Big data security analysis approach using Computational Intelligence techniques in R for desktop users

© 2016 IEEE.Big Data security analysis is commonly used for the analysis of large volume security data from an organisational perspective, requiring powerful IT infrastructure and expensive data analysis tools. Therefore, it can be considered to be inaccessible to the vast majority of desktop users and is difficult to apply to their rapidly growing data sets for security analysis. A number of commercial companies offer a desktop-oriented big data security analysis solution; however, most of them are prohibitive to ordinary desktop users with respect to cost and IT processing power. This paper presents an intuitive and inexpensive big data security analysis approach using Computational Intelligence (CI) techniques for Windows desktop users, where the combination of Windows batch programming, EmEditor and R are used for the security analysis. The simulation is performed on a real dataset with more than 10 million observations, which are collected from Windows Firewall logs to demonstrate how a desktop user can gain insight into their abundant and untouched data and extract useful information to prevent their system from current and future security threats. This CI-based big data security analysis approach can also be extended to other types of security logs such as event logs, application logs and web logs

TextRWeb: Large-Scale Text Analytics with R on the Web

As digital data sources grow in number and size, they pose an opportunity for computational investigation by means of text mining, NLP, and other text analysis techniques. R is a popular and powerful text analytics tool; however, it needs to run in parallel and re- quires special handling to protect copyrighted content against full access (consumption). The HathiTrust Research Center (HTRC) currently has 11 million volumes (books) where 7 million volumes are copyrighted. In this paper we propose HTRC TextRWeb, an interactive R software environment which employs complexity hiding interfaces and automatic code generation to allow large-scale text analytics in a non-consumptive means. For our principal test case of copyrighted data in HathiTrust Digital Library, TextRWeb permits us to code, edit, and submit text analytics methods empowered by a family of interactive web user interfaces. All these methods combine to reveal a new interactive paradigm for large-scale text analytics on the web