Big data consists of large amount of data for the purpose of analyzing the data set and determine patterns. Data assets are identified followed by a process of exposure analysis. Risks and vulnerabilities are shown in this case study (Mahajan, Gaba & Chauhan, 2016). The asset classes of big data are identified and then level of risk exposure of the assets is assessed.
This report talks about the different threats as well as the key threat agents (Kao et al. 2014). The methods that can be used to minimize the impact of the threats are discussed in this report. It also explains how ETL process can be improved.
Big data consists of large amount of data for the purpose of analyzing the data set and determine patterns. Human behavior and preferences can be identified by analyzing big data. The usage of big data is gaining importance with time. This case study provides information about the security threats. Attackers are mostly targeting big data systems. Data assets are identified followed by a process of exposure analysis. Risks and vulnerabilities are shown n this case study. The asset classes of big data are identified and then level of risk exposure of the assets is assessed (Enisa.europa.eu, 2017). The security threats as well as their agents are also classified in this case study. The threats of the big data are all the ordinary data threats but are not limited to these threats (Patil & Seshadri, 2014). There are also new kinds of breach like degradation and leakage of data that are specific in case of Big Data. There is significant impact of the data protection as well as privacy. There can be conflict among the several asset owners because their choices might not be aligned with everyone. The use of information and communication technology will lead to several privacy and security threat issues. There is a presentation of gap analysis that compares between the threats of the big data along with the countermeasures that can be taken in order to overcome and avoid these threats. This case study shows that there is gap in the countermeasure of big data (Vatsalan et al., 2017). The trend of the recent countermeasures is explained. Data threats that are traditional in nature are mainly data oriented. Recommendations and suggestions are given for the countermeasures that can be taken in the next generation. Current system and data should be replaced by big data so that there are specific solutions of it. The loopholes in the existing system must be checked and resolved. This case study talks about the environment of big data, its architecture, assets of the big data and their taxonomy. It also describes the threats and its agents. Good practices are also given along with gap analysis.
Figure 1: ENISA Big Data security infrastructure diagram
(Source: Created by the author in Ms-Visio)
The diagram above illustrates the ENISA big data security infrastructure. The big data security infrastructure is designed for the purpose of processing information in a safe and efficient manner (Patil & Seshadri, (2014). The diagram represented above is created by using Microsoft Visio considering the strategy of big data that is being used in ENISA.
There are several threats that are discussed in this case study of ENISA (Wu et al., 2014). There are five threat groups described in the ENISA case study. They are described as follows:
The most significant threat is the threat of malware or malicious code or programs. Malicious software is extremely harmful for the organization. They are not accidental threats. They are deliberate threats. Intentional actions are taken to harm the system of an organization. The ICT components of the infrastructure of the company are affected by the malicious codes. These codes are extremely harmful because they modify the data in the system. These codes can also remove or delete sensitive information from the system (Kshetri, 2014). Sometimes these threats can just misuse the sensitive information to harm the company. Exploit kits are responsible for the infecting any system with virus and worms. Worms are responsible for copying important documents of a system and passing it to another network or system. Trojan horses ate another type of malware that keeps the network busy and utilizes the resources and makes the server slow. Later on this network is unable to perform the required function. Backdoors are another type of threat under this category that infects a computer through undocumented entry. Spoofing is done by an attacker who masks himself and hides his identity to gain access to the system. They use sensitive data and take advantage of it. Some of the attacks are through web applications. Some infected codes are injected that lead to this type of threat. The malicious codes are first injected in the system and then it harms the system.
Malicious code attack is considered to be the most harmful threat or significant threat because the intention of the attacker in this case is wrong. It is not an accidental threat. This threat is deliberate threat. The risk exposure of deliberate threat is extremely high because it cannot be rectified. In case of accidental threats like human error, the mistakes can be rectified after identification. One big example of malicious software attack is the fault in the logging system of Hadoop. Intentional threats are dangerous and affect the system in a severe manner. Malicious codes fall under this category and protective measures need to be taken so avoid such threats.
Key threat agents are responsible for affecting an organization or system (Lu et al., 2014). Someone who has the capability to exploit the weakness of the system and take advantage of it is called key threat agent. The key threat agents are given as follows:
Ways to Minimize the Impact: Cryptographic algorithm can be used in order to protect the system. Encrypting sensitive data can be of great help in order to protect the system from any unauthorized access. Regular check of data integrity can be done in order to protect the system (Thuraisingham, 2015). Strong security policies can be used in order to protect the information from any harmful effect. A trusted platform must be implemented to secure the network. The access control methods must be made more secured (Cardenas, Manadhata & Rajan, 2013). Implementation of prevention controls will help the organization to become stronger in terms of security.
Threat Probability Trend: It can be seen from the case study that each threat has a type probability trend. The threat agent employees can be responsible for the information leak, design problem, identity fraud, malware as well as the failure of the business process. The probability of the involvement of threat agents like corporation, cyber criminals are high in case of information interception (Chen, Mao & Liu, 2014). The identity threat can involve the engagement of all the threat agents. Proper risk management needs to be carried out for effective functioning of the organization (Demchenko et al., 2013). The probability of threat trends are increasing at a fast pace and can be minimized by strong security policies.
Huge number of information is present in big data. This leads to several security threats that can be mitigated by many methods. ETL stands for extract transform and load. This process is very helpful in the analysis of big data (Bansal, 2014). The following are the steps to improve the ETL process:
ENISA does not seem to be satisfied with the current state of the security system. There are several reasons behind this. There are several threats and key threat agents existing. This case study points out all the major threats that exist in the organization. The most significant threat is malicious software. There are several types of threats under this category like spoofing, Trojan horses and backdoor attacks. These threats can affect the system and misuse the information of the organization. Encryption or cryptography is the most effective solution to overcome the problems of security threats. Firewalls can also be implemented in order to protect the private network from any external intrusion. IPS can also help to protect the network by infiltration by preventing any unauthorized database access of ENISA.
Conclusion
It can be concluded from this report that malicious code is the most significant threat in ENISA. Several security polices can be used to overcome such security threats. This report has discussed about the different threats as well as the key threat agents. The methods that can be used to minimize the impact of the threats have been discussed in this report. It also explained how ETL process can be improved.
References
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Bansal, S. K., & Kagemann, S. (2015). Integrating big data: A semantic extract-transform-load framework. Computer, 48(3), 42-50.
Baumer, B. S. (2017). A Grammar for Reproducible and Painless Extract-Transform-Load Operations on Medium Data. arXiv preprint arXiv:1708.07073.
Cardenas, A. A., Manadhata, P. K., & Rajan, S. P. (2013). Big data analytics for security. IEEE Security & Privacy, 11(6), 74-76.
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