scholarly journals Blockchain Based Information Sharing Mechanism for Cyber Threat Intelligence

2020 ◽  
Vol 8 (3) ◽  
pp. 242-253 ◽  
Author(s):  
Ebubekir BÜBER ◽  
Özgür Koray ŞAHİNGÖZ

Significance Multiple actors seek to do harm online for a variety of reasons -- from financial to political motivations. This is creating a new industry: cyber threat intelligence (CTI). Impacts By developing both national and industry-specific information-sharing agreements, organisations may share vital CTI. Firms will increasingly specialise in specific components of CTI (such as examining particular regions or industries). Better defended firms will encourage criminals to become more innovative.


2017 ◽  
Vol 67 ◽  
pp. 35-58 ◽  
Author(s):  
Sara Qamar ◽  
Zahid Anwar ◽  
Mohammad Ashiqur Rahman ◽  
Ehab Al-Shaer ◽  
Bei-Tseng Chu

Author(s):  
John Robertson ◽  
Ahmad Diab ◽  
Ericsson Marin ◽  
Eric Nunes ◽  
Vivin Paliath ◽  
...  

Author(s):  
Nolan Arnold ◽  
Mohammadreza Ebrahimi ◽  
Ning Zhang ◽  
Ben Lazarine ◽  
Mark Patton ◽  
...  

2019 ◽  
Vol 11 (7) ◽  
pp. 162 ◽  
Author(s):  
Nikolaos Serketzis ◽  
Vasilios Katos ◽  
Christos Ilioudis ◽  
Dimitrios Baltatzis ◽  
Georgios Pangalos

The complication of information technology and the proliferation of heterogeneous security devices that produce increased volumes of data coupled with the ever-changing threat landscape challenges have an adverse impact on the efficiency of information security controls and digital forensics, as well as incident response approaches. Cyber Threat Intelligence (CTI)and forensic preparedness are the two parts of the so-called managed security services that defendants can employ to repel, mitigate or investigate security incidents. Despite their success, there is no known effort that has combined these two approaches to enhance Digital Forensic Readiness (DFR) and thus decrease the time and cost of incident response and investigation. This paper builds upon and extends a DFR model that utilises actionable CTI to improve the maturity levels of DFR. The effectiveness and applicability of this model are evaluated through a series of experiments that employ malware-related network data simulating real-world attack scenarios. To this extent, the model manages to identify the root causes of information security incidents with high accuracy (90.73%), precision (96.17%) and recall (93.61%), while managing to decrease significantly the volume of data digital forensic investigators need to examine. The contribution of this paper is twofold. First, it indicates that CTI can be employed by digital forensics processes. Second, it demonstrates and evaluates an efficient mechanism that enhances operational DFR.


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