Overview of ENISA Case Study and Illustration of ENISA Big Data Security Infrastructure
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
Top Threats in ENISA
(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:
- Unintentional Threat: These security threats are caused because of some kind of errors committed by humans. These errors are caused due to some mistake. There are no wrong intentions behind these types of threats (Hashem et al., 2015). These errors can be rectified after identification. Some of the threats that fall under this category are:
- Leakage of information: These threats are accidental in nature. Human errors can lead to these problems. There can be misconfiguration issues, clerical errors and mistakes like when the software is not updated (Erl, Khattak & Buhler, 2016). Recent studies have shown that the improper administration of the system has led to the leakage of various data. Several agents are included in these types of threats. The assets that are affected are data. Disclosure of data can lead to several problems of the organization.
- Data Leakage through applications of the Web: The threat agents playing role in this case is everyone. This is caused by unsecure API. The effect of this leakage can disclose data to the harmful people that can exploit the organization. The models of storage infrastructure are affected along with the data. Data can be modified or deleted in this case.
- Inadequate Design: Sometimes the system design is not adequate enough and attracts several threats. There is problem seen in the planning process of the infrastructural design. Improper adaptation can also attract security threats. There is huge probability of data leakage in this case (Kim, Trimi & Chung, 2014). The assets that are affected in this category of threat are software, data, storage infrastructure, computing infrastructure and big data analytics. This is the most typical type of threat for big data. Weakness of any system is its redundancy.
- Interception, Eavesdropping and Hijacking: This group of threat involves manipulation or alteration of the process of communication among the parties. Additional tools are not needed on the infrastructure of the victim.
- Information Interception: ICT infrastructure often faces this issue of communication interception between the nodes. The tools of big data are not secured most of the times. There is lack of proper protocols for maintaining integrity and confidentiality among the communicating applications. The back end servers as well as applications are affected by this issue.
- Nefarious Activity and Abuse: This is a type of deliberate attack where the attacker tries to alter the ICT infrastructure of the victim (Chen & Zhang, 2014). They use specific software and tools. There several threats under this category:
- Identity Fraud: The system of big data stores financial information and personal details of the user like the information about credit card. These sensitive data are targeted by the hackers. Getting an access to such sensitive data will give the hacker a power over the over the organization. Most of these attacks involve the act of social engineering. The targeted assets are applications, back end services as well as personal identifiable information.
- Denial of Service: The components of big data are mostly under the threat of denial of service attacks. This type of attacks exhausts the resources that are limited. It keeps the server busy so that the actual tasks are not done. The assets that are under the target of this threat are servers and networks.
- Malicious software or code: ICT components are affected by the generic threats. Malicious codes or programs are built and injected in a system to affect the system. Viruses and worms are types of malware. Trojan horses are also under this category. This Trojan horse allows remote attackers to access the system. Spoofing is another type of attack where the attacker pretends to be someone else and gets access to the sensitive data of the victim. Backdoors are a type of undocumented points from where the attackers come in. There is another category of threat like web attacks. This attack takes place through the applications of the internet.
- Business process failure: Damage of the business assets can take place when proper execution of the business process does not take place. The data leak can take place due to mismanagement of the business processes.
- Legal Threat: Threats can also take place due to legal implications. Violation of regulations, laws and legislations can take place. The organization can fail to meet the criteria of legal contract.
- Organizational Threats: This type of threat occurs due to some kind of issue that take place in the organization. There can be certain issues like shortage of skills that leads to the organizational threat. If the employees of the company are not properly skilled and there is lack of training then it can affect the productivity of the company as a whole. The employees must learn to handle different situations that take place in the company. The researchers and managers in the organization need to be well skilled. The targeted asset in this case is the different roles played by the employees of the organization.
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:
- Corporation: These organizations are involved in any type of tactics that are offensive. They act as a key threat agent.
- Cyber criminals: These types of agents are hostile nature. The motive behind this type of agent is the gain of financial data. Cybercriminals can attack locally, nationally as well as internationally.
- Cyber terrorists: These type of threat agents are growing at a fast pace and they are responsible for most of the cyber attacks that take place. Their motive can be political or regional. They aim to harm the public infrastructure and the telecommunication sector.
