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Filters: Keyword is qualitative evaluation  [Clear All Filters]
2020-07-10
Bradley, Cerys, Stringhini, Gianluca.  2019.  A Qualitative Evaluation of Two Different Law Enforcement Approaches on Dark Net Markets. 2019 IEEE European Symposium on Security and Privacy Workshops (EuroS PW). :453—463.

This paper presents the results of a qualitative study on discussions about two major law enforcement interventions against Dark Net Market (DNM) users extracted from relevant Reddit forums. We assess the impact of Operation Hyperion and Operation Bayonet (combined with the closure of the site Hansa) by analyzing posts and comments made by users of two Reddit forums created for the discussion of Dark Net Markets. The operations are compared in terms of the size of the discussions, the consequences recorded, and the opinions shared by forum users. We find that Operation Bayonet generated a higher number of discussions on Reddit, and from the qualitative analysis of such discussions it appears that this operation also had a greater impact on the DNM ecosystem.

2020-02-17
Liu, Haitian, Han, Weihong, jia, Yan.  2019.  Construction of Cyber Range Network Security Indication System Based on Deep Learning. 2019 IEEE Fourth International Conference on Data Science in Cyberspace (DSC). :495–502.
The main purpose of this paper is to solve the problem of quantitative and qualitative evaluation of network security. Referring to the relevant network security situation assessment algorithms, and by means of advanced artificial intelligence deep learning technology, to build a network security Indication System based on Cyber Range, and optimize the guidance model of deep learning technology.
2017-02-23
Fisk, G., Ardi, C., Pickett, N., Heidemann, J., Fisk, M., Papadopoulos, C..  2015.  Privacy Principles for Sharing Cyber Security Data. 2015 IEEE Security and Privacy Workshops. :193–197.

Sharing cyber security data across organizational boundaries brings both privacy risks in the exposure of personal information and data, and organizational risk in disclosing internal information. These risks occur as information leaks in network traffic or logs, and also in queries made across organizations. They are also complicated by the trade-offs in privacy preservation and utility present in anonymization to manage disclosure. In this paper, we define three principles that guide sharing security information across organizations: Least Disclosure, Qualitative Evaluation, and Forward Progress. We then discuss engineering approaches that apply these principles to a distributed security system. Application of these principles can reduce the risk of data exposure and help manage trust requirements for data sharing, helping to meet our goal of balancing privacy, organizational risk, and the ability to better respond to security with shared information.