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Botnets account for a substantial portion of cybercrime. Botmasters utilize darkweb marketplaces to promote and provide their services, which can vary from renting or buying a botnet (or parts of it) to hiring services (e.g., distributed denial of service attacks). At the same time, botnet takedown attempts have proven to be challenging, demanding a combination of technical and legal methods, and often requiring the collaboration of a plethora of entities with varying jurisdictions. In this article, we map the elements associated with the business aspect of botnets and utilize them to develop adaptations of two widely used business models. Furthermore, we analyze the 28 most notable botnet takedown operations carried out from 2008 to 2021, in regard to the methods employed, and illustrate the correlation between these methods and the segments of our adapted business models. Our analysis suggests that the botnet takedown methods have been mainly focused on the technical side, but not on the botnet economic components. We aim to shed light on new takedown vectors and incentivize takedown actors to expand their efforts to methods oriented more toward the business side of botnets, which could contribute toward eliminating some of the challenges that surround takedown operations.

Honeypots are decoy systems that lure attackers by presenting them with a seemingly vulnerable system. They provide an early detection mechanism as well as a method for learning how adversaries work and think. However, over the last years a number of researchers have shown methods for fingerprinting honeypots. This significantly decreases the value of a honeypot; if an attacker is able to recognize the existence of such a system, they can evade it. In this article, we revisit the honeypot identification field, by providing a holistic framework that includes state of the art and novel fingerprinting components. We decrease the probability of false positives by proposing a rigid multi-step approach for labeling a system as a honeypot. We perform extensive scans covering 2.9 billion addresses of the IPv4 space and identify a total of 21,855 honeypot instances. Moreover, we present a number of interesting side-findings such as the identification of around 355,000 non-honeypot systems that represent potentially misconfigured or unpatched vulnerable servers (e.g. SSH servers with default password configurations and vulnerable versions). We ethically disclose our findings to network administrators about the default configuration and the honeypot developers about the gaps in implementation that lead to possible honeypot fingerprinting. Lastly, we discuss countermeasures against honeypot fingerprinting techniques.

DNS hijacking represents a security threat to users because it enables bypassing existing DNS security measures. Several malware families exploit this by changing the client DNS configuration to point to a malicious DNS resolver. Following the assumption that users will never actively choose to use a resolver that is not well-known, our paper introduces the idea of detecting client-based DNS hijacking by classifying public resolvers based on whether they are well-known or not. Furthermore, we propose to use NetFlow-based features to classify a resolver as well-known or malicious. By characterizing and manually labelling the 405 resolvers seen in four weeks of NetFlow data from a national ISP, we show that classification of both well-known and malicious servers can be made with an AUROC of 0.85.

Botnets are an ongoing threat to the cyber world and can be utilized to carry out DDoS attacks of high magnitude. From the botmaster's perspective, there is a constant need for deploying more effective botnets and discovering new ways to bolster their bot ranks. Integrated Development Environments (IDEs) have been essential for software developers to write and compile source code. The increasing need for remote work and collaborative workspaces have led to the IDE-as-a-service paradigm that offers online code editing and compilation with multiple language support. In this paper, we show that a multitude of online IDEs do not run control checks on the user code and can be therefore lever-aged by a botnet. We examine the concept of uncontrolled execution environments and present a proof of concept to show how uncontrolled online-IDEs can be weaponized to perform large-scale attacks by a botnet. Overall, we detect a total of 719 online-IDEs with uncontrolled execution environments and limited sandboxing. Lastly, as ethical disclosure, we inform the IDE developers and service providers of the vulnerabilities and propose countermeasures.

Botnet research is one of the many research areas affected by the coming into force of the General Data Protection Regulation (GDPR). This article aims to identify the most appropriate legal bases that would legitimise data processing in the context of botnet tracking and to give an overview of the practical implications for practitioners. First, we give a technical introduction to botnet tracking techniques and the types of processed data. Afterward, we argue that botnet tracking qualifies as ”processing of personal data” and falls under the material scope of the GDPR. We then present three scenarios where these botnet tracking techniques apply: botnet tracking research in the public interest, botnet tracking in the commercial interest and botnet tracking conducted by Internet service providers. For each scenario, we discuss the differing goals, identify the appropriate legal bases, and elaborate on the practical implications. This article concludes that the legal implications are very different for each of the three scenarios, highlighting the importance of carefully considering the legal bases before engaging in botnet tracking.

