Critical Information Infrastructure Protection Techniques Implementation Against Cyber Attacks Using Big Data Analytics (a Case Study of INEC & JAMB)
Chapter One
Aims and Objectives of the study
The main aim of this study is to investigate the implementation of critical information infrastructure protection techniques against cyber attacks using big data analytics. Specifically, the study seeks to:
- Investigate the efficacy of big data analytics as a protection technique.
- Examine the extent of big data analytics implementation in government agencies.
- Elucidate on the challenges in implementing big data analytics as a protection technique.
CHAPTER TWO
LITERATURE REVIEW
INTRODUCTION
Finding dynamic or proactive security measures from data analytics is what cyber security analysis is all about. When network traffic is monitored in order to detect compromise before a real danger arises, this is one example of this. When it comes to assaults and threats, no infrastructure or organization can predict the future, but with the correct security analytic tools in place to monitor security events, it is possible to detect a danger before it arises or has a chance to create havoc.
Literature review refers to the critical examination of state of knowledge including substantive findings as well as theoretical and methodological contribution to a particular topic. In line with this definition, the literature reviewed revolved around the exploration of the intrinsic meaning of variables under study.
Our focus in this chapter is to critically examine relevant literature that would assist in explaining the research problem and furthermore recognize the efforts of scholars who had previously contributed immensely to similar research. The chapter intends to deepen the understanding of the study and close the perceived gaps.
Precisely, the chapter will be considered in three sub-headings:
- Review/Explanation of important/relevant terms and technologies
- Review of Similar existing systems/previous related works
- Identification of gap from existing systems reviewed and solution to be proffered by this project
REVIEW/EXPLANATION OF IMPORTANT/RELEVANT TERMS AND TECHNOLOGIES
Concept of Critical Information Structure
Critical information infrastructure is described by Aladenusi (2015) in his presentation at the Nigeria Computer Society’s 12th international conference as those ICT infrastructures that are dependent on core assets that are important for the running of the organization. He went on to say that if such assets are compromised, it has a disastrous effect on national security, government, the economy, and the country’s overall status.
Food and agriculture, dams, financial services, oil and gas, commercial facilities, communication, defense, emergency services, power and energy, government and facilities, information technology, healthcare, transportation systems, and water and sanitation are among the 15 industry sectors defined as critical information infrastructure in Nigeria, according to Aladenusi (2015).
The importance of critical infrastructure in nation-building is demonstrated by the fact that critical information infrastructures are interdependent on a large number of services and infrastructure, and the failure of any of these CII infrastructures causes a catastrophic domino effect that negatively impacts other services.
Concept of Big Data
Big data is data that is too complicated to be managed, searched, or analyzed using typical data storage systems, algorithms, or query techniques (MessageLabs Intelligence, 2010). The three V’s define the “complexity” of big data:
1) volume – refers to the information of data held in terabytes, petabytes, or even exabytes (10006 bytes).
2) variety – this refers to the coexistence of unstructured, semi-structured, and structured data, as well as
3) velocity — the rate at which big data is created. The fourth V, veracity, has been introduced by some academics to emphasize the necessity of keeping high-quality data within an organization.
Data from computer networks, telecommunication networks, banking, healthcare, social media networks, bioinformatics, E-Commerce, surveillance, and other sources are some of the most common sources of big data transactions.
According to Cisco, global IP traffic will surpass 1000 Exabytes (1 zettabyte) by 2016. (Cisco, 2015). To put the size of the data being discussed in context, one zettabyte is the same size as the Great Wall of China (Arthur, 2011). Big data is the term for this avalanche of data. Big data, on the other hand, is about more than simply volume. It’s also about velocity and variety. Variety refers to a wide range of data forms and forms, including video, audio, photos, text messages, and email, as well as data created by sensors and machines. The speed (including real time) at which these data are created, processed, and transferred is referred to as velocity. Despite the fact that there are additional qualities, big data is primarily defined by the “three Vs” – volume, variety, and velocity (Gartner, 2012).
CHAPTER THREE
METHODOLOGY/SYSTEM ANALYSIS AND DESIGN
Introduction
This chapter gives the methodology that the researcher used in the study. The research design, population of the study, sample and sampling techniques, methods of data collection, variables and measurement, method of data analysis, and ethical consideration
Research Design
Research designs are perceived to be an overall strategy adopted by the researcher whereby different components of the study are integrated in a logical manner to effectively address a research problem. In this study, the researcher employed the survey research design. This is due to the nature of the study whereby the opinion and views of people are sampled.
Population of the Study
According to Udoyen (2019), a study population is a group of elements or individuals as the case may be, who share similar characteristics. These similar features can include location, gender, age, sex or specific interest. The emphasis on study population is that it constitute of individuals or elements that are homogeneous in description.
This study was carried out to investigate the implementation of critical information infrastructure protection techniques against cyber attacks using big data analytics. The Staff of Joint Admissions and Matriculation Board (JAMB) and Independent National Electoral Commission (INEC), Abuja form the population of the Study.
Statistics derived from the sampled respondents website shows that the estimated population is 502.
CHAPTER FOUR
DATA PRESENTATION, ANALYSIS AND DISCUSSION
Introduction
In this chapter, the researcher presents an analysis of the data collected from the survey. The data used for this chapter was analysed using the statistical package for social science (SPSS v.23). The demographic analysis of respondents were first discussed, followed by the research questions. Finally, the research null hypotheses were tested using the logistics binary regression.
CHAPTER FIVE
SUMMARY OF FINDINGS, CONCLUSION AND RECOMMENDATION
Summary of findings
This study was carried out to examine the implementation of big data analytics as a protection technique for critical information. Specifically, the study examine the efficacy of big data analytics in information protection, it also examined the extent of big data analytics implementation. The motivation for this study is premised on the need for data protection against cyber attacks in Nigeria. The study adopted the survey research design to carry out this study. A total of 121 staff members of the Joint Admission and Matriculation Board (JAMB) and the Independent National Electoral Commission (INEC) were enrolled in the survey. The study employed the binomial logistic regression analysis to test the hypotheses formulated. The findings from the study shows that at 1 degree of freedom p-value < 0.05, big data analytics hold better advantage in protecting large information held by organizations against cyber attacks, also that there was no significant (.505 > 0.05) application of big data analytics in government agencies, and there are significant challenges (.002 < 0.05) impeding the application of big data analytics in institutions and organizations.
Conclusion and Recommendation
Data comes from a variety of sources, including demographic data, climatic data, scientific and medical data, energy usage data, and so on. All of these data give information about the devices’ users’ whereabouts, travel, interests, consumption patterns, leisure activities, and projects, among other things. However, there is also data on how infrastructure, machinery, and apparatus are utilised. The volume of digital data is continuously increasing as the number of Internet and mobile phone users continues to rise. We now live in an Informational Society that is transitioning to a Knowledge Based Society. A larger volume of data is required to extract superior understanding. The Information Society is a society in which information plays a significant role in the economic, cultural, and political spheres. Data that exceeds the storage, processing, and computational capability of traditional databases and data analysis methodologies is referred to as big data. Big Data as a resource necessitates the use of tools and methods for analyzing and extracting patterns from enormous amounts of data. Big Data Analytics is a rapidly evolving field. It has been adopted by the most unlikely industries and has grown into its own industry. However, analyzing these data in the context of Big Data is a process that can be rather intrusive at times. Organizations, institutions, and agencies should fully adopt big data analytics, according to this study. Furthermore, in conjunction with the use of big data, staff should be trained in order to obtain the necessary abilities for executing big data analytics. For optimal implementation, this study also suggests purchasing software and a complete big data package.
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