Constantinos Patsakis

Evgenios Gkritsis, Constantinos Patsakis, George Stergiopoulos

Malware analysis systems, including dynamic-analysis sandboxes and digital forensics and incident response (DFIR) platforms, rely on telemetry pipelines comprising collection agents, serializers, and database backends to capture and present program behavior to analysts. We show that these data-handling components constitute an exploitable attack surface that can lead to denial-of-analysis (DoA) states without disabling sensors or requiring elevated privileges. We present Telemetry Complexity Attacks (TCAs), a new class of vulnerabilities that exploit mismatches between unbounded collection mechanisms and bounded processing capabilities. Our method recursively spawns child processes to generate deeply nested and oversized objects that stress serialization and storage boundaries, as well as visualization layers, e.g., JSON/BSON depth and size limits. Depending on the product, this leads to truncated or missing behavioral reports, rejected database inserts, serializer recursion and size errors, and unresponsive dashboards, with some cases also exhibiting normal malicious execution that was not recorded or presented to analysts. We evaluate our technique against 18 commercial and open-source malware analysis platforms and endpoint detection and response (EDR) solutions. Seven products fail at different stages of the telemetry pipeline; two CVE identifiers have been assigned (CVE-61301 and CVE-61303); one more is pending; one has been assigned to an underlying library, and others have issued patches or configuration changes. We discuss root causes and propose mitigation strategies to prevent DoA attacks triggered by adversarial telemetry.

Daniel Gibert, Nikolaos Totosis, Constantinos Patsakis et al.

The proliferation of malware, particularly through the use of packing, presents a significant challenge to static analysis and signature-based malware detection techniques. The application of packing to the original executable code renders extracting meaningful features and signatures challenging. To deal with the increasing amount of malware in the wild, researchers and anti-malware companies started harnessing machine learning capabilities with very promising results. However, little is known about the effects of packing on static machine learning-based malware detection and classification systems. This work addresses this gap by investigating the impact of packing on the performance of static machine learning-based models used for malware detection and classification, with a particular focus on those using visualisation techniques. To this end, we present a comprehensive analysis of various packing techniques and their effects on the performance of machine learning-based detectors and classifiers. Our findings highlight the limitations of current static detection and classification systems and underscore the need to be proactive to effectively counteract the evolving tactics of malware authors.

George Karantzas, Constantinos Patsakis

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.

Constantinos Patsakis, Anargyros Chrysanthou

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.

Fran Casino, Nikolaos Lykousas, Ivan Homoliak et al.

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.

Constantinos Patsakis, Fran Casino, Nikolaos Lykousas et al.

The current landscape of the core Internet technologies shows considerable centralisation with the big tech companies controlling the vast majority of traffic and services. This has sparked a wide range of decentralisation initiatives with perhaps the most profound and successful being the blockchain technology. In the past years, a core Internet infrastructure, domain name system (DNS), is being revised mainly due to its inherent security and privacy issues. One of the proposed panaceas is Blockchain-based DNS, which claims to solve many issues of traditional DNS. However, this does not come without security concerns and issues, as any introduction and adoption of a new technology does - let alone a disruptive one such as blockchain. In this work, we discuss a number of associated threats, including emerging ones, and we validate many of them with real-world data. In this regard, we explore a part of the blockchain DNS ecosystem in terms of the browser extensions using such technologies, the chain itself (Namecoin and Emercoin), the domains, and users which have been registered in both platforms. Finally, we provide some countermeasures to address the identified threats, and we propose a fertile common ground for further research.

Constantinos Patsakis, Fran Casino, Vasilios Katos

There is a continuous increase in the sophistication that modern malware exercise in order to bypass the deployed security mechanisms. A typical approach to evade the identification and potential takedown of a botnet command and control server is domain fluxing through the use of Domain Generation Algorithms (DGAs). These algorithms produce a vast amount of domain names that the infected device tries to communicate with to find the C&C server, yet only a small fragment of them is actually registered. This allows the botmaster to pivot the control and make the work of seizing the botnet control rather difficult. Current state of the art and practice considers that the DNS queries performed by a compromised device are transparent to the network administrator and therefore can be monitored, analysed, and blocked. In this work, we showcase that the latter is a strong assumption as malware could efficiently hide its DNS queries using covert and/or encrypted channels bypassing the detection mechanisms. To this end, we discuss possible mitigation measures based on traffic analysis to address the new challenges that arise f

Eugenia Politou, Fran Casino, Efthimios Alepis et al.

Blockchain's evolution during the past decade is astonishing: from bitcoin to over 2.000 altcoins, and from decentralised electronic payments to transactions programmable by smart contracts and complex tokens governed by decentralised organisations. While the new generation of blockchain applications is still evolving, blockchain's technical characteristics are also advancing. Yet, immutability, a hitherto indisputable property according to which blockchain data cannot be edited nor deleted, remains the cornerstone of blockchain's security. Nevertheless, blockchain's immutability is being called into question lately in the light of the new erasing requirements imposed by the GDPR's ``\textit{Right to be Forgotten (RtbF)}'' provision. As the RtbF obliges blockchain data to be editable in order restricted content redactions, modifications or deletions to be applied when requested, blockchains compliance with the regulation is indeed challenging, if not impracticable. Towards resolving this contradiction, various methods and techniques for mutable blockchains have been proposed in an effort to satisfy regulatory erasing requirements while preserving blockchains' security. To this end, this work aims to provide a comprehensive review on the state-of-the-art research approaches, technical workarounds and advanced cryptographic techniques that have been put forward to resolve this conflict and to discuss their potentials, constraints and limitations when applied in the wild to either permissioned or permissionless blockchains.

Constantinos Patsakis, Efthimios Alepis

Android Notifications can be considered as essential parts in Human-Smartphone interaction and inextricable modules of modern mobile applications that can facilitate User Interaction and improve User Experience. This paper presents how this well-crafted and thoroughly documented mechanism, provided by the OS can be exploited by an adversary. More precisely, we present attacks that result either in forging smartphone application notifications to lure the user in disclosing sensitive information, or manipulate Android Notifications to launch a Denial of Service attack to the users' device, locally and remotely, rendering them unusable. This paper concludes by proposing generic countermeasures for the discussed security threats.

Fran Casino, Constantinos Patsakis, Antoni Martinez-Balleste et al.

In this article, we explain in detail the internal structures and databases of a smart health application. Moreover, we describe how to generate a statistically sound synthetic dataset using real-world medical data.

Michael Clear, Constantinos Patsakis, Paul Laird

In this work we compare two recent multiparty computation (MPC) protocols for private summation in terms of performance. Both protocols allow multiple rounds of aggregation from the same set of public keys generated by parties in an initial stage. We instantiate the protocols with a fast elliptic curve and provide an experimental comparison of their performance for different phases of the protocol. Furthermore, we introduce a technique that allows the computational load of both protocols to be reduced at the expense of protection against collusion tolerance. We prove that both protocols remain secure with this technique, and evaluate its impact on collusion tolerance and the number of rounds supported.