NAVSEC: A recommender system for 3D network security visualizations
Conference
Nunnally, T, Abdullah, K, Uluagac, AS et al. (2013). NAVSEC: A recommender system for 3D network security visualizations
. 41-48. 10.1145/2517957.2517963
Nunnally, T, Abdullah, K, Uluagac, AS et al. (2013). NAVSEC: A recommender system for 3D network security visualizations
. 41-48. 10.1145/2517957.2517963
As network attacks increase in complexity, the ability to quickly analyze security data and mitigate the effect of these attacks becomes a difficult problem. To alleviate these challenges, researchers are looking into various two-dimensional (2D) and three-dimensional (3D) visualization tools to detect, identify, and analyze malicious attacks. These visualization tools often require advanced knowledge in networking, visualization, and information security to operate, navigate, and successfully examine malicious attacks. Novice users, deficient in the required advanced knowledge, may find navigation within these visualization tools difficult. Furthermore, expert users may be limited and costly. We discuss the use of a modern recommender system to aid in navigating within a complex 3D visualization for network security applications. We developed a visualization module called NAVSEC, a recommender system prototype for navigating in 3D network security visualization tools. NAVSEC recommends visualizations and interactions to novice users. Given visualization interaction input from a novice user and expert communities, NAVSEC is instrumental in reducing confusion for a novice user while navigating in a 3D visualization. We illustrate NAVSEC with a use-case from an emulated stealthy scanning attack disguised as a file transfer with multiple concurrent connections. We show that using NAVSEC, a novice user's visualization converges towards a visualization used to identify or detect a suspected attack by an expert user. As a result, NAVSEC can successfully guide the novice user in differentiating between complex network attacks and benign legitimate traffic with step-by-step created visualizations and suggested user interactions. Copyright 2013 ACM.
