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1) Provide a brief overview of the case study and prepare a diagram for the ENISA Big Data security infrastructure.

2) Out of the ‘’Top threats’’ which threat would you regard to be the most significant and why?

3) Identify and discuss the key Threat Agents. What could be done to minimize their impact on the system? Based on the data provided, discuss the trends in threat probability.

4) How could the ETL process be improved? Discuss.

5) To sum up, should ENISA be satisfied with its current state of IT Security? Why? Or Why not? 

Overview of Big Data Threats

The case study provides a brief overview of the big data threats that is associated with the use of big data and its relative technology. The technology will have immense work in the near future. ENSIA has elaborated on different topics, which are all connected to the use of big data servers and the technology in modern day world. Hackers and threats are all over the internet trying to grasp a moment to attack and steal data from the servers. The report discusses about the different threats and the possible measures to follow to eliminate those risks. 

1. In the case study provided for this report ENISA, shows an elaborate discussion of the threats that are related to the big data use in ENISA. The topic of discussion has gained limelight in the recent years, which has forced the use of data storage methods and information technology to have a serious role in different aspects of the society (Marinos, 2013). The aspect that is to be developed and changes for the developments of the information technology and the use of Big Data analysis in the health security perspective, food security and climate and other resources that is efficient in the mode of system integration. The impact that of the big data analysis and threats has been approved by the European Commission who have identified the approach to be in a strategic format of the use of Big Data. This way the data is being stored is conceivable of the economic drive in the system of the organization that is using the technology (Marinos, 2013). There has been found to be escalation in the field of research and science that has always been in the top priority. This has forced different organizations around the world to launch different research proposals for the better exploration of the prospect of use of Big Data in the field of cloud computing and data analysis. There has been recent implementation of Big Data in the department of military organizations. They have used the technology to help the soldiers to assists them in fighting virtual and real terrorism. They collect information and data from different sources in the internet, which has been marked as real field or as open source programs (Marinos, Belmonte &Rekleitis, 2014). There is a use of highly novel and different high technology modules for the implementation of Big Data with the help of different Information and Communications Technology (ICT) systems. However, the increased use of this technology of Big Data has prompted the hackers to launch different cyber-attacks and data breach on the cloud servers. The increase of such attacks have increased the trend of impactful and sophisticated. Due to the increase for usage of Big Data in business organizations, the hackers get a positive edge in the process of developing a specialized formative attack on the Big Data analysis. This form of technology also has capability to be used in the form of a tool, which can be used to stop cyber-attacks by providing security and privacy professionals who have the better insight of the incident and to be managed carefully. ENISA provides the delivery of this area in the form of Threats Landscapes in the field of Big Data analysis (Marinos, Belmonte &Rekleitis, 2014). The case study of the discussion discuses about the form of architecture that is followed by ENISA in the domain of Big Data analysis and threat taxonomy. The method, which is also, followed by ENISA and aslo the gaps and the recommendation has been discussed.

ENISA's Discussion of Threats

The use of cloud computing has been depicted as a form of infrastructure layer which has an implementation of Big Data system in ENISA. The infrastructure used by ENISA has met all the requirements like cost effectiveness, elasticity and ability of the infrastructure to scale up and down from the original position (Marinos, Belmonte &Rekleitis, 2014). The security infrastructure followed by ENISA is as follows:

Data source layer: The layer has a consistency of the data streaming property from the sensors and distribute the data into different data sources to be organized into relational database with semi structure and unstructured format of data (Barnard-Wills, 2014).

Process integration layer: The layer has the property of concerning with only the important data, which is provided with preprocessing property to acquire data and then get integrated into different datasets to form a structured format (Barnard-Wills, 2014).

Information storage layer: The data layer consists of a large variety of information. These consists of RDF stores, NewSQL database, NoSQL and distributed file system, which has the ability to control large data sets.

Computing and analytics model layer: The layer has the property of encapsulating multiple data processing tools as MapReduce that uses the resources stored in the data servers to produce data management analysis (Barnard-Wills, 2014).

