Table of Contents
Project Overview
The focus of this research is studying ways in which analytics can be used in information security risk management to detect and prevent information security threats, attacks, as well as malicious activities. Particularly, this study will evaluate the effectiveness of big data analytics for the detection of complicated and stealthy attacks. The study’s hypotheses includes: analytics are the most effective tools for detecting and preventing cyber-security attacks and using the Big Data analytics, as the overall strategy, is important in ensuring the security of information systems. The sample size for this study will be 31 large organizations. A quantitative research method will be used as the study’s methodology, with the use of SPSS in analyzing collected data.
Hypothesis and Justification
Today, to argue that security risks are widespread and diverse is a significant understatement. With today’s society increasing becoming independent on technology, and the advent of the internet of things, security attacks and risks have become worse and still remains a disastrous consequence of the human actions. Just as the human bacteria and other deadly organisms exist within the human society, so are the malware and other threats to the information security management. Additionally, just there are also significant and concerted efforts in handling the threats of drug resistant organisms, so is the information security community striving to find solutions into the growing information security risks. As such, big data analytics may be used in efforts to protect the critical information infrastructure for organizations. There are several types of risks and threats, which most organizations encounter. Thus, understanding what these risks and threats are remains necessary and relevant.
From previous literature, it is largely clear that even though big data analytics is seen as the most powerful and relevant technology in detecting and preventing risks and attacks, there exist limitations, especially in the documentation of how exactly to analytics can be used. In addition, there appears to be lack of abstract model of proper practice regarding ways in which analytics can be used for attacks and risk detection. The presence of such a model would be highly important in assisting new organizations, which often seek to use technology in their activities, and therefore, preventing potential attacks and even saving money. Given this research gap, the main hypotheses for this research are offered below:
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- Hypothesis 1: Analytics are the most effective tools for detecting and preventing cyber-security attacks.
- Hypothesis 2: Using the Big Data analytics, as the overall strategy, is important in ensuring the security of information systems.
Looking at the review analysis as well as synthesis, including that performed by Cardenas, Manadhata and Rajan (2013), it is clear that, even though traditional security systems are still being used today, they are no match for cyber-security attacks and threats that are being launched by attackers. This case is partly true, as explained by Cardenas, Manadhata, and Rajan (2013) because traditional systems are majorly based on traditional analytics, which are further, based on limited and poor storage capacity, slow speed, as well as specified data type. In this regard, there is therefore, need for much more dynamic systems.
Moreover, looking at the era of today’s cloud computing and information systems, the traditional network perimeter remains nonexistent since people can easily connect to organizations’ systems from any remote devices and areas. In this case, traditional systems, when compared to the modern analytic tools, are no long efficient. As such, modern analytics are considerably effective, since they have been developed to effectively handle network and information perimeters and are largely ‘detection and prevention-based.’ As suggested by Brewer (2015), modern tools, and particularly, analytics such as Big Data, are more appropriate for the current information and cyber-attack landscape. What is more, the compelling theme that is emerging today is that a more dynamic system, which is centered on Big Data analytics, provides the answers and solutions to address the current risks and attacks to the information systems and management.
Supporting Evidence
Analytics is swiftly becoming a central tool in today’s increasingly digitalized world. These analytics are not only being used in large organizations to help in decision making but in many other fields, including in the discipline of artificial intelligence (AI), information security, as well as other related areas. McAfee and Brnjolfsson (2012) presents the significance of big data analytics through offering an example in which real time location information from users’ smartphones were utilized in determining the number of shoppers at Macy’s parking lot on Black Friday during the start of the Christmas shopping period in America. As such, according to McAfee and Brnjolfsson (2012), this data worked to assist analysts in estimating the retailer’s level of sales even before the real sales were recorded.
Furthermore, combined with other machine-learning algorithms, these analytics have also been used to design artificial intelligence systems, which are good at doing tasks that over the past, only the humans could perform. An excellent example to demonstrate this concept is the case of IBM’s Watson, which defeated the best minds at the game of Jeopardy which was held in the year 2012, as explained by Ferrucci (2012). Another typical example of machine-learning today is the use of driverless vehicles. As for Gibbs (2014), even though these technologies have not completely exceeded humans, there is overwhelming evidence that such machines have mastered the art of doing different activities, including driving.
