Big Data Analytics in Cyber Security (original) (raw)

Internet is a changing technology and it is impossible to anticipate the changes that are occurring constantly. Every change leads to more dangerous threats that had not been previously imagined, reason for this condition is expeditious expansion in internet innovations. This elaboration of the cyberspace has involved with it an aggressive raise in the type and frequency of cyber-attacks. So there is a need of cyber security solutions to block these attacks. However, the generation of Big Data over computer networks is expeditiously rendering these traditional solutions are outdated. To overcome this problem, corporate researchers are now focusing on Security Analytics, i.e., the application of Big Data Analytics approach to cyber security. The security in big data analytics has the ability to congregate huge amounts of data to be analyzed visualized and drawn insights that can make it possible to predict and avoid cyber-attacks. This paper will presents a inclusive survey on the state of the art of security analytics i e its representation, the technology, the trends and its tools. It hence achieves to satisfy the reader of the forthcoming application of analytics as an unparalleled cyber security solution in the near future.

This study aimed to understand and elaborate the issues and challenges in Network Security that Big Data Analytics can solve. Big Data's role in today's industrial revolution is incredible. It aids in decisionmaking, forecasting and understanding of patterns, planning and execution of strategies in achieving goals and objectives. The study aimed to know the: issues and challenges of Big Data in Cybersecurity particularly in Network Traffics, Intrusion, and Attacks; best practices and recent solutions that address the issues and challenges; and most advanced Big Data Solutions that will solve this issues and challenges. The study found out that Big Data may have an issue on Endpoint Vulnerabilities, Distributed Data, Access control and Multiple Security Requirements, Complexity due to the use of Different Technologies and, Data, Process and Management Challenges. Further, to counter measure these issues, best practices like application of:

The technological and social changes in the cur- rent information age pose new challenges for security analysts. Novel strategies and security solutions are sought to improve security operations concerning the detection and analysis of security threats and attacks. Security analysts address security challenges by analyzing large amounts of data from server logs, communication equipment, security solutions, and blogs related to information security in different structured and unstructured formats. In this paper, we examine the application of big data to support some security activities and conceptual models to generate knowledge that can be used for the decision making or automation of security response action. Concretely, we present a massive data processing methodology and introduce a big data architecture devised for cybersecurity applications. This architecture identifies anomalous behavior patterns and trends to anticipate cybersecurity attacks characterized as relatively random...

Machine Learning is an Approach to AI that uses a system that is capable of learning from experience. It is intended not only for AI goals (e.g., copying human behavior) but it can also reduce the efforts and/or time spent for both simple and difficult tasks like stock price prediction. In other words, ML is a system that can recognize patterns by using examples rather than by programming them. Big data analytics in security involves the ability to gather massive amounts of digital information to analyze, picture and draw insights that can make it possible to predict and stop cyber attacks. Along with security technologies, it gives us stronger cyber defense posture. They allow organizations to recognize patterns of activity that represent network threats. In this paper, I emphasis on how Big Data be able to progress information security best practices. I am trying to apply machine learning procedures in cyber security using big data Analytics.

—Big Data is related to technologies for collecting, processing, analyzing and extracting useful knowledge from very large volumes of structured and unstructured data generated by different sources at high speed. Big Data creates critical information security and privacy problems, at the same time Big Data analytics promises significant opportunities for prevention and detection of advanced cyber-attacks using correlated internal and external security data. We must address several challenges to realize true potential of Big Data for information security. The paper analyzes Big Data applications for information security problems, and defines research directions on Big Data analytics for security intelligence.

In the area of the fastest growing fields of ICT technology which determine the successive stages of progress in the field of online electronic banking, it is necessary to disseminate standards for conducting financial operations carried out in the so-called cloud as well as using the large data sets located in the so-called Big Data platforms. Current Big Data technology solutions are not only large data bases, data warehouses allowing for multi-aspect analysis of huge sets of quantitative data made for the purposes of reports submitted periodically to the managerial staff. Currently emerging trends in the development of technology based on Big Data dataset platforms usually allow to perform multidimensional calculations and reported results of analyzes in real time. The analyzes conducted on huge data sets allow for comprehensive, multi-aspect risk assessments at the level of the whole entity. During the SARS-CoV-2 (Covid-19) coronavirus pandemic, there was an increase in the importance of analytics based on Big Data Analytics. During the pandemic, there was also an increase in the digitisation and internetisation of business processes. This also resulted in an increase In the scale of cybercrime development. the development of information technologies functioning on the Internet also involves the risk of loss or theft of information by unauthorized entities. The process of providing information via the Internet generates many threats related to identity theft, interception of classified data and cash embezzlement in Internet business systems. In response to these threats, particular entities are developing security systems for remote sharing of information and transactions conducted via the Internet. The pandemic and the 2022 energy crisis may result in an increase in the pro-environmental and pro-climate awareness of citizens and an acceleration of the processes of green transformation of the economy, including, inter alia, the development of renewable and emission-free energy sources. Analytics based on Big Data Analytics can be of great help in investigating these changing and/or consolidating trends.

Cyber crime over big data is expand with unprecedented rate that badly affects the Internet industry and the global data. Progressively sophisticated attack and offensive methods used by cyber attacker and the growing role of data-driven and intelligence-driven adversaries demonstrate that traditional approaches to mitigate cyber threats are becoming ineffective.

In the time of huge information, there are a great deal of examination strategies and procedures for analyzing large data sets and acquiring applicable outcomes that are proposed to be used for specific purposes in various ranges of business.In the virtual environments, many attacks are launched for obtaining advantages through information leakages from their targets. The motivation behind investigation techniques in digital security is to end up distinctly more adaptable with changes in adversary behaviors. Visual examination and prediction algorithms seem to contribute considerable a lot in resolving cyber security issues. Exploring large data sets, achieving knowledge, forensic investigation, are representing the most known cases in cyber security big data solutions.To get significant information from analytics, the most important steps to take before analyzing data are to normalize, eliminate duplicates and put it in a format that can enhance the proficiency of an algorithm. Normalizing data is a pre-process that incorporate capacities and systems for sorting, mining, connection information and so forth.