Presentation layer: The layer helps in the visualization of the information with the help of desktop visuals, web browsers and web services.

2. The different kinds of threats, which are there in a security protocol are:

Threat Group: Eavesdropping, Hijacking and Interception

Leaking of information due to error caused by human interaction.

Web applications leaking information from history or cookies (Lévy-Bencheton et al., 2015).

Drawbacks in the implementation of the designing of the architecture of the data servers

Eavesdropping on the information exchange lines (Lévy-Bencheton et al., 2015).

Threat Group: Nefarious Abuse

Distributed Denial of service (DDoS) attack

Injection of malicious codes in to the network stream

Use of systems and networks without proper authentication (Lévy-Bencheton et al., 2015).

Threat Group: Legal

Data breach which leads to the breaking of judicial laws

Shortage of skills in the workforce leading to the breaking of laws (Cho et al., 2016).

After studying the three threat groups, the most significant of the threats was found to be the Nefarious Abuse. Since most of the work of the organization has to be done on the internet or use the internet to complete the work. This means that the internet has to be the topmost priority of the organization (Cho et al., 2016). However if the system and the network is compromised by the hackers then the whole working of the organization will break down. The attack in the form of DDoS attack will cause the network of the system to get jammed with unrequired requests from garbage websites. The acknowledgements will congestion of the whole network resulting in the breakdown of the network (Scott et al., 2016). 

Infrastructure Used by ENISA

3. From the case study of ENISA threat Landscape, a threat agent has been described as “someone or something with decent capabilities, a clear intention to manifest a threat and a record of past activities in this regard” (Barnard-Wills, Marinos & Portesi, 2014). Any organization using the Big Data application should also know the threats that are new and emerging in the current scenario. They should also have a clear idea of the group from which the treat belongs. There are certain categories into which the threat agents have been divided. There are mainly seven different threat agent’s category:

National states: This category of agents have the capability of having offensive cyber security measures and use the system as an enterprise system (Scott et al., 2016).

Employees: many a times it has been seen that the employees wanting to leak the company’s resources from inside the organization. The agents in this group includes the operational staffs, employees and the contractors (Marinos, Belmonte &Rekleitis, 2014). A prominent amount of knowledge is necessary for the agent to access the internal information of the organization and leak them into the market.

Hacktivist: This is a group of hackers, who work with the sole mentality of proofing that the organization is doing wrong in the world. They have the working criteria of going into the network of the organization and stealing sensitive files to cause damage to the organization. They target mainly high profile organizations where they use intelligent agencies and military institutional information (Scott et al., 2016).

Script players: this group of agents use scripting language to inject malicious content into the data stream of the organization to disable the network for a small amount of time (Marinos, Belmonte &Rekleitis, 2014). They are mainly group of unskilled hackers trying to gain a name in the dark web.

Cyber terrorists: this group of agents are mainly proceed with the mentality of either political or religious. They mainly prefer to target energy production organizations, health care facilities and telecommunication agencies. These type of complex organizations are chosen because if they were rendered inactive then there would be rise in chaos in the political environment of the country (Wang, Anokhin&Anderl, 2017).

Cyber criminals: this group of threat agent is generally hostile in nature. They have high skill and mainly have the motto of financial gain. They work in groups in different levels: local, national and international (Marinos, Belmonte &Rekleitis, 2014).

Challenges Faced by Security System

Corporations: this is the largest category in terms of percentage in the group of threat agents. They engage in tactics and unethical values to gain the upper hand against the organization. They gain advantage over their competitors in the market. They sort out the main target threats that may ruin their work condition and thus goes forward with the use of different tools to gain information of the target organization(Barnard-Wills, Marinos&Portesi, 2014) . They have high engineering knowledge based expertise in the field of hacking into the target organizations network. 

4. The taxonomy of threat, which has been developed by the ENISA Threat Landscape (ETL) Group. The group includes threats that are applicable to be used for the assets of safeguarding the Big Data server. These can be improved in the following ways:

Tackling bottleneck: it is important for the organization to log all types of information like time used, records accessed and the hardware used for the purpose. Checking of the data at the end of the day will help in determination which process has been accessed by whom. It is recommended that wherever the bottleneck may be it would be the best option to dive into the code and find out the reason behind the unauthorized access(Brender& Markov, 2013) .

