Biblio

A approach to research on the attack mechanism designs through attack surface technology due to the complexity of the attack mechanism. The attack mechanism of a mimic architecture is analyzed in a relative way using attack surface metrics to indicate whether mimic architectures are safer than non-mimic architectures. The definition of the architectures attack surface in terms of the mimic brackets along three abstract dimensions referenced the system attack surface. The larger the attack surface, the more likely the architecture will be attacked.

Attackers create new threats and constantly change their behavior to mislead security systems. In this paper, we propose an adaptive threat detection architecture that trains its detection models in real time. The major contributions of the proposed architecture are: i) gather data about zero-day attacks and attacker behavior using honeypots in the network; ii) process data in real time and achieve high processing throughput through detection schemes implemented with stream processing technology; iii) use of two real datasets to evaluate our detection schemes, the first from a major network operator in Brazil and the other created in our lab; iv) design and development of adaptive detection schemes including both online trained supervised classification schemes that update their parameters in real time and learn zero-day threats from the honeypots, and online trained unsupervised anomaly detection schemes that model legitimate user behavior and adapt to changes. The performance evaluation results show that proposed architecture maintains an excellent trade-off between threat detection and false positive rates and achieves high classification accuracy of more than 90%, even with legitimate behavior changes and zero-day threats.

This study focuses on the spatial context of hacking to networks of Honey-pots. We investigate the relationship between topological positions and geographic positions of victimized computers and system trespassers. We've deployed research Honeypots on the computer networks of two academic institutions, collected information on successful brute force attacks (BFA) and system trespassing events (sessions), and used Social Network Analysis (SNA) techniques, to depict and understand the correlation between spatial attributes (IP addresses) and hacking networks' topology. We mapped and explored hacking patterns and found that geography might set the behavior of the attackers as well as the topology of hacking networks. The contribution of this study stems from the fact that there are no prior studies of geographical influences on the topology of hacking networks and from the unique usage of SNA to investigate hacking activities. Looking ahead, our study can assist policymakers in forming effective policies in the field of cybercrime.