| Title | Automatic Detection of Cyber Security Related Accounts on Online Social Networks: Twitter As an Example |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Aslan, Ça\u grı B., Sa\u glam, Rahime Belen, Li, Shujun |
| Conference Name | Proceedings of the 9th International Conference on Social Media and Society |
| Publisher | ACM |
| Conference Location | New York, NY, USA |
| ISBN Number | 978-1-4503-6334-1 |
| Keywords | expert systems, Human Behavior, pubcrawl, resilience, Resiliency, Scalability, security |
| Abstract | Recent studies have revealed that cyber criminals tend to exchange knowledge about cyber attacks in online social networks (OSNs). Cyber security experts are another set of information providers on OSNs who frequently share information about cyber security incidents and their personal opinions and analyses. Therefore, in order to improve our knowledge about evolving cyber attacks and the underlying human behavior for different purposes (e.g., crime investigation, understanding career development of cyber criminals and cyber security professionals, detection of impeding cyber attacks), it will be very useful to detect cyber security related accounts on OSNs automatically, and monitor their activities. This paper reports our preliminarywork on automatic detection of cyber security related accounts on OSNs using Twitter as an example. Three machine learning based classification algorithms were applied and compared: decision trees, random forests, and SVM (support vector machines). Experimental results showed that both decision trees and random forests had performed well with an overall accuracy over 95%, and when random forests were used with behavioral features the accuracy had reached as high as 97.877%. |
| DOI | 10.1145/3217804.3217919 |
| Citation Key | aslan_automatic_2018 |
Automatic Detection of Cyber Security Related Accounts on Online Social Networks: Twitter As an Example