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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 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.

Most research in the area of intrusion detection requires datasets to develop, evaluate or compare systems in one way or another. In this field, however, finding suitable datasets is a challenge on to itself. Most publicly available datasets have negative qualities that limit their usefulness. In this article, we propose ID2T (Intrusion Detection Dataset Toolkit) to tackle this problem. ID2T facilitates the creation of labeled datasets by injecting synthetic attacks into background traffic. The injected synthetic attacks blend themselves with the background traffic by mimicking the background traffic's properties to eliminate any trace of ID2T's usage. This work has three core contribution areas. First, we present a comprehensive survey on intrusion detection datasets. In the survey, we propose a classification to group the negative qualities we found in the datasets. Second, the architecture of ID2T is revised, improved and expanded. The architectural changes enable ID2T to inject recent and advanced attacks such as the widespread EternalBlue exploit or botnet communication patterns. The toolkit's new functionality provides a set of tests, known as TIDED (Testing Intrusion Detection Datasets), that help identify potential defects in the background traffic into which attacks are injected. Third, we illustrate how ID2T is used in different use-case scenarios to evaluate the performance of anomaly and signature-based intrusion detection systems in a reproducible manner. ID2T is open source software and is made available to the community to expand its arsenal of attacks and capabilities., Comment: 31 pages

Over the last years, we have experienced an increased utilization of Unmanned Aerial Vehicles (UAVs) not only in personal, but also commercial and public safety applications. Simultaneously, malicious activities have emerged too; from hijacking of UAVs (and their cargo), to the theft of private information stored in UAVs, attacks not only exist but seem to increase both in their numbers and their sophistication. In this paper, we propose HoneyDrone, the first honeypot that is specifically designed for the protection of UAVs. HoneyDrone emulates a number of UAV-specific and UAV-tailored protocols, making it possible to lure adversaries into attacking it. The honeypot is designed to run in portable low-cost devices, e.g., Raspberry Pis, which makes it possible to strategically deploy it in a variety of locations. Our system can assist in detecting active attackers in a certain area, as well as in shedding light into the adversaries' techniques for compromising UAVs. We evaluate HoneyDrone's performance and also examine a number of different realistic attack scenarios to show how the honeypot can cope with them.

In an attempt to cope with the increased number of cyber-attacks, research in Intrusion Detection System IDSs is moving towards more collaborative mechanisms. Collaborative IDSs (CIDSs) are such an approach; they combine the knowledge of a plethora of monitors to generate a holistic picture of the monitored network. Despite the research done in this field, CIDSs still face a number of fundamental challenges, especially regarding maintaining trust among the collaborating parties. Recent advances in distributed ledger technologies, e.g. various implementations of blockchain protocols, are a good fit to the problem of enhancing trust in collaborative environments. This paper touches the intersection of CIDSs and blockchains. Particularly, it introduces the idea of utilizing blockchain technologies as a mechanism for improving CIDSs. We argue that certain properties of blockchains can be of significant benefit for CIDSs; namely for the improvement of trust between monitors, and for providing accountability and consensus. For this, we study the related work and highlight the research gaps and challenges towards such a task. Finally, we propose a generic architecture for the incorporation of blockchains into the field of CIDSs and an analysis of the design decisions that need to be made to implement such an architecture.

With the increase in the sophistication of cyberattacks, collaborative defensive approaches such as Collaborative IDSs (CIDSs) have emerged. CIDSs utilize a multitude of heterogeneous monitors to create a holistic picture of the monitored network. Nowadays, a number of research institutes and companies deploy CIDSs that publish their alert data publicly, over the Internet. Such systems are important for researchers and security administrators as they provide a source of real-world alert data for experimentation. However, a class of attacks exist, called Probe-Response Attacks (PRAs), which can significantly reduce the benefits of a CIDS. In particular, such attacks allow an adversary to detect the network location of the monitors of a CIDS. In this paper, we first study the related work and analyze the various mitigation techniques for defending against PRAs. Subsequently, we propose a novel mitigation mechanism that improves the state of the art. Our method, namely the Shuffle-based PRA Mitigation (SPM), is based on the idea of shuffling the watermarks, so-called markers, which the adversary requires to successfully perform a PRA. By doing so the whole process of the attack is disrupted leading to a very small number of identified monitors. Our experimental results suggest that our proposed method significantly reduces the impact of a PRA whilst it does not introduce a trade-off for the usability of the data produced by the CIDS.