The Internet of things (IoT) and critical infrastructure utilizing operational technology (OT) protocols are nowadays a common attack target and/or attack surface used to further propagate malicious actions. Deception techniques such as honeypots have been proposed for both IoT and OT but they either lack an extensive evaluation or are subject to fingerprinting attacks. In this paper, we extend and evaluate RIoTPot, a hybrid-interaction honeypot, by exposing it to attacks on the Internet and perform a longitudinal study with multiple evaluation parameters for three months. Furthermore, we publish the aforementioned study in the form of a dataset that is available to researchers upon request. We leverage RIoTPot's hybrid-interaction model to deploy it in three interaction variants with six protocols deployed on both cloud and self-hosted infrastructure to study and compare the attacks gathered. At a glance, we receive 10.87 million attack events originating from 22,518 unique IP addresses that involve brute-force, poisoning, multistage and other attacks. Moreover, we fingerprint the attacker IP addresses to identify the type of devices who participate in the attacks. Lastly, our results indicate that the honeypot interaction levels have an important role in attracting specific attacks and scanning probes.

Mirai and its variants have demonstrated the ease and devastating effects of exploiting vulnerable Internet of Things (IoT) devices. In many cases, the exploitation vector is not sophisticated; rather, adversaries exploit misconfigured devices (e.g. unauthenticated protocol settings or weak/default passwords). Our work aims at unveiling the state of IoT devices along with an exploration of the current attack landscape. In this paper, we perform an Internet-level IPv4 scan to unveil 1.8 million misconfigured IoT devices that may be exploited to perform large-scale attacks. These results are filtered to exclude a total of 8,192 devices that we identify as honeypots during our scan. To study current attack trends, we deploy six state-of-art IoT honeypots for a period of 1 month. We gather a total of 200, 209 attacks and investigate how adversaries leverage misconfigured IoT devices. In particular, we study different attack types, including denial of service, multistage attacks and attacks from infected online hosts. Furthermore, we analyze data from a /8 network telescope covering a total of 81 billion requests towards IoT protocols (e.g. CoAP, UPnP). Combining knowledge from the aforementioned experiments, we identify 11, 118 IP addresses (that are part of the detected misconfigured IoT devices) that attacked our honeypot setup and the network telescope.

Darkweb marketplaces have evolved greatly since the rise of the Silk Road in 2011, the first platform of its kind, and have become a highly profitable underground trading ecosystem, which provides anonymity for both buyers and sellers. Law enforcement along with researchers, have been successful in taking down marketplaces over the years. However, the combination of mechanisms implemented by these platforms (e.g. payment mechanisms, cryptocurrencies, trust systems), along with the success of the Tor network's anonymity properties, have made marketplaces much more enticing to users, while providing ease of access and use, as well as resilience against hostile actions. Through qualitative methods, this paper presents a mapping of darkweb marketplaces. We systematically investigate the operation of 41 marketplaces, along with 35 vendor shops, and gather information about the mechanisms and features implemented. Additionally, to acquire real world information, we explore the marketplaces' integrated forums, as well as 3 popular independent ones, focusing on discussions between vendors, buyers and marketplace owners, on topics related to illegal trading. We believe that gaining an up-to-date and deep understanding of the framework that marketplaces are built upon, is the first step towards discovering weak spots in the cyber security product and service market, with the disruption of its operation being the ultimate goal.

Bitcoin is undoubtedly the most used distributed ledger technology nowadays. Bitcoin's OP_RETURN operator allows for saving arbitrary data on the blockchain. This comes as an extension of Bitcoin's core usage (i.e. cryptocurrency) and opens up a multitude of use cases. These range from benign applications (e.g. ownership of a digital/physical asset) to illegal/malicious scenarios (e.g. blockchain-based botnets). In this paper, we present a system that provides advanced analytic and visual capabilities with regard to the OP_RETURN operator. Furthermore, we showcase a quantitative and qualitative analysis of the OP_RETURN along with a number of interesting findings.

Sophisticated mass attacks, especially when exploiting zero-day vulnerabilities, have the potential to cause destructive damage to organizations and critical infrastructure. To timely detect and contain such attacks, collaboration among the defenders is critical. By correlating real-time detection information (threat indicators) from multiple sources, defenders can detect attacks and take the appropriate measures in time. However, although the technical tools to facilitate collaboration exist, real-world adoption of such collaborative security mechanisms is still underwhelming. This is largely due to a lack of trust and participation incentives for companies and organizations. This paper proposes TRIDEnT, a novel collaborative platform that aims to enable parties to exchange network threat indicators, thus increasing their overall detection capabilities. TRIDEnT allows parties that may be in a competitive relationship, to selectively advertise, sell and acquire threat indicators in the form of (near) real-time peer-to-peer streams. To demonstrate the feasibility of our approach, we instantiate our design in a decentralized manner using Ethereum smart contracts and provide a fully functional prototype.

