A research paper on BIG DATA ANALYTICS ADOPTION FOR CYBER SECURITY.Pages: 20 to 25Strictly No plagiarism.

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BIG DATA ANALYTICS ADOPTION FOR CYBER SECURITY
Abstract
This research paper focuses on cyber security as in the present day the misuse of confidential data has increased. In 2018 Cambridge Analytica misused the data of Facebook. People share videos, pictures and many more confidential documents that are needed to be secure. Application of the data analysis helps to monitor irregular or abnormal flow of information. The research paper has talked about the presence of big data in cyber security nowadays. Different data analysis techniques are used worldwide to prevent these cyber-attacks and those have been decisively discussed in this study. Also, the study has talked about the significance of numerous data mining techniques. The advanced data mining techniques enrich the big data and ultimately results in the development of cyber security systems. The detection of malware is utilised in mobile phones as it comprises a complex programming system. For maintaining privacy it is essential to detect malware.
The study critically elaborates on the fundamentals of the big data processing factor. In this study, the phenomenon of the big data cybersecurity system concerning the business firms is highlighted. The study provides a brief elaboration on different frameworks and concepts of big data. Such as the study provides the concept of 5Vs of the big data system. Following that the study critically elaborated the framework of the big data cyber security. This factor has been highlighted in this part of the study so that the readers get a clear view of the fundamentals of the big data process and storing factor. Some of the brief elaboration on the encryptions of the big data has been highlighted in this study. For example, the process of data mining and other unethical practices to get access to the private sectors of the users has been highlighted. Lastly the study through lights on of the Hadoop system and briefly elaborate the process of securely storing and analysing the big data of the users.
Table of Contents
Introduction    4
Literature review    5
Problem statement    7
Research aim    8
Research objectives    8
Analysis and discussion on the idea and concept    8
Application of the ideas and concepts    11
Conclusion    13
Reference list    15
Introduction
The concept of Big data can be understood as the data or data sets that are big in volume which makes it difficult to process the data or the data sets using traditional software or traditional methods. John Mashey, the chief scientist at Silicon Graphics is said to have introduced the term 'Big Data'. The term 'Big Data' was popularised by Doug Laney, an industry analyst who defined the term by using three defining properties - Volume, Velocity, and Variety conventionally known as 3Vs of Big data (Safhi et al., 2019). The concept of Big Data Analytics can be understood as the methods or processes that are capable of assessing a huge amount of data to reveal secret patterns, correlations, and other meaningful insights.
The fact that Big Data Analytics is capable of examining a huge amount of data to ascertain irregularities makes Big Data Analytics an appealing option to combat cybercrimes and thus establishes its importance as a tool for cyber security. The stored data and the assessment of those data can prevent a probable attack through the revelation of secret patterns. Apart from this, Big data analytics is capable of detecting malware attacks and other cyber security threats (Angin et al., 2019).
A report published on csoonline.com mentions that 84% of the business organizations in the US recognizes that Big Data Analytics helps in enhancing cyber security. The programs related to Big Data Analytics are capable of foreseeing upcoming cybersecurity threats and the intensity of those threats. This assists in establishing a predictive model that can issue an alert in case of a cyber-security breach. All these indicate that with the support of artificial intelligence and machine learning, the adoption of big data analytics for cyber security can enhance the cybersecurity framework. Although there exist several advantages in using Big Data Analytics in cybersecurity the disadvantages must be assessed also. The three defining components - velocity, variety, and volume also raises the chance of being a target of probable attackers.
The operations that big data analytics perform to ensure cyber security require storage of data and a report released by Gemanto, a cyber-security vendor reveals almost 2.6 million data records were breached in 2017. A report published by Verizon stipulated that exfiltration on systems occurs almost within an hour after data breaches. All these indicate that the adoption of big-data for cyber security may prove beneficial but concerns can be raised about the privacy of the stored data.
Literature review
According to Pham, C. M. (2019), big data analysis is applied to maintain cyber security in present time. Big data cyber security has become a novel application for tracking cybercrime. Many organizations are using different new approaches to track cybercrime. The organization is using the concept of the maturity model, this model aids the organization to establish systematic approaches for measuring and improving its maturity level. This article focuses on the maturity framework that is needed in big data cyber security analysis. The maturity framework had five stage levels and seven dimensions. The five stages of maturity framework are i) initial, ii) repeatable, iii) defined, iv) managed and v) optimizing.