Cyber-attacks are steadily increasing in both their size and sophistication. To cope with this, Intrusion Detection Systems (IDSs) are considered mandatory for the protection of critical infrastructure. Furthermore, research is currently focusing on collaborative architectures for IDSs, creating a Collaborative IDS (CIDS). In such a system a number of IDS monitors work together towards creating a holistic picture of the monitored network. Nevertheless, a class of attacks exists, called probe-response, which can assist adversaries to detect the network position of CIDS monitors. This can significantly affect the advantages of a CIDS. In this paper, we introduce PREPARE, a framework for deploying probe-response attacks and also for studying methods for their mitigation. Moreover, we present significant improvements on both the effectiveness of probe-response attacks as well as on mitigation techniques for detecting them. We evaluate our approach via an extensive simulation and a real-world attack deployment that targets two CIDSs. Our results show that our framework can be practically utilized, that our proposals significantly improve probe-response attacks and, lastly, that the introduced detection and mitigation techniques are effective.

The ever-growing number of cyber attacks from botnets has made them one of the biggest threats on the Internet. Thus, it is crucial to study and analyze botnets, to take them down. For this, an extensive monitoring is a pre-requisite for preparing a botnet takedown, e.g., via a sinkholing attack. However, every new monitoring mechanism developed for botnets is usually tackled by the botmasters by introducing novel antimonitoring countermeasures. In this paper, we anticipate these countermeasures by proposing a set of lightweight techniques for detecting the presence of crawlers in P2P botnets, called BoobyTrap. For that, we exploit botnet-specific protocol and design constraints. We evaluate the performance of our BoobyTrap mechanism on two real-world botnets: Sality and ZeroAccess. Our results indicate that we can distinguish many crawlers from benign bots. In fact, we discovered close to 10 crawler nodes within our observation period in the Sality botnet and around 120 in the ZeroAccess botnet. In addition, we also describe the observable characteristics of the detected crawlers and suggest crawler improvements for enabling monitoring in the presence of the BoobyTrap mechanism.

Intrusion Detection Systems (IDSs) are an important defense tool against the sophisticated and ever-growing network attacks. With this in mind, the research community has been immersed in the field of IDSs over the past years more than before. Still, assessing and comparing performance between different systems and algorithms remains one of the biggest challenges in this research area. IDSs need to be evaluated and compared against high quality datasets; nevertheless, the existing ones have become outdated or lack many essential requirements. We present the Intrusion Detection Dataset Toolkit (ID2T), an approach for creating out-of-the-box labeled datasets that contain user defined attacks. In this paper, we discuss the essential requirements needed to create synthetic, yet realistic, datasets with user defined attacks. We also present typical problems found in synthetic datasets and propose a software architecture for building tools that can cope with the most typical problems. A publicly available prototype, is implemented and evaluated. The evaluation comprises a performance analysis and a quality assessment of the generated datasets. We show that our tool can handle large amounts of network traffic and that it can generate synthetic datasets without the problems or shortcomings we identified in other datasets.

New attack surfaces are emerging with the rise of Industrial Control System (ICS) devices exposed on the Internet. ICS devices must be protected in a holistic and efficient manner; especially when these are supporting critical infrastructure. Taking this issue into account, cyber-security research is recently being focused on providing early detection and warning mechanisms for ICSs. In this paper we present a novel honeypot capable of detecting multi-stage attacks targeting ICS networks. Upon detecting a multi-stage attack, our honeypot can generate signatures so that misuse Intrusion Detection Systems (IDSs) can subsequently thwart attacks of the same type. Our experimental results indicate that our honeypot and the signatures it generates provide good detection accuracy and that the Bro IDS can successfully use the signatures to prevent future attacks.

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.