Implementation of Intrusion Detection System (IDS) as a Current Technology in Supporting Big Data Analytics (BDA)

Implementation of Intrusion Detection System (IDS) as a Current Technology in Supporting Big Data Analytics (BDA)

Overview

Big Data Analytics (BDA) is one of the common terms in the current technology world. It encompasses various technological approaches which have been implemented to enhance the fight for enhanced cybersecurity in the industry (Cardenas, Manadhata, & Rajan, 2013). Every organization is dealing with one or various sources of data which must be well organized to ensure easy retrieval and structured security implementation. After collecting data from various security technologies, it is imperative for organizations to analyze this data to ensure that its meaning is enhanced. While BDA is the very promising step which can be used to enhance security, it has to be tailored to specific area or organization since every organization faces different levels of challenges. Various recommendations on major literature reviews point at implementing BDA in the cybersecurity suite.

BDA subscribes to all five pillars of information security which includes protection, detection, reaction, documentation, and prevention of malicious actions. One of the most important technologies which false under the BDA facet is the Intrusion Detection System (Dhage, & Meshram, 2012). It is the most important element of enterprise systems which form the first line of defense.IDS supports BDA in ending the challenge of data collection which has always crippled the performance of BDA. IDS provides the combination of attractive and easy to use computer interface, scalable platforms, and data mining capabilities.IDS has the capability to keep data which is a true alignment to the documentation pillar of data security.

IDS support the security measures through information filtering to get the right piece of information or data which must be allowed into a given system. This will definitely help various decision-making organs in making investment decisions.

Annotated Bibliography for Big Data Analytics (Summary)

Big data can be used in various systems to detect anomalies in network infrastructure (Barrie, Razaq, & Tianfield, 2016). The understanding of these anomalies can help in development of appropriate mechanisms to counter-intrusion attempts.

Proactive Cybersecurity System (PCS) have also been implemented as a way of providing initial prevention mechanisms to shield the systems from attacks. It also has enough capabilities for data analysis which offers a great advantage compared to the traditional data analysis systems. The system also offers a list of possible vulnerabilities in the computer.

Song & Cuzzocrea (2014) explains various challenges which BDAs face as far as its implementation attempts are concerned. Many researchers have concentrated in unraveling the more advantages and good parts of the BDAs but a few of them have gone an extra mile to depict some of the growing challenges in data analysis.

The Delphi study is one of the most successful researches which seek to help organizations understand big data roles in supporting organization’s “bottom line” operations. Organizations are advised on efficient methods to use big data to obtain value in the operations (Stryk, 2015)..

The best method for foiling cybersecurity threats is the use of preemptive and proactive measures as opposed to reactionary methods. Ombrellaro (2016). It provides a specific area to commit resources in the network rather than making a general strategy of combating attacks.

The main reason attributed to increase in cyber attacks is large number amounts of data Senatorov et al provides a quick preview about how increased data in the systems can be of negative value in the fight against cyber attacks.

Guennoun, Mouftah, & Gahi (2016) offer various channels of potential risks, vulnerabilities and methods of attacks which can be used by criminals. The mitigation steps can, therefore, be instituted by the company by handling big data.

The article drafted by Wang et al. (2014) presents the growing needs for Big Data analytics and the challenges provided by such measures. It also explains in deeper means the essential parts of networks in protecting research conducted. The system shows the positive parts of Intrusion Detection System (IDS) and the demonstration of why IDS should be used as a supplement for Big Data analytics.

IDS offer information from outside sources which can easily increase confidence on the part of researchers thus solidifying their analysis. The article supports the fight to continue using Big Data analytics with better results. It also provides the proper evaluation of cybersecurity threats, cybersecurity infrastructure, software, hardware and the cybercriminals threats which can be used to exploit networks. The vulnerabilities presented offers proper guidance on the databasing, processing and mitigation procedures for the threats of these vulnerabilities. Cyber-physical systems are the current applications meant to reduce risks.

Cyber-security Issues which need Investigation

Data security entails upholding of four key security values which include confidentiality, integrity, availability and data completeness (Barrie, Razaq, & Tianfield, 2016). BDAs have been used to provide early detection of weak points which will eventually boost data integrity. Breach of data security due to various cyber attacks brings down data integrity by destroying data.

Cybersecurity issues that need investigating using the interactions between BDA and people

Many attacks are orchestrated by people within the organization. BDAs have been used to provide early and proactive measures to find the employees who are planning heinous actions (Guennoun, Gahi, & Mouftah, 2016). BDA also provide proper framework for responding to attacks through calculated steps of logs.

Cybersecurity-related costs and benefits associated with the adoption of BDA

Every step taken in cyber security will need proper calculation of costs and benefits relating to implementation process. Currently, an estimation of costs of cyber attacks have reached more than a billion while almost all computers and software in various organizations have once been affected by these attacks (Ombrellaro, 2016). Preventive measures offer far much less costs if compared to the cost of curing attacks.

Cybersecurity-related risks and vulnerabilities associated with the adoption of BDA

Use of PDAs come with various challenges which include sensitive data protection, rights to data access, ownership rights and people alienable rights to data analysis.

For BDA to be implemented, an economical perspective must be implemented. This requires large amount of data to offset the cost of analysis. Few people making analysis can easily be overwhelmed. On the part of data ownership, it is hard for the BDA system to analyze data which lacks its ownership (Nepal, & Jang-Jaccard, 2014). Costs of seeking permission for all these users are untenable hence creating a big problem to the system. On the basis of data rights, many resources have been committed to lawsuits emanating from people who do not believe in the safety of their information due to data analysis.

While all these challenges have been witnessed, BDA implementation can be justified due to its benefits in fast data processing as well as the formidable security supported by this system (Kazman et al., 2016). It is also believed that it provides a proper and structured arrangement of data for easy access and retrieval.

References

Barrie, P., Razaq, A., & Tianfield, H. (2016). A Big Data analytics based approach to anomaly detection. Proceedings of the 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies – BDC 16. doi:10.1145/3006299.3006317

Guennoun, M., Gahi, Y., & Mouftah, H. T. (2016). Big Data Analytics: Security and privacy challenges. 2016 IEEE Symposium on Computers and Communication (ISCC). doi:10.1109/iscc.2016.7543859

Kazman, R., Chen, H., Wang, P.  & Monarch, I.(2016). Predicting and fixing vulnerabilities before they occur. Proceedings of the 2nd International Workshop on Big Data Software Engineering – BIGDSE 16. doi:10.1145/2896825.2896829

Nepal, S. & Jang-Jaccard, J., (2014). A survey of emerging threats in cybersecurity. Journal of Computer and System Sciences, 80(5), 973-993. doi:10.1016/j.jcss.2014.02.005

Ombrellaro, A. (2016). Advanced analytics – a proactive approach to cybersecurity (Order No. 10153549). Retrieved from http://ezproxy.umuc.edu/login?url=http://search.proquest.com.ezproxy.umuc.edu/docview/1836076673?accountid=14580