arXiv subjects

Fran Casino

Publications and source records attributed to Fran Casino.

At least 19 recordsLinked to original sources

arXiv2026

A Data-Driven Analysis of Infostealer Malware Victims

Infostealer malware infects devices worldwide and harvests their most sensitive contents: credentials, browser sessions, private keys, and access certificates. Yet its impact on victims remains difficult to study without an ethical, legal, and curated research dataset. To close this gap, we build a privacy-preserving pipeline that turns illicitly sourced infostealer logs into a reproducible research artifact, minimizing sensitive data while preserving measurement utility, and use it to construct a dataset of 170,298 victims from logs of multiple infostealer families. Analyzing these victims, we find that the most compromised services mirror the world's most popular platforms, with gaming and entertainment services strongly overrepresented. Within the sample we identify compromised credentials for high-value organizations, including law-enforcement domains, government and military services, and all eight Ivy League universities, as well as substantial exposure of security-critical infrastructure and of financial, remote-access, and development platforms. Victims also show widespread credential reuse and significant revictimization risk, overlapping with phishing and ransomware victim populations. We release the first anonymized victim-level infostealer dataset under controlled access to enable ethical, privacy-preserving, and reproducible research on information security and victim behavior.

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arXiv2026

Exploring the connection between coding habits and cognitive styles in malware developers

Malware research primarily studies the results, the methods, and the impact. Even from an offensive security perspective, what is examined is the method, not the development strategy of the offender. This study investigates the behavioral signatures and coding patterns embedded in the malware source code. By analyzing a large corpus of leaked malware code and comparing it with carefully selected benign open-source software, we apply static application security testing and compute multiple software metrics. Based on cognitive psychology and criminological theories, our work interprets differences in code structure and quality as behavioral indicators, reflecting distinct motivational structures, risk tolerances, and development strategies of malware authors compared to benign software developers. Our findings reveal that malware code is generally smaller, less documented, and exhibits higher cyclomatic complexity per function, with reduced use of abstraction mechanisms such as classes and closures. Vulnerability analysis further reveals that malware exhibits more issues of the types that benign code typically avoids, suggesting a minimal investment in secure development practices. These patterns imply a development style optimized for expedience, operational secrecy, and evasion rather than long-term maintainability. Nonetheless, the code quality metrics indicate that it does not deviate significantly from benign software enough to be distinctive. By framing code metrics as proxies for behavioral signals and strategic choices, we demonstrate how quantitative software analysis can enrich behavioral cybersecurity research, offering new insights into the practices and priorities of malware developers. Our results pave the way for further research in the behavioral profiling of cyber offenders.

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arXiv2025

Analysing Multidisciplinary Approaches to Fight Large-Scale Digital Influence Operations

Crime as a Service (CaaS) has evolved from isolated criminal incidents to a broad spectrum of illicit activities, including social media manipulation, foreign information manipulation and interference (FIMI), and the sale of disinformation toolkits. This article analyses how threat actors exploit specialised infrastructures ranging from proxy and VPN services to AI-driven generative models to orchestrate large-scale opinion manipulation. Moreover, it discusses how these malicious operations monetise the virality of social networks, weaponise dual-use technologies, and leverage user biases to amplify polarising narratives. In parallel, it examines key strategies for detecting, attributing, and mitigating such campaigns by highlighting the roles of blockchain-based content verification, advanced cryptographic proofs, and cross-disciplinary collaboration. Finally, the article highlights that countering disinformation demands an integrated framework that combines legal, technological, and societal efforts to address a rapidly adapting and borderless threat

cs.CY↗

arXiv2025

Inside LockBit: Technical, Behavioral, and Financial Anatomy of a Ransomware Empire

