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The use of Big Data Analytics to protect Critical Information Infrastructures from Cyber-attacks
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering.
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Unfortunately, cyber-attacks, which are the consequence of our increasing dependence on digital technology, is a phenomenon that we have to live with today. As technology becomes more advanced and complex, so have the types of malware that are used in these cyber-attacks. Currently, targeted cyber-attacks directed at CIIs such as financial institutions and telecom companies are on the rise. A particular group of malware known as APTs, which are used for targeted attacks, are very difficult to detect and prevent due to their sophisticated and stealthy nature. These malwares are able to attack and wreak havoc (in the targeted system) within a matter of seconds; this is very worrying because traditional cyber security defence systems cannot handle these attacks. The solution, as proposed by some in the industry, is the use of BDA systems. However, whilst it appears that BDA has achieved greater success at large companies, little is known about success at smaller companies. Also, there is scarcity of research addressing how BDA is deployed for the purpose of detecting and preventing cyber-attacks on CII. This research examines and discusses the effectiveness of the use of BDA for detecting cyber-attacks and also describes how such a system is deployed. To establish the effectiveness of using a BDA, a survey by questionnaire was conducted. The target audience of the survey were large corporations that were likely to use such systems for cyber security. The research concludes that a BDA system is indeed a powerful and effective tool, and currently the best method for protecting CIIs against the range of stealthy cyber-attacks. Also, a description of how such a system is deployed is abstracted into a model of meaningful practice.

Place, publisher, year, edition, pages
2016. , 63 p.
Keyword [en]
Big data, big data analytics, CII, CI, APTs, cyber-attacks, cyber-security
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:ltu:diva-59779OAI: oai:DiVA.org:ltu-59779DiVA: diva2:1037515
Subject / course
Student thesis, at least 30 credits
Educational program
Information Security, master's level
Supervisors
Examiners
Available from: 2016-10-28 Created: 2016-10-16 Last updated: 2016-10-28Bibliographically approved

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fulltext(1497 kB)47 downloads
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Oseku-Afful, Thomas
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Department of Computer Science, Electrical and Space Engineering
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