Information has been always the most crucial for every sector and thus, information system in the present world has become the key for reaching at the top of the competitive environment. Many information technologies have come in trend after evolvement in the information systems and information technology such as Cloud computing, Data mining, Big Data and many more those are improving the existing ways of accomplishing the operational activities. Big Data analytics can be defined as the set of practices for examining the varied and large data sets in manner to detect the hidden patterns, customer preferences, market trends, unknown correlations and many more those can be helpful in better decision- making for the organizations. This report will be emphasizing on the adoption of the Big Data Analytics technology and services those have been adopted by the industries in manner to improve the existing environment and ways of the delivery of the operational activities.
Big data security analytics is type of approach is associated with providing an improved rate of detection. The detection techniques must be capable of identifying the changes in the use pattern for the purpose of executing the complex analysis process at a rapid rate on a real time basis. This is to be done so as to perform the complex correlations across a variety of data sources which generally ranges from the server and application logs to network events and the user activities.
There is a requirement of both advanced as well as simple rule based approaches along with the capability of running the analysis on huge amount of data that are historical as well as current (Cardenas, Manadhata, & Rajan, 2013). With the combination of the current state of analytics and security it is possible for almost all organizations to have an improved security of the information system that they are having.
This is one of the new generation security analysis that has helped a lot in the collection, storage and analysis of the huge amount of data for the purpose of securing the data. This are in turn enhanced by the addition of the context data as well as the external threat intelligence and by this the data re analyzed by making use of the correlation algorithms which helps in determination of any type of anomalies which in turn helps in the identification of the various kind of malicious activities (Gandomi & Haider, 2015). Once this are identified certain steps can be taken in order to secure the information system.
The traditional SIEM systems which were used generally operated in near to real time and was associated with the generation of less amount of security alerts which were generally ranked in accordance to the severity. Followed by this certain additional forensic details were used so as enrich the alerts which initially helped in the simplification of the job of the security analyst and helped them in taking quick steps so as to detect and mitigate the various threats to the system (Hu & Vasilakos, 2016).
Storing data and information in a virtual place make them vulnerable to data breaches and intrusion and hence, security has become one of the concerning sector for the application of the Big Data analytics in the real world. Rate of cyber attacks have been rapidly increasing and most of the intruders are targeting the data and information collected using Big Data as it could be a treasure for them to utilize the same sensitive information of others’ for personal benefit. These are some of the reasons those led to the enhancement and upgrade in manner to protect the privacy and security of the individuals whose data and information are being collected for the benefit of the companies.
Coupling of the big data security analytics with the sources of external security intelligence has helped a lot in providing of the current information regarding the vulnerabilities which are latest and this in turn is associated with helping a lot in the identification of the ongoing threats which would initially help in taking of steps that are helpful in the mitigation of this threats (Najafabadi et al., 2015).
Simplification of the initial calibration to a normal pattern of activities is also possible in case when there exists large amount of data. Thin in turn is used for the detection of the anomalies. The solutions that are being used are having the capability of automating the calibration by having a little amount of input (Thuraisingham, 2015).
Big data security analytics is capable of reducing massive flow of the raw security events to a certain manageable number of alerts which are concise as well as categorized in a clear way. This is mainly done by filtering out of the various statistical noise. This filtering out would be associated with allowing personals to take decisions on this and along with this an unexperienced person is capable of taking decisions by making use of this process (Xu et al., 2014). By making the historical data available for later analysis helps the forensic experts in receiving of more details about the various incidents along with providing information about the relation that the recent anomaly is having with the past anomalies.
Besides this the modern big data analytic solution is associated with providing numerous amount of automated workflows in order to provide response to the various kind of threats that have been detected and this includes the disruption of the malware attacks which have been identified or for the purpose of submitting the various suspicious events to the managed security services in order to have a further analysis of the issues (Suthaharan, 2014).
Various changing laws as well as regulations have a high impact upon the security posture that the organizations are having. The new laws and regulations are generally having the goal of ensuring the fact that all the new information systems are being properly secured and each member who are associated with the business is understanding the standards that have been defined along with understanding any kind of deviations from the standards (Abbasi, Sarker & Chiang, 2016). The new laws also helps in the process of measuring the importance that information security is having along with understanding the current state of security of the information system which utilizes the big data analytics. Besides this new changes also helps in making of future plans regarding the initiative of the big data security analytics all around the various sectors associated with putting forward of the presentation of an overview of the different kind of opportunities benefits as well as the challenges that are related to all this initiatives (Gahi, Guennoun & Mouftah, 2016). This regulations also helps in outlining the ranges of the technology that are available in order to address the various kind of challenges.
Conclusion
Based on the above report it can be concluded that the Big Data analytics have been far more beneficial for the organizations in manner to take the better decision-making that can alternatively lead to the enhancement in the performance and better customer targeting. Due to all this reason the organizations which are facing problems needs to adopt a more holistic as well as an in depth view of the various risks as well as the incidents that they are facing. Big data analytics is in having the capability of meeting the requirements of the organization so as to tackle the incidents as this provides a wide insight of the situation. The insight is provided by analyzing the vast as well disparate data sources both internal as external. This is almost a standard practice for various parts of a business. However, security has been another concerning objective in this field considering the privacy and security of the individuals who are connected with this technology. Acquiring effective strategies for tackling the cyber attacks and eliminating the cyber flaws, the security, and privacy of the data and information being collected can be enhanced in an efficient and effective manner.
References
Abbasi, A., Sarker, S., & Chiang, R. H. (2016). Big data research in information systems: Toward an inclusive research agenda. Journal of the Association for Information Systems, 17(2).
Cardenas, A. A., Manadhata, P. K., & Rajan, S. P. (2013). Big data analytics for security. IEEE Security & Privacy, 11(6), 74-76.
Gahi, Y., Guennoun, M., & Mouftah, H. T. (2016, June). Big data analytics: Security and privacy challenges. In Computers and Communication (ISCC), 2016 IEEE Symposium on (pp. 952-957). IEEE.
Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137-144.
Hu, J., & Vasilakos, A. V. (2016). Energy big data analytics and security: challenges and opportunities. IEEE Transactions on Smart Grid, 7(5), 2423-2436.
Najafabadi, M. M., Villanustre, F., Khoshgoftaar, T. M., Seliya, N., Wald, R., & Muharemagic, E. (2015). Deep learning applications and challenges in big data analytics. Journal of Big Data, 2(1), 1.
Suthaharan, S. (2014). Big data classification: Problems and challenges in network intrusion prediction with machine learning. ACM SIGMETRICS Performance Evaluation Review, 41(4), 70-73.
Szczypiorski, K., Wang, L., Luo, X., & Ye, D. (2018). Big Data Analytics for Information Security. Security and Communication Networks, 2018.
Thuraisingham, B. (2015, March). Big data security and privacy. In Proceedings of the 5th ACM Conference on Data and Application Security and Privacy (pp. 279-280). ACM.
Xu, L., Jiang, C., Wang, J., Yuan, J., & Ren, Y. (2014). Information security in big data: privacy and data mining. IEEE Access, 2, 1149-1176.
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