The topic of performing safe and secure elections is a long-standing debate. Regardless, of the various attempts for electronic or Internet-based voting, the majority of countries still use paper ballots. Nevertheless, with major advancements occurring over the last years in both cryptography and distributed ledgers we believe that there is space now for re-investigating this area. In this paper, we propose ethVote an Internet voting system that makes use of the Ethereum blockchain, state of the art cryptographic mechanisms and a P2P-based front-end to ensure a secure voting process. In addition, we provide an open-source proof of concept implementation that features the majority of the needed components for securely using ethVote. Our proposal is tested both in terms of unit testing, requirement verification, and with regard to the feasibility to perform such an operation in a public distributed ledger.

Internet Service Providers (ISPs) have an economic and operational interest in detecting malicious network activity relating to their subscribers. However, it is unclear what kind of traffic data an ISP has available for cyber-security research, and under which legal conditions it can be used. This paper gives an overview of the challenges posed by legislation and of the data sources available to a European ISP. DNS and NetFlow logs are identified as relevant data sources and the state of the art in anonymization and fingerprinting techniques is discussed. Based on legislation, data availability and privacy considerations, a practically applicable anonymization policy is presented.

The devastating effects of cyber-attacks, highlight the need for novel attack detection and prevention techniques. Over the last years, considerable work has been done in the areas of attack detection as well as in collaborative defense. However, an analysis of the state of the art suggests that many challenges exist in prioritizing alert data and in studying the relation between a recently discovered attack and the probability of it occurring again. In this article, we propose a system that is intended for characterizing network entities and the likelihood that they will behave maliciously in the future. Our system, namely Network Entity Reputation Database System (NERDS), takes into account all the available information regarding a network entity (e. g. IP address) to calculate the probability that it will act maliciously. The latter part is achieved via the utilization of machine learning. Our experimental results show that it is indeed possible to precisely estimate the probability of future attacks from each entity using information about its previous malicious behavior and other characteristics. Ranking the entities by this probability has practical applications in alert prioritization, assembly of highly effective blacklists of a limited length and other use cases., 30 pages, 8 figures

Botnet attacks have devastating effects on public and private infrastructures. The botmasters controlling these networks aim to prevent takedown attempts by using highly resilient P2P overlays to commandeer their botnets, and even harden them with countermeasures against intelligence gathering attempts. In fact, recent research indicates that advanced countermeasures can hamper the ability to gather the necessary intelligence for taking down botnets. In this article, we take the perspective of the botmaster to eventually anticipate their behavior. That said, we present a novel mechanism, namely Trust Based Botnet Monitoring Countermeasure (TrustBotMC), that combines computational trust with specially crafted bot messages to detect the presence of monitoring activity. We study and evaluate different computational trust models, to create a local and autonomous mechanism that ensures the avoidance of common botnet tracking mechanisms, such as sensors. Furthermore, we show, via our experimental results, that our approach can reduce the gathered intelligence by at least 53% compared to techniques that have been seen in botnets to date. Finally, we investigate techniques for mitigating our approach.

The effects of botnet attacks, over the years, have been devastating. From high volume Distributed Denial of Service (DDoS) attacks to ransomware attacks, it is evident that defensive measures need to be taken. Indeed, there has been a number of successful takedowns of botnets that exhibit a centralized architecture. However, this is not the case with distributed botnets that are more resilient and armed with countermeasures against monitoring. In this paper, we argue that monitoring countermeasures, applied by botmasters, will only become more sophisticated; to such an extent that monitoring, under these adverse conditions, may become infeasible. That said, we present the most detailed analysis, to date, of parameters that influence a P2P botnet’s resilience and monitoring resistance. Integral to our analysis, we introduce BotChurn (BC) a realistic and botnet-focused churn generator that can assist in the analysis of botnets. Our experimental results suggest that certain parameter combinations greatly limit intelligence gathering operations. Furthermore, our analysis highlights the need for extensive collaboration between defenders. For instance, we show that even the combined knowledge of 500 monitoring instances is insufficient to fully enumerate some of the examined botnets. In this context, we also raise the question of whether botnet monitoring will still be feasible in the near future.

Collaborative Intrusion Detection Systems (CIDSs) are an emerging field in cyber-security. In such an approach, multiple sensors collaborate by exchanging alert data with the goal of generating a complete picture of the monitored network. This can provide significant improvements in intrusion detection and especially in the identification of sophisticated attacks. However, the challenge of deciding to which extend a sensor can trust others, has not yet been holistically addressed in related work. In this paper, we firstly propose a set of requirements for reliable trust management in CIDSs. Afterwards, we carefully investigate the most dominant CIDS trust schemes. The main contribution of the paper is mapping the results of the analysis to the aforementioned requirements, along with a comparison of the state of the art. Furthermore, this paper identifies and discusses the research gaps and challenges with regard to trust and CIDSs.