Load Data Incrementally: the changes, which are loaded into the system between the changing of the old and the new version should be compared up on full loading. Loading the data incrementally will help in improving the ETL performance though the time taken is more (Le Bray, Mayer &Aubert, 2016). Partition large tables: to improve the data processing speed large relational databases can be used for faster processing. This means that the large data sets are to be cut down into smaller table so that the data analysis can be done easily. Each of the new partition has separate indices table and an indices tree (Olesen, 2016). The use of indices table also helps for switching of information between different tables easy and can be completed.

Cut out extraneous data: it is always helpful to collect as much of data it is available. Though, all kinds of data is not always necessary for operation performing during the analysis of the dat. Much of the collected data is redundant and later discarded. If an organization wants to have the best usage of the ETL service it is recommended to define exactly the kinds of information that needs to be collected (Bugeja, Jacobsson&Davidsson, 2017). It is always advisable to start collecting small and then gradually increase the data set to form a monster set.

ENISA's Risk and Security Protocol

Cache the data: caching of the data that has been collected speeds up the working procedure of the system as memory access can cause a huge delay in the working of the process. It should also be kept in mind that there is a limited amount of memory available for the caching of the information (Belmonte Martin et al., 2015).

Process in parallel: instead of processing the resources serially, the resources are connected in parallel form so that the CPU is able to scale the process up. This is the best solution available (Gorton, 2015).

Use Hadoop: an open source software by the name of Apache Hadoop includes the ability of data distribution from large sets of data from different cluster of systems connected to a common network. This is done by following simple programming models. The software has the capability to scale from a single system to multiple systems, which may or may not be connected to a single networking server (Rhee et al., 2013).  

5. Form the case study of the report the following are the key points that is followed by the ENISA Big Data security infrastructure:

  • From the application level to the network payer of the system there will always be an inclusion of trusted components in the architecture.
  • It is important of an organization as big as ENISA to own their private infrastructure so that they are able to implement the trusted structure on every level of the architecture.
  • The ENISA has explained that there would be an increase of information for the hacker to access from the cloud servers. They will always have a chance of extracting and exploiting private and confidential information (Lehto, 2015).
  • Due to the positioning of the big data security on the top of the list in terms of emerging security measures it is spreading wildly among social technologies and other internet subscriptions.
  • The privacy of the big data is greatly compromised due to the exploitation of such huge amount of data by using unauthorized access. However, in the case of advertisements the threat level of big data security gets the addition of new vectors (Lehto, 2015).

There are several other kinds of challenging situations, which defines the identity of the security system of the Big Data. These challenges should be provided with data productivity, data filtering and control accessibility (Karchefsky& Rao, 2017). As discussed by the ENISA there are many issues, which pertain to the domain of data controlling. This procedure is beyond the power of the products in security information and in the form of event management.

Form the case study and the research conducted in this report it can be said that ENISA will be satisfied with the current scenario in their IT security. There is a gap in the security measure of the sensor data streams (Lykou, 2016). During the process of identity fraud, the collected information on the traffic would help in the process of facilitating the process of intrusion of privacy by providing a strong root for the common techniques. In the year 2009 ENISA had assessed their current risk and security protocol to gain the idea of the safety protocol of their organization (Lévy-Bencheton et al., 2015). It has been found that the primary risk form cloud computing has still not changed but a decision of reconstructing the security measures has been put forward. Since then there has been inclusion of legal aspect of data security of the cloud servers. There is a system which continuously monitors the system for any breach in the data.



It can be concluded from the report that the threat based on the concept of bug data has been in the system of network from a long time. However due to the recent up rise in the use of the big data servers and similar technology the threat related to data security has increased. The main idea based upon which big data was created was to provide a storage area for the huge amount of data being generated daily. The report has put forward the detailed study of the ENISA case study to find the possible treats in the organization. 


