Risk Assessment
I. Introduction
ATC (Air Traffic Control) is considering the use of twitter posting feeds as a resource to gather user generated information, some of this information could also contain geolocation [7] metadata that will increase the trustworthiness of the location of the post, nevertheless this does not support the veracity of the content of the tweeter post. The four initial papers examined and several others added to explore the usability of twitter on ATC related task, had helped us to create a list of Strengths, Weaknesses, Opportunities and Threats (SWOT) that will shape the way we define Risk.
We ultimately strive to answer the question: What are the security risks and/or vulnerabilities in incorporating aviation-related twitter data to the Air Traffic Control’s information resource? We will use the SWOT analysis to determine the Risk associated to use twitter data.
II. Document Review and Related Work
This paper [1][6][11] discuses the information diffusion on Twitter when emergencies happened.
They analyzed the pattern of the information cascades and identified the veracity of information during the diffusion. [3] describes the use of twitter on emergency events from 2 natural disasters in the U.S., the analysis of geolocation enable tweets and retweets, and the consideration of twitter API for command control in emergency scenarios. In [4], mainly highlights the privacy concerns of users who use apps on their smartphones. In our case, this plays an important role in appraising willingness of the user to use twitter as a resource for event information. [5][10] identify the low credibility of Twitter information, they also discusses the reliability of Twitter as a news media.[13][14][19] are some statistics about Twitter, including the total users, monthly active users, mobile users, age group,etc. [20][22] discusses the problems of spam on Twitter,
[20] focuses on
References: [1] C. Hui et al (2012). Information cascades in social media in response to a crisis: a preliminary model and a case study [2] F. Maggi et al (2013). Two years of Short URLs Internet Measurement: Security Threats and Countermeasures [3] M. Mathioudakis & N. Koudas (2010). TwitterMonitor: Trend Detection over the Twitter Stream [4] J. Nichols et al. Summarizing Sports Events Using Twitter. In IUI, pages 189-197, 2012. [5] H. Kwak et al (2010). What is twitter, a social network or a news media? InWWW, pages 591–600. [6] M.Marcelo et al (2010). Twitter under crisis: can we trust what we RT?, Proceedings of the First Workshop on Social Media Analytics, p.71-79, July 25-28. [7] Sarah Vieweg et. al. (2010). Microblogging During Two Natural Events: What Twitter May Contribute to Situational Awareness [10] C. Castillo et al(2011). Information credibility on twitter. In Proceedings of the 20th international conference on World wide web, pages 675-684. [11] J. Ratkiewicz et al(2011). Truthy: mapping the spread of astroturf in microblog streams. In Proceedings of WWW. [14] H.Mengdie et al(2012). Breaking news on Twitter. In The Proceedings of the 2012 ACM Annual Conference on Human Factors in Computing Systems, pages 2751–2754. [15] Felt, A. P., Egelman, S., & Wagner, D(2012). I 've got 99 problems, but vibration ain 't one: A survey of smartphone users ' concerns [16] Barrera, D., Clark, J., McCarney, D., & van Oorschot, P. C (2012). Understanding and improving app installation security mechanisms through empirical analysis of android [17] Sadeh, N., Cranor, L. F., & Kelley, P. G (2013). Privacy as Part of the App DecisionMaking Process. [18] S.Consolvo, S., Smith, I. E., Matthews, T., LaMarca, A., Tabert, J., & Powledge, P(2015). [20] D. Sullivan(2009). Twitter’s Real Time Spam Problem. Search Engine Land. [21] S. Owens(2009). How Celebrity Imposters Hurt Twitter’s Credibility. Mediashift . [22] F. Benevenuto et al (2010). Detecting Spammers on Twitter . In Collaboration ,Electronic messaging, Anti-Abuse and Spam Conference (CEAS) .