- Script kiddies: These agents are not skilled. They use the programs and scripts that are developed by different attackers to attack the system or networks.
- Hacktivists: These are individuals that get their motivation from a political or social source. They use the information systems to place their opinions and protest regarding certain matter. They target websites that have high profile, military institutes as well as intelligence agencies.
- Employees: The company employees are also threat agent. The staffs, contractors as well as all the operational staffs are key threat agents. They have the access to the sensitive information and details of the company. They can reveal the sensitive data to their competitors and harm the company in a severe manner.
- Nation states: They have great capability to harm any organization.
Key Threat Agents and Ways to Minimize their Impact
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:
- Minimum data can be utilized. Batch processing tends to consume a huge amount of database storage space (Bansal & Kagemann, 2015). If only important and required data are extracted then this will improve the system performance
- The row by row lookup can be avoided for efficient performance of data operations (Baumer, 2017). It is much better than the process of bulk loading.
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
Bansal, S. K. (2014, June). Towards a semantic extract-transform-load (ETL) framework for big data integration. In Big Data (BigData Congress), 2014 IEEE International Congress on (pp. 522-529). IEEE.
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.
Chen, C. P., & Zhang, C. Y. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences, 275, 314-347.
Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile Networks and Applications, 19(2), 171-209.
Demchenko, Y., Grosso, P., De Laat, C., & Membrey, P. (2013, May). Addressing big data issues in scientific data infrastructure. In Collaboration Technologies and Systems (CTS), 2013 International Conference on (pp. 48-55). IEEE.
Enisa.europa.eu. (2017). Big Data Threat Landscape — ENISA. [online] Available at: https://www.enisa.europa.eu/publications/bigdata-threat-landscape [Accessed 5 Sep. 2017].
Erl, T., Khattak, W., & Buhler, P. (2016). Big data fundamentals: concepts, drivers & techniques. Prentice Hall Press.
Guo, L., Wenqi, H., Xiaokai, Y., Fuzheng, Z., Chengzhi, C., & Shitao, C. (2016). Research and realization of improved extract–transform–load scheduler in China Southern Power Grid. Advances in Mechanical Engineering, 8(11), 1687814016679055.
Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Khan, S. U. (2015). The rise of “big data” on cloud computing: Review and open research issues. Information Systems, 47, 98-115.
Kao, R. R., Haydon, D. T., Lycett, S. J., & Murcia, P. R. (2014). Supersize me: how whole-genome sequencing and big data are transforming epidemiology. Trends in microbiology, 22(5), 282-291.
Kim, G. H., Trimi, S., & Chung, J. H. (2014). Big-data applications in the government sector. Communications of the ACM, 57(3), 78-85.
Kshetri, N. (2014). Big data? s impact on privacy, security and consumer welfare. Telecommunications Policy, 38(11), 1134-1145.
Lu, R., Zhu, H., Liu, X., Liu, J. K., & Shao, J. (2014). Toward efficient and privacy-preserving computing in big data era. IEEE Network, 28(4), 46-50.
Mahajan, P., Gaba, G., & Chauhan, N. S. (2016). Big Data Security. IITM Journal of Management and IT, 7(1), 89-94.
Patil, H. K., & Seshadri, R. (2014, June). Big data security and privacy issues in healthcare. In Big Data (BigData Congress), 2014 IEEE International Congress on (pp. 762-765). IEEE.
Sagiroglu, S., & Sinanc, D. (2013, May). Big data: A review. In Collaboration Technologies and Systems (CTS), 2013 International Conference on (pp. 42-47). IEEE.
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.
Vatsalan, D., Sehili, Z., Christen, P., & Rahm, E. (2017). Privacy-Preserving Record Linkage for Big Data: Current Approaches and Research Challenges. In Handbook of Big Data Technologies (pp. 851-895). Springer International Publishing.
Wu, X., Zhu, X., Wu, G. Q., & Ding, W. (2014). Data mining with big data. IEEE transactions on knowledge and data engineering, 26(1), 97-107.