Biblio

Chaotic dynamics is widely used to design pseudo-random number generators and for other applications, such as secure communications and encryption. This paper aims to study the dynamics of the discrete-time chaotic maps in the digital (i.e., finite-precision) domain. Differing from the traditional approaches treating a digital chaotic map as a black box with different explanations according to the test results of the output, the dynamical properties of such chaotic maps are first explored with a fixed-point arithmetic, using the Logistic map and the Tent map as two representative examples, from a new perspective with the corresponding state-mapping networks (SMNs). In an SMN, every possible value in the digital domain is considered as a node and the mapping relationship between any pair of nodes is a directed edge. The scale-free properties of the Logistic map's SMN are proved. The analytic results are further extended to the scenario of floating-point arithmetic and for other chaotic maps. Understanding the network structure of a chaotic map's SMN in digital computers can facilitate counteracting the undesirable degeneration of chaotic dynamics in finite-precision domains, also helping to classify and improve the randomness of pseudo-random number sequences generated by iterating the chaotic maps.

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%.