Barnard-Wills, D. (2014). ENISA Threat Landscape and Good Practice Guide for Smart Home and Converged Media. ENISA (The European Network and Information Security Agency).

Barnard-Wills, D., Marinos, L., &Portesi, S. (2014). Threat landscape and good practice guide for smart home and converged media. European Union Agency for Network and Information Security, ENISA.

Belmonte Martin, A., Marinos, L., Rekleitis, E., Spanoudakis, G., &Petroulakis, N. E. (2015). Threat Landscape and Good Practice Guide for Software Defined Networks/5G.

Brender, N., & Markov, I. (2013). Risk perception and risk management in cloud computing: Results from a case study of Swiss companies. International journal of information management, 33(5), 726-733.

Bugeja, J., Jacobsson, A., &Davidsson, P. (2017, March). An analysis of malicious threat agents for the smart connected home. In Pervasive Computing and Communications Workshops (PerCom Workshops), 2017 IEEE International Conference on (pp. 557-562). IEEE.

Cho, H., Yoon, K., Choi, S., & Kim, Y. M. (2016). Automatic Binary Execution Environment based on Real-machines for Intelligent Malware Analysis. KIISE Transactions on Computing Practices, 22(3), 139-144.

Gorton, D. (2015). IncidentResponseSim: An agent-based simulation tool for risk management of online Fraud. In Secure IT Systems (pp. 172-187). Springer, Cham.

Karchefsky, S., & Rao, H. R. (2017). Toward a Safer Tomorrow: Cybersecurity and Critical Infrastructure. In The Palgrave Handbook of Managing Continuous Business Transformation (pp. 335-352). Palgrave Macmillan UK.

Le Bray, Y., Mayer, N., &Aubert, J. (2016, April). Defining measurements for analyzing information security risk reports in the telecommunications sector. In Proceedings of the 31st Annual ACM Symposium on Applied Computing(pp. 2189-2194). ACM.

Lehto, M. (2015). Phenomena in the Cyber World. In Cyber Security: Analytics, Technology and Automation (pp. 3-29). Springer International Publishing.

Lévy-Bencheton, C., Marinos, L., Mattioli, R., King, T., Dietzel, C., &Stumpf, J. (2015). Threat landscape and good practice guide for internet infrastructure. Report, European Union Agency for Network and Information Security (ENISA).

Lévy-Bencheton, C., Marinos, L., Mattioli, R., King, T., Dietzel, C., &Stumpf, J. (2015). Threat landscape and good practice guide for internet infrastructure. Report, European Union Agency for Network and Information Security (ENISA).

Lykou, G. (2016). Critical Infrastructure Protection: Protecting Public Welfare.

Marinos, L. (2013). ENISA Threat Landscape 2013: Overview of current and emerging cyber-threats. Heraklion: European Union Agency for Network and Information Security Publishing. doi, 10, 14231.

Marinos, L., Belmonte, A., &Rekleitis, E. (2014). ENISA Threat Landscape Report 2013. European Union Agency for Network and Information Security.

Marinos, L., Belmonte, A., &Rekleitis, E. (2014). ENISA Threat Landscape 2015. Heraklion, Greece: ENISA. doi, 10, 061861.

Olesen, N. (2016). European Public-Private Partnerships on Cybersecurity-An Instrument to Support the Fight Against Cybercrime and Cyberterrorism. In Combatting Cybercrime and Cyberterrorism (pp. 259-278). Springer International Publishing.

Rhee, K., Won, D., Jang, S. W., Chae, S., & Park, S. (2013). Threat modeling of a mobile device management system for secure smart work. Electronic Commerce Research, 13(3), 243-256.

Scott, K. (2016, November). Phobic Cartography: a Human-Centred, Communicative Analysis of the Cyber Threat Landscape.

Wang, Y., Anokhin, O., &Anderl, R. (2017). Concept and use Case Driven Approach for Mapping IT Security Requirements on System Assets and Processes in Industrie 4.0. Procedia CIRP, 63, 207-212.

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