The main point here is that analytics have the ability to offer the most powerful tools for organizations in making better and enhanced decision, since such analytics could offer an excellent picture of any given event even before the event occurs. As such, this makes analytics much perfect and suitable while rendering them potent tools in detecting and preventing information security attacks. While discussing the benefits of big data, Tankard (2012) explained ways in which the application of analytics could be used to detect and deter cyber-attacks. For instance, Tankard (2012) suggests that organizations may decide to mine the large amounts of information they gather for possible security attacks, including the detection of phishing practices and malware attacks on their computer systems.
Though information security attacks pose significant risks, the lack of analytics use results in far-reaching consequences for organizations and societies. Some of these consequences have been demonstrated in several companies today, including the Telecoms Company, which owns as well as maintains the physical infrastructure making United Kingdom’s (UK) broadband network. According to Williams (2016), the organizations faced an outage of a part of its broadband services, which led to many of its customers losing connection to the internet for approximately two hours. Undoubtedly, this was one of the greatest and most extensive system failures in years. Despite the organization denying it and arguing that it resulted from a defective router, there is overwhelming proof that it was particularly due to cyber-security attack. Regardless of the company’s stand, the fact stands that it is possible for attackers to launch attacks, which can take an organization’s activities and critical infrastructure down.
In yet another example, during the JPMorgan security attack, it took approximately one month for analysts to detect the attack. However, JPMorgan’s case is never an isolated one. In Sony’s significantly publicized, the attack took several days to be discovered, just like in the case of JPMorgan. Finkle and Heavy (2014) noted that even though a remarkable component of the attack was that it was detected by the security systems of the company, there was reluctance on the part of the organization’s personnel. Thus, the question that has been asked over time is, ‘‘how does one deter against such attacks, and could big data analytics be the ultimate solution?’’ In his prediction in 2014, Rivera (2014) made a suggestion that huge organizations could turn to big data analytics for security breaches and attacks by the year 2016. Therefore, it is apparent that analytics will permit most of the organizations using its tools to witness greater and stronger pictures of threats and risks, and thereby, enabling them to detect and prevent such security dangers from happening.
Review of Relevant Research
This review of relevant literature especially focuses on the relevance and important of big data analytics in informing security, how such tools have been applied previously for data detection and prevention within the information systems field, and its use in the critical information infrastructure.
Big Data Analytic for Cyber Security
According to the Information Security Forum (2012), there are several and potential positive impacts and enhancement when using big data on information systems’ securities. According to this forum, organizations have to shift and move away from reacting to security attacks towards identifying and deterring such incidents. The report also determined that the application of big data analytics could assist in improving information security, especially through decreasing risks while at the same time, increasing the level of agility (Information Security Forum, 2012).
In his research study, Tankard (2012) also supports the findings made by Information Security Forum (2012), arguing that the utilization of big data accrues several benefits, with the most compelling one being operational efficiency, especially for most of the large and commercial organizations. Tankard’s (2012) argument of ‘operational efficiency’ can be dissected to comprise cyber-security attacks, since keeping systems secure forms part of organizations, and as such, the use of big data analytics might be useful in detecting threats, hacks, and other criminal activities such as phishing. Tankard (2012) also asserts that apart from its benefits to large organizations, big data analytics could also be useful for governments in ensuring safety of systems for organizations.
In this case, Tankard’s (2012) idea is that since the data collected from big data analytics is of great significance, it can be used to prevent possible and actual attacks. In other words, analytics can be applied by different organizations to improve and boost productivity while equally making their information systems much secure. However, for big data analytics to be successful, Tankard (2012) argues that security access controls must be moved from network perimeter towards the data assets, which require defense.
Just like Tankard’s (2012) research study, the study by Cardenas, Manadhata, and Rajan (2013) praises the application of big data analytics though their main focus is its use for cyber security. Accordingly, Cardenas, Manadhata, and Rajan (2013) argue that the use of big data in the field of information technology is not knew and that such tools have been used in monitoring network traffics as well as analyzing system logs in addition to other data sources in efforts to discover risks or malicious attempts on information systems. Cardenas, Manadhata, and Rajan (2013) also assert that one of the major effects of the application of big data technologies lies in the facilitation of the creation of cheap infrastructure.
For Curry et al. (2013), their study on the potential use of data analytics projected that it would change the status quo of many of the information security aspects, including detecting fraud activities, monitoring issues to do with authentication, while at the same time, evolving to possess greater prognostic and instantaneous features. Curry et al. (2013) state that just like the application of big data analytics has worked to transform the competitive dynamics in most of today’s commercial organizations, it would also help to significantly improve the information security platform. For Curry et al. (2013), the use of big data analytics remains relevant largely because they are able to make any system better compared to traditional ones.
