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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

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.

The dependency of our society on networked computers has become frightening: In the economy, all-digital networks have turned from facilitators to drivers; as cyber-physical systems are coming of age, computer networks are now becoming the central nervous systems of our physical world—even of highly critical infrastructures such as the power grid. At the same time, the 24/7 availability and correct functioning of networked computers has become much more threatened: The number of sophisticated and highly tailored attacks on IT systems has significantly increased. Intrusion Detection Systems (IDSs) are a key component of the corresponding defense measures; they have been extensively studied and utilized in the past. Since conventional IDSs are not scalable to big company networks and beyond, nor to massively parallel attacks, Collaborative IDSs (CIDSs) have emerged. They consist of several monitoring components that collect and exchange data. Depending on the specific CIDS architecture, central or distributed analysis components mine the gathered data to identify attacks. Resulting alerts are correlated among multiple monitors in order to create a holistic view of the network monitored. This article first determines relevant requirements for CIDSs; it then differentiates distinct building blocks as a basis for introducing a CIDS design space and for discussing it with respect to requirements. Based on this design space, attacks that evade CIDSs and attacks on the availability of the CIDSs themselves are discussed. The entire framework of requirements, building blocks, and attacks as introduced is then used for a comprehensive analysis of the state of the art in collaborative intrusion detection, including a detailed survey and comparison of specific CIDS approaches.

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.

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.

Due to the increasing quantity and sophistication of cyber-attacks, Intrusion Detection Systems (IDSs) are nowadays considered mandatory security mechanisms for protecting critical networks. Research on cyber-security is moving from such isolated IDSs towards Collaborative IDSs (CIDSs) in order to protect large-scale networks. In CIDSs, a number of IDS sensors work together for creating a holistic picture of the monitored network. Our contribution in this paper is a novel distributed and scalable CIDS, called SkipMon. Our system supports, both, the idea of locality and privacy preserving communication by means of exchanging compact alert data. Furthermore, we propose a mechanism for interconnecting sensors that experience similar traffic patterns. The experimental results suggest that our CIDS, with our technique of connecting monitoring nodes that experience similar traffic, is scalable and offers a good accuracy rate compared to a centralized system with full knowledge of the participating sensors' data.

Intrusion Detection Systems (IDSs) are an important defense tool against the sophisticated and ever-growing network attacks. These systems need to be evaluated against high quality datasets for correctly assessing their usefulness and comparing their performance. We present an Intrusion Detection Dataset Toolkit (ID2T) for the creation of labeled datasets containing user defined synthetic attacks. The architecture of the toolkit is provided for examination and the example of an injected attack, in real network traffic, is visualized and analyzed. We further discuss the ability of the toolkit of creating realistic synthetic attacks of high quality and low bias.

Over the last years the number of cyber-attacks has been constantly increasing. Since isolated Intrusion Detection Systems (IDSs) cannot cope with the number and sophistication of attacks, collaboration among the defenders is required. Collaborative IDSs (CIDSs) work by exchanging alert traffic to construct a holistic view of the monitored network. However, an adversary can utilize probe-response attacks to successfully detect CIDS's monitoring sensors. We discuss the practicability of such attacks, suggest improvements, and also propose novel techniques to reduce the effects of such attacks. Moreover, we present preliminary results in the applicability of the attacks and hints on performing such attacks in a well known CIDS.

The IT infrastructure of today needs to be ready to defend against massive cyber-attacks which often originate from distributed attackers such as Botnets. Most Intrusion Detection Systems (IDSs), nonetheless, are still working in isolation and cannot effectively detect distributed attacks. Collaborative IDSs (CIDSs) have been proposed as a collaborative defense against the ever more sophisticated distributed attacks. However, collaboration by exchanging suspicious alarms among all interconnected sensors in CIDSs does not scale with the size of the IT infrastructure; hence, detection performance and communication overhead, required for collaboration, must be traded off. We propose to partition the set of considered sensors into subsets, or communities, as a lever for this trade off. The novelty of our approach is the application of ensemble based learning, a machine learning paradigm suitable for distributed intrusion detection. In our approach, community members exchange data features used to train models of normality, not bare alarms, thereby further reducing the communication overhead of our approach. Our experiments show that we can achieve detection rates close to those based on global information exchange with smaller subsets of collaborating sensors.

In recent years, the number of sophisticated cyber attacks has increased rapidly. At the same time, people tend to utilize unknown, in terms of trustworthiness, wireless networks in their daily life. They connect to these networks, e.g., airports, without knowledge of whether they are safe or infected with actively propagating malware. In traditional networks, malicious behavior can be detected via Intrusion Detection Systems (IDSs). However, IDSs cannot be applied easily to mobile environments and to resource constrained devices. Another common defense mechanism is honeypots, i.e., systems that pretend to be an attractive target to attract malware and attackers. As a honeypot has no productive use, each attempt to access it can be interpreted as an attack. Hence, they can provide an early indication on malicious network environments. Since low interaction honeypots do not demand high CPU or memory requirements, they are suitable to resource constrained devices like smartphones or tablets.In this paper we present the idea of Honeypot-To-Go. We envision portable honeypots on mobile devices that aim on the fast detection of malicious networks and thus boost the security awareness of users. Moreover, to demonstrate the feasibility of this proposal we present our prototype HosTaGe, a low-interaction honeypot implemented for the Android OS. We present some initial results regarding the performance of this application as well as its ability to detect attacks in a realistic environment. To the best of our knowledge, HosTaGe is the first implementation of a generic low-interaction honeypot for mobile devices.

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.