In today’s world, as the volume of digitized data grows exponentially, the need and the ability to store and computationally analyze large datasets are growing along with it. The term “big data” refers to very large or complex datasets, such that classical data processing software applications are insufficient to manage. A great example of a company that symbolizes the modern mass data-driven world is Google. It is possibly the most successful IT company in the world as well as the largest data processing company of modern times. In April 2004, Larry Page and Sergey Brin wrote their first and now famous “Founders Letter” to their employees which stated “Google is not a conventional company. We do not intend to become one.” Twelve years down the line, with a change in leadership, incoming CEO Sundar Pichai wrote a letter to employees in 2016 and concluded it with “Google is an information company. It was when it was founded, and it is today. And it’s what people do with that information that amazes and inspires me every day.” There are many challenges in the analysis of large volumes of data, including data capture and storage, data analysis, curation, searching, sharing and transfer-ring, data visualization, data inquiry and updating, among others. However, the biggest challenge is information security and privacy of big data . A lack of securi-ty around big data can lead to great financial losses and damage to the reputation for the company. Security threats and attacks are becoming more active in violating cyber rules and regulations. These attacks also affect big data and the information contained in it. Attackers target personal and financial data, or a company’s confidential intellectual property information, which greatly affects their competitiveness. The biggest threat is when attackers target personal or consumer financial information stored in big data. Although there are rules and regulations in place to protect data, there are still vulnerabilities in big data that are serious enough to warrant substantial concern. In a recent and highly publicized incident, WikiLeaks released a huge trove of alleged internal documents from the US Central Intelligence Agency (CIA). It is by far the largest leak of CIA documents in history. There are thousands of pages describing sophisticated software tools and techniques used by the agency to break into smartphones, computers, and even Internet-connected televisions. Both government and corporate leaks have been made possible due to the ease of downloading, storing, and transferring millions of documents in a very short time. With this state of affairs in mind, there needs to be a comprehensive examination of these threats and attacks on big data, and a study of novel approaches to defend it. This chapter presents an in-depth look into the threats and attacks on big data and inspects the methods of defense and protection. We discuss the vulnerabilities of modern big data systems, and the characteristic methods of intrusion, and unauthorized seizure of data. We present a few case studies of big data weaknesses and their exploitation by attackers. The information offered here is very useful in building proper defenses against potential malicious incidents. We also discuss the specific security demands of big data environments in government and medical sectors.
- Big data
- Security threats
- Digital footprints
- Machine learning
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Gohel, H.A., Upadhyay, H. (2018). Developing Security Intelligence in Big Data. In: Margret Anouncia, S., Wiil, U. (eds) Knowledge Computing and Its Applications. Springer, Singapore. https://doi.org/10.1007/978-981-10-6680-1_2
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