LockBit has evolved from an obscure Ransomware-as-a-Service newcomer in 2019 to the most prolific ransomware franchise of 2024. Leveraging a recently leaked MySQL dump of the gang's management panel, this study offers an end-to-end reconstruction of LockBit's technical, behavioral, and financial apparatus. We recall the family's version timeline and map its tactics, techniques, and procedures to MITRE ATT&CK, highlighting the incremental hardening that distinguishes LockBit 3.0 from its predecessors. We then analyze 51 negotiation chat logs using natural-language embeddings and clustering to infer a canonical interaction playbook, revealing recurrent rhetorical stages that underpin the double-extortion strategy. Finally, we trace 19 Bitcoin addresses related to ransom payment chains, revealing two distinct patterns based on different laundering phases. In both cases, a small portion of the ransom is immediately split into long-lived addresses (presumably retained by the group as profit and to finance further operations) while the remainder is ultimately aggregated into two high-volume addresses before likely being sent to the affiliate. These two collector addresses appear to belong to distinct exchanges, each processing over 200k BTC. The combined evidence portrays LockBit as a tightly integrated criminal service whose resilience rests on rapid code iteration, script-driven social engineering, and industrial-scale cash-out pipelines.

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arXiv2024

The potential of LLM-generated reports in DevSecOps

Alert fatigue is a common issue faced by software teams using the DevSecOps paradigm. The overwhelming number of warnings and alerts generated by security and code scanning tools, particularly in smaller teams where resources are limited, leads to desensitization and diminished responsiveness to security warnings, potentially exposing systems to vulnerabilities. This paper explores the potential of LLMs in generating actionable security reports that emphasize the financial impact and consequences of detected security issues, such as credential leaks, if they remain unaddressed. A survey conducted among developers indicates that LLM-generated reports significantly enhance the likelihood of immediate action on security issues by providing clear, comprehensive, and motivating insights. Integrating these reports into DevSecOps workflows can mitigate attention saturation and alert fatigue, ensuring that critical security warnings are addressed effectively.

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arXiv2024

Outside the Comfort Zone: Analysing LLM Capabilities in Software Vulnerability Detection

The significant increase in software production driven by automation and faster development lifecycles has resulted in a corresponding surge in software vulnerabilities. In parallel, the evolving landscape of software vulnerability detection, highlighting the shift from traditional methods to machine learning and large language models (LLMs), provides massive opportunities at the cost of resource-demanding computations. This paper thoroughly analyses LLMs' capabilities in detecting vulnerabilities within source code by testing models beyond their usual applications to study their potential in cybersecurity tasks. We evaluate the performance of six open-source models that are specifically trained for vulnerability detection against six general-purpose LLMs, three of which were further fine-tuned on a dataset that we compiled. Our dataset, alongside five state-of-the-art benchmark datasets, were used to create a pipeline to leverage a binary classification task, namely classifying code into vulnerable and non-vulnerable. The findings highlight significant variations in classification accuracy across benchmarks, revealing the critical influence of fine-tuning in enhancing the detection capabilities of small LLMs over their larger counterparts, yet only in the specific scenarios in which they were trained. Further experiments and analysis also underscore the issues with current benchmark datasets, particularly around mislabeling and their impact on model training and performance, which raises concerns about the current state of practice. We also discuss the road ahead in the field suggesting strategies for improved model training and dataset curation.

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arXiv2024

The Malware as a Service ecosystem

The goal of this chapter is to illuminate the operational frameworks, key actors, and significant cybersecurity implications of the Malware as a Service (MaaS) ecosystem. Highlighting the transformation of malware proliferation into a service-oriented model, the chapter discusses how MaaS democratises access to sophisticated cyberattack capabilities, enabling even those with minimal technical knowledge to execute catastrophic cyberattacks. The discussion extends to the roles within the MaaS ecosystem, including malware developers, affiliates, initial access brokers, and the essential infrastructure providers that support these nefarious activities. The study emphasises the profound challenges MaaS poses to traditional cybersecurity defences, rendered ineffective against the constantly evolving and highly adaptable threats generated by MaaS platforms. With the increase in malware sophistication, there is a parallel call for a paradigm shift in defensive strategies, advocating for dynamic analysis, behavioural detection, and the integration of AI and machine learning techniques. By exploring the intricacies of the MaaS ecosystem, including the economic motivations driving its growth and the blurred lines between legitimate service models and cyber crime, the chapter presents a comprehensive overview intended to foster a deeper understanding among researchers and cybersecurity professionals. The ultimate goal is to aid in developing more effective strategies for combating the spread of commoditised malware threats and safeguarding against the increasing accessibility and scalability of cyberattacks facilitated by the MaaS model.