Equally, and discussing the tremendous benefits of the use of big data analytics, Mahmood and Afzal (2013) also present that such technologies offer the solution to the growing concerns of threats from cyber-attacks. Just like the findings of Curry et al. (2013) as well as Cardenas, Manadhata, and Rajan (2013), they are convinced that traditional security tools are not good enough in detecting and preventing security concerns as the use of big data analytics. Nevertheless, Mahmood and Afzal (2013) agree that for big data analytics to become effective, then data must be pooled from different sources. They also argue that such data must be complex and cutting-edge in addition to enabling interactive use interface.
Critical Information Infrastructure Protection Models
One important model of protecting organization’s critical information infrastructure (CII) is information sharing as proposed by Ulltveit-Moe et al. (2013). In particular, they argue that information sharing, among computer emergency response professionals as outlined by EU is important. According to Ulltveit-Moe et al. (2013), not only will information sharing among different organizations on security attacks aid to enhance the security of such organizations but will also decrease the costs associated with protecting their own systems. However, there are several challenges that confront this model, including lack of trust, standards of enforcing privacy, as well as lack of awareness. To overcome these challenges, the PRECYSE method, which stands for prevention, protection, and reaction to cyber-attacks to critical infrastructures, should be used.
Another model of protecting the CIIs is that suggested by McLaughlin et al. (2014), which is also based on the concept of PRECYSE. As such, McLaughlin et al. (2014) offer an intriguing claim that security for organizations’ CIIs must be based on money as opposed to the strength of the different forms of attacks faced by such organizations. Unlike the two models discussed above, Ma, Smith, and Skopik (2013) recommend the use of security analysis exercise as a major aspect in protecting organizations’ CIIs.
Main Purpose/Focus
The major focus of this research includes studying ways in which analytics can be used in information security risk management to detect and prevent information security threats, attacks, as well as malicious activities. Particularly, this study will evaluate the effectiveness of big data analytics for the detection of complicated and stealthy attacks, including APTs. Because attacks and stealthy malwares are meant to be undetected, an attack may therefore, pose a significant effect on systems within a matter of seconds.
Methodology
The research methodology chosen for this study will be quantitative research method. Considering the nature of the information to be collected in this study, the data analysis will be performed using descriptive statistical techniques, including such tools such as frequency tables, charts, and regression analyses. To facilitate this process, the researcher will make use of statistical software, and in this case, SPSS will be utilized.
Sample
The target audience for this study will be large organizations whose activities and operations are particularly critical in their nature, especially in relation to a country’s economy. The sample size will be 31 organizations drawn from diverse fields.
Procedures
After designing survey questionnaires, it will be tested with the study’s target audience. In efforts to accomplish this, the questionnaire will first be send to about 10 cyber-security experts and later obtain their feedback. To gather data, these questionnaires will be deployed using online survey service platforms, such as SurveyMoney.com.
Measures
The above sample size has been determined using a number of measures, including confidence interval as well as the z-score.
Data Analytics Plan
. To gather data, these questionnaires will be deployed using online survey service platforms, such as SurveyMoney.com. Once the information has been collected, it will be assessed and analyzed using SPSS.
References
- Brewer, R. (2015). Cyber threats: reducing the time to detection and response. Network Security, 2015(5), 5-8.
- Cardenas, A. A., Manadhata, P. K., & Rajan, S. P. (2013). Big data analytics for security. IEEE Security & Privacy, 11(6), 74-76.
- Information Security Forum, (2012). Data Analytics for Information Security: From hindsight to insight. London: Information Security Forum Ltd, pp.1 – 3.
- McLaughlin, K., Sezer, S., Smith, P., Ma, Z., & Skopik, F. (2014, September). PRECYSE: Cyber-attack Detection and Response for Industrial Control Systems. In ICS-CSR.
- Rivera, J. (2014). By 2016, 25 Percent of Large Global Companies Will Have Adopted Big Data Analytics For At Least One Security or Fraud Detection Use Case.[online] Gartner. com.
- Tankard, C. (2012). Big data security. Network security, 2012(7), 5-8.
- Ulltveit-Moe, N., Gjøsæter, T., Assev, S. M., Køien, G. M., & Oleshchuk, V. A. (2013, July). Privacy handling for critical information infrastructures. In INDIN (pp. 688-694).
- Williams, R. (2016). BT broadband suffers major outage across UK. Retrieved from https://www.telegraph.co.uk/technology/2016/02/02/bt-broadband-suffers-major-outage-across-uk/.