The organization, human, infrastructure, data management, analytics application, governance and security dimension are the seven dimensions of the maturity model. The maturity framework provides a clear knowledge regarding the improvement of the dimension and this model also helps to select the path that ensures the successful application of big data analysis in the organization. All organizations need to maintain security in their organization. In recent times the increase of online access and online business has increased the rate of cybercrime. This cybercrime can be maintained with a systematic framework and with the help of advanced technology.
In the era of technology, the use of the internet is increasing, billions of devices are connected with the internet. The information is spread and stored in the closed. This is creating a scope for an increase in a cyber-attack. In the past decade, the storage of data due to mobile computing, mass storage and communication has created big data that can be analysed to track cybercrime. The detection of the cybercrime should be rapid to prevent this cyber-attack. Big data analysis is a tool that can process huge data in real-time, so this method could be used in the evaluation of cyber threats. Big data is collected or accessed from sensors, cloud, computers and networks (Angin, Bhargava & Ranchal, 2019).
Cyber threat analysis can identify or detect the problem in real-time. This information is useful to prevent cybercrime and develop strong security for users. There are multiple benefits in cloud computing, it is a technology for storing data and people and organizations are accessing cloud computing as this is user friendly. The use of cloud is the reason for unexpected risk due to lack of monitoring and lack of control in shared data.
According to Kantarcioglu, M., & Xi, B. (2016, October), Data analysis techniques are used to detect malicious attacks, security must be provided with the changing patterns of the cyber-attack. The big data analysis has the ability in the quick detection of the attack. Numerous advanced data mining techniques help in data analysis. This article focuses on the application of data mining techniques for controlling cyber security. The author has discussed intrusion detection and mobile malware detection. This is the important application for data mining and application of this is done in the data management. It detects the flow of the information and changes in the information flow pattern in a complex networking system. Intrusion detection is a system that monitors network trafficking.
Sometimes the attacker hacks and maintains the normal flow of information, this helps to trace the irregular hidden activity. Mobile malware detection is used in smart cell phones as it has a complex programming system. The malware detection is necessary for maintaining privacy. Huge access to personal information on the network has a greater risk for a cyber-attack. Many malware detections have been developed to detect and prevent malware attacks.
In the present technological developed era, communication has become advanced and data processing technologies are being benefited for the smart grid. The smart grid is a network that can flow both electricity and data and it has self-healing ability. A smart grid also faces cyber security threats, the smart grid has traditional protection for detecting cybercrime (Wu t al., 2016). In the present advanced technology, a problem can occur in a few seconds or in very less time. This threat can be overlooked in the traditional protection system.
This thread disturbs the normal activity of the smart grid and this affects a lot of smart grids. The traditional protection security is taking a lot of time for taking action after the detecting and rectification of the damage is becoming difficult. This issue of poor security in the smart grid can be solved with the application of big data analysis in the smart grid. This is highly effective for taking measures of cybercrime. Smart grids are used by the policymaker and they have an effective role in developing the smart grid. For the quick development of the smart grid technology, incentives have to be provided to the previous adopters. A smart grid is used as this reduces the cost, conserves energy, and has transparency. These factors help to maintain transparency. The security of the smart grid has to increase or in other words, the development of the security is needed.
Cyber security is an international matter, more than fifty countries have published cyber strategies. The author had discussed information security and cyber security (Almuhammadi & Alsaleh, 2017). The author had argued that information security and cyber security is not the same though both are overlapped. Still, cyber security is used as an analogous term of information security. Information security defines the security of private or confidential information; this has a high threat of misuse by others. Cyber security is the protection of mobile devices, networks, computers, electronic systems, data and servers from malicious attacks. Cyber terrorists can attack a country's infrastructure with the help of cyberspace. This attack may be caused directly or indirectly. Indirectly, they attack with the help of information services. Cyber-attack that occurs in the country is the risk for the entire population. An example of this type of attack had occurred in April /May of 2007 on Eatonia.
According to Barnea, A. (2019) since 2000 big data information is helping in the development of business sectors. The big data are used by the companies to develop new strategies and this data helped them to compete with their competitors. The data mining is used to understand the pattern of the information. With the advancement of technology, rapid development was there in the industry. The new challenges faced by them as cyber terrorism, this was the thread for all the people. Tightening of cyber security was necessary for maintaining the confidential documents.
Role of big data in cyber security
Use of machine...