A honeypot is a type of security facility deliberately created to be probed, attacked, and compromised. It is often used for protecting production systems by detecting and deflecting unauthorized accesses. It is also useful for investigating the behavior of attackers, and in particular, unknown attacks. For the past 17 years plenty of effort has been invested in the research and development of honeypot techniques, and they have evolved to be an increasingly powerful means of defending against the creations of the blackhat community. In this paper, by studying a wide set of honeypots, the two essential elements of honeypots—the decoy and the captor—are captured and presented, together with two abstract organizational forms—independent and cooperative—where these two elements can be integrated. A novel decoy and captor (D-C) based taxonomy is proposed for the purpose of studying and classifying the various honeypot techniques. An extensive set of independent and cooperative honeypot projects and research that cover these techniques is surveyed under the taxonomy framework. Furthermore, two subsets of features from the taxonomy are identified, which can greatly influence the honeypot performances. These two subsets of features are applied to a number of typical independent and cooperative honeypots separately in order to validate the taxonomy and predict the honeypot development trends.
Conference or workshop item
Giubilo, F. et al. (2017). An Architecture for Privacy-preserving Sharing of CTI with 3rd party Analysis Services. in:12th International Conference for Internet Technology and Secured Transactions (ICITST).
Increasing numbers of Small and Medium Enterprises (SME) are outsourcing or hosting their services on different Cloud Service Providers (CSP). They are also using different security services from these CSPs such as firewalls, intrusion detection/prevention systems and anti-malware. Although for the SMEs the main purpose of using these security services is to protect their cyber assets, either physical or virtual, from security threats and compromises, a very useful and valuable by-product of these security services is the wealth of Cyber Threat Information (CTI) that is collected over time. However, a common problem faced by SMEs is that they lack the resources and expertise for monitoring, analysing and reacting to any security notifications, alerts or events generated by the security services they have subscribed to. An obvious solution to this problem is that the SMEs outsource this problem to a cloud based service as well, by sharing their CTI with this service and allowing it to analyse the information and generate actionable reports or patches. The more CTI obtained from different SMEs, the better the analysis result. In this paper, we try to address some of the privacy and confidentiality issues that arise as a result of different SMEs sharing their CTI with such a third party analysis service for the aggregate analysis scenario we just described. We present the design and architecture of our solution that aims to allow SMEs to perform policy-based sharing of CTI, while also offering them flexible privacy and confidentiality controls.