on blockchain: the case of mobile phones

Human beings produce electronic waste (e-waste) at an unprecedented pace. Mobile phones and other inter-connected smart devices make a significant contribution to the generation of e-waste. Reverse logistics (RL) activities play an essential role in managing mobile phones during their end-of-life. However, remanufacturing and/or refurbishing of mobile phones might prove difficult not only from an operational point of view but also from a data management and privacy perspective (due to privacy-related regulatory frameworks like the EU General Data Protection Regulation directive). In this paper, we propose a distributed trustless and secure framework for electronic equipment RL activities based on blockchain technology. We consider the remanufacturing/refurbishing recovery option for mobile phones, and we develop an autonomous and effective back-end data sharing architecture based on smart contracts/blockchain technology for keeping track of all the remanufacturing/refurbishing processes. For demonstrating the applicability of our approach, we develop a functional set of smart contracts and a local private blockchain. The benefits of our framework are further discussed, along with fruitful areas for future research.

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arXiv2020

SoK: Blockchain Solutions for Forensics

As the digitization of information-intensive processes gains momentum in nowadays, the concern is growing about how to deal with the ever-growing problem of cybercrime. To this end, law enforcement officials and security firms use sophisticated digital forensics techniques for analyzing and investigating cybercrimes. However, multi-jurisdictional mandates, interoperability issues, the massive amount of evidence gathered (multimedia, text etc.) and multiple stakeholders involved (law enforcement agencies, security firms etc.) are just a few among the various challenges that hinder the adoption and implementation of sound digital forensics schemes. Blockchain technology has been recently proposed as a viable solution for developing robust digital forensics mechanisms. In this paper, we provide an overview and classification of the available blockchain-based digital forensic tools, and we further describe their main features. We also offer a thorough analysis of the various benefits and challenges of the symbiotic relationship between blockchain technology and the current digital forensics approaches, as proposed in the available literature. Based on the findings, we identify various research gaps, and we suggest future research directions that are expected to be of significant value both for academics and practitioners in the field of digital forensics.

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