International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 04 Issue: 10 | Oct -2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 1544 Big Data Security Challenges: An Overview and Application of User Behavior Analytics Tanya Akutota 1 , Swarnava Choudhury 2 1 UG Student, Computer Science Department National Institute of Technology- Silchar 2 UG Student, Electronics and Communication Department National Institute of Technology-Silchar ---------------------------------------------------------------------------***--------------------------------------------------------------------------- Abstract- Automation and digitization of activities have resulted in a huge volume of data generated, called Big Data. Big Data helps many organizations gain useful insights, but at the same time, there are two types of risk involved: Security Risk to Big Data itself, and Privacy Risks of users and Individuals. In this paper, the characteristics of big data, its applications, and the security and privacy challenges that come with it are discussed. This paper also explores a novel Big Data Security Analytics method, called User Behavior Analytics, its functioning, use cases and advantages. Keywords: Analytics, Big Data, Challenges, Security, SIEM, UBA 1. INTRODUCTION 21 st century has seen the human lives shifting towards digitalization; automated machines in industries, cellular phones, social networks, etc., all have led us to this. Such huge digitization means generation of huge, perhaps complex sets of data every day. These large and complex data maybe the data from sensors, browsing reports, usersǯ statistics or anything which are increasing exponentially with each passing day. As the inventor of World-Wide-Web, Tim Berners-Lee said, ǮData is a precious thing as they last longer than systemsǯ, Big Data Analytics (or BDA) is the tool which actually helps us in realizing the power of such large and complex datasets. The conventional database tools are not able to process such amount of heterogeneous data. Whereas Big Data Analytics uses the power of parallel processing to extract an enormous amount of valuable information, like future trends of market, developments in life science, etc., from the data gathered from all possible and available sources. A Big Data has many unique characteristics which set it apart from a conventional database system. The types of data they work upon varies. There are basically 3 major classes of data, namely: 1. Structured data- These data are present in the form of rigid relational models, with specific data types and sizes. Conventional database techniques are efficient at this level. 2. Semi-structured data- A type of structured data, but it is hierarchical in nature with the use of tags and markers. XML data is a perfect example of such data. 3. Unstructured data- It doesnǯt follow a predefined model. The data vary widely; this is where Big Data Analytics comes in. A Big Data can be best described using 5 characteristics, more popularly known as Dzthe ͷ Vǯsdz:  Volume- the scale of data; from Exabytes to Zettabytes!  Velocity- rate at which streaming data is generated and analysed.  Variety- different forms of data- from various external or internal sources.  Veracity- the uncertainty of data, i.e., the different probabilities a value can take.  Value- analysis and visualization of all the above components gives out the final data, the precious information referred to as the Value.