Cybersecurity Information Exchange with Privacy (CYBEX-P) and TAHOE – A Cyberthreat Language

Cybersecurity information sharing (CIS) is envisioned to protect organizations more effectively from advanced cyberattacks. However, a completely automated CIS platform is not widely adopted. The major challenges are: (1) the absence of advanced data analytics capabilities and (2) the absence of a robust cyberthreat language (CTL). This work introduces Cybersecurity Information Exchange with Privacy (CYBEX-P), as a CIS framework, to tackle these challenges. CYBEX-P allows organizations to share heterogeneous data from various sources. It correlates the data to automatically generate intuitive reports and defensive rules. To achieve such versatility, we have developed TAHOE - a graph-based CTL. TAHOE is a structure for storing, sharing, and analyzing threat data. It also intrinsically correlates the data. We have further developed a universal Threat Data Query Language (TDQL). In this work, we propose the system architecture for CYBEX-P. We then discuss its scalability along with a protocol to correlate attributes of threat data. We further introduce TAHOE & TDQL as better alternatives to existing CTLs and formulate ThreatRank - an algorithm to detect new malicious events.We have developed CYBEX-P as a complete CIS platform for not only data sharing but also for advanced threat data analysis. To that end, we have developed two frameworks that use CYBEX-P infrastructure as a service (IaaS). The first work is a phishing URL detector that uses machine learning to detect new phishing URLs. This real-time system adapts to the ever-changing landscape of phishing URLs and maintains an accuracy of 86%. The second work models attacker behavior in a botnet. It combines heterogeneous threat data and analyses them together to predict the behavior of an attacker in a host infected by a bot malware. We have achieved a prediction accuracy of 85-97% using our methodology. These two frameworks establish the feasibility of CYBEX-P for advanced threat data analysis for future researchers

Ethical issues in research using datasets of illicit origin

We evaluate the use of data obtained by illicit means against a broad set of ethical and legal issues. Our analysis covers both the direct collection, and secondary uses of, data obtained via illicit means such as exploiting a vulnerability, or unauthorized disclosure. We extract ethical principles from existing advice and guidance and analyse how they have been applied within more than 20 recent peer reviewed papers that deal with illicitly obtained datasets. We find that existing advice and guidance does not address all of the problems that researchers have faced and explain how the papers tackle ethical issues inconsistently, and sometimes not at all. Our analysis reveals not only a lack of application of safeguards but also that legitimate ethical justifications for research are being overlooked. In many cases positive benefits, as well as potential harms, remain entirely unidentified. Few papers record explicit Research Ethics Board (REB) approval for the activity that is described and the justifications given for exemption suggest deficiencies in the REB process.Daniel R. Thomas is supported by a grant from ThreatSTOP Inc. All authors are supported by the EPSRC [grant number EP/M020320/1]. The opinions, findings, and conclusions or recommendations expressed are those of the authors and do not necessarily reflect those of any of the funders

Computer Science & Technology Series : XVIII Argentine Congress of Computer Science. Selected papers

CACIC’12 was the eighteenth Congress in the CACIC series. It was organized by the School of Computer Science and Engineering at the Universidad Nacional del Sur. The Congress included 13 Workshops with 178 accepted papers, 5 Conferences, 2 invited tutorials, different meetings related with Computer Science Education (Professors, PhD students, Curricula) and an International School with 5 courses. CACIC 2012 was organized following the traditional Congress format, with 13 Workshops covering a diversity of dimensions of Computer Science Research. Each topic was supervised by a committee of 3-5 chairs of different Universities. The call for papers attracted a total of 302 submissions. An average of 2.5 review reports were collected for each paper, for a grand total of 752 review reports that involved about 410 different reviewers. A total of 178 full papers, involving 496 authors and 83 Universities, were accepted and 27 of them were selected for this book.Red de Universidades con Carreras en Informática (RedUNCI