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arXiv2024

Assessing LLMs in Malicious Code Deobfuscation of Real-world Malware Campaigns

The integration of large language models (LLMs) into various pipelines is increasingly widespread, effectively automating many manual tasks and often surpassing human capabilities. Cybersecurity researchers and practitioners have recognised this potential. Thus, they are actively exploring its applications, given the vast volume of heterogeneous data that requires processing to identify anomalies, potential bypasses, attacks, and fraudulent incidents. On top of this, LLMs' advanced capabilities in generating functional code, comprehending code context, and summarising its operations can also be leveraged for reverse engineering and malware deobfuscation. To this end, we delve into the deobfuscation capabilities of state-of-the-art LLMs. Beyond merely discussing a hypothetical scenario, we evaluate four LLMs with real-world malicious scripts used in the notorious Emotet malware campaign. Our results indicate that while not absolutely accurate yet, some LLMs can efficiently deobfuscate such payloads. Thus, fine-tuning LLMs for this task can be a viable potential for future AI-powered threat intelligence pipelines in the fight against obfuscated malware.

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arXiv2023

CBDC-AquaSphere: Interoperable Central Bank Digital Currency Built on Trusted Computing and Blockchain

The adoption of decentralized, tamper-proof ledger systems is paving the way for new applications and opportunities in different contexts. While most research aims to improve their scalability, privacy, and governance issues, interoperability has received less attention. Executing transactions across various blockchains is notably instrumental in unlocking the potential of novel applications, particularly in the financial sector, where their potential would otherwise be significantly diminished. Therefore, interoperable ledgers are crucial to ensure the expansion and further adoption of such a technology in various contexts. In this paper, we present a protocol that uses a combination of trusted execution environment (TEE) and blockchains to enable interoperability over independent semi-centralized CBDC ledgers, guaranteeing the atomicity of inter-bank transfers. Our interoperability protocol uses a custom adaptation of atomic swap protocol and is executed by any pair of CBDC instances to realize a one-way transfer. It ensures features such as atomicity, verifiability, correctness, censorship resistance, and privacy while offering high scalability in terms of the number of CBDC instances. Our approach enables to possible deployment scenarios that can be combined: (1) CBDC instances represent central banks of multiple countries, and (2) CBDC instances represent the set of retail banks and a paramount central bank of a single country. We provide a detailed description of our protocol as well as an extensive analysis of its benefits, features, and security. In this WIP paper, we made a proof-of-concept implementation and made a partial evaluation, while the more extensive evaluation will be made in our future work.

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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

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

A blockchain-based Forensic Model for Financial Crime Investigation: The Embezzlement Scenario

The financial crime landscape is evolving along with the digitization in financial services. In this context, laws and regulations cannot efficiently cope with a fast-moving industry such as finance, which translates in late adoption of measures and legal voids, providing a fruitful landscape for malicious actors. In parallel, blockchain technology and its promising features such as immutability, verifiability, and authentication, enhance the opportunities of financial forensics. In this paper, we focus on an embezzlement scheme and we provide a forensic-by-design methodology for its investigation. In addition, the feasibility and adaptability of our approach can be extended and embrace digital investigations on other types of schemes. We provide a functional implementation based on smart contracts and we integrate standardised forensic flows and chain of custody preservation mechanisms. Finally, we discuss the benefits and challenges of the symbiotic relationship between blockchain and financial investigations, along with future research directions.

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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

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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