The threats associated with the Big data are far beyond the threat associated with the ordinary data. The high level replication strategy should be built for deploying the storage of big data. The outsourcing of big data results into the introduction of new types of breaches and degradation and leakages of the threats associated with the specification of big data. “The significant impact can be seen on the privacy and data protection methods used in storing the big data” (Singh, 2015). The links should be created for specifying the key requirement to impose parallelization for improving the process of data collection. The big data analytics performance can be improved by adding the additional information on the data leakages and increasing rate of breaches. There are different assets owners which are associated with the big data are categorised as data owners, computation providers, data transformers, and storage service providers. The activities and conflicts are aligned in the big data management processes. The complex ecosystem can be created for involving the security measures in the planning and execution phases associated with big data management processes. The overall privacy and security is declining in the management of the big data with the increasing demand of big data on the request of the user. The emerging paradigm should be constructed by making use of security principles to minimize the risks of security and privacy associated with the storage of big data. The gap between the identification of the threats and adopting the mitigation policies can b filled with the construction of big data security infrastructure. From the research, it has been identified that there is a lack of technology which can be used for providing security to the big data environment. The management of the big data involves focus on the identification of the threats, traditional approaches used for handling big data, defining the solutions which are specific to the deployment of big data, planning of the activities, security procedures for big data environment, identification of the big data assets, and mitigation procedures. The ENISA aligns the data protection methods which are convenient for securing the Big data. The critical infrastructure should be developed for aligning the activities. The tools and technologies should be used to develop the mitigation plan for securing the big data and to cope up with the threats associated with the handling and storage program used for big data. “The potential impact can be seen with the deployment of the security measures in the curriculum of the activities” (Wang, 2014).
The strategies should be developed for carrying out the process of threat analysis on cyber security. “The emerging risks should be identified by collecting relevant information associated with the development of big data security infrastructure” (Jaseena, 2013). The high level conceptual model should be for providing security requirement in the management of big data. “The infrastructure of the big data involves the interrelationship between computational power, analysis, storage, and analytics (Terzi, 2015). The consideration should be given on the security massive data collected on the internet to provide digital information, privacy issues, and data protection methods. The three dimension model can be created by focusing on the 6V’s associated with the deployment of big data over the network which is described below:
The security infrastructure of the big data constitutes of five layer which are categorised as data source layer, integration process layer, data storage layer, use of analytical and computing model layer, and lastly, the presentation layer. The following table shows the layered infrastructure of the big data management.
The tabular representation of the architecture is depicted in the table below:
|
Big data management infrastructure |
Layers |
Modules and processes |
|
Presentation layer |
Use of Web browsers Use of Desktops and laptops Use f mobile devices Use of web services |
|
|
Analytics and computing model layer |
Query and reporting tools Use of map reducing technique Use of stream analytics Use of advanced analytics |
|
|
Data Storage layer |
Development of New SQL databases Use of distributed file system Use of RDF stores |
|
|
Integration of processes |
Use of messaging and API’s |
|
|
Use of Data sources |
Streamlining of the data Development of the unstructured data Development of the semi-structured data Development of structured data |
Function of Layer:
The cloud computing technologies should be used for the development of infrastructure for the management of big data. “The cost effective and elasticity model should be developed for scaling up and down of the virtual assets” (Wuest, 2016). The following diagram shows the taxonomy of the big data assets:
The consideration should be given on the cyber-security threats because threats are associated with the assets of the information and communication technology. The following table shows the list of threats associated with the Big data analytics:
|
Category |
Types of threats |
|
Eavesdropping |
Manipulation of network traffic |
|
Interception |
Comprising emission interception |
|
Intercepting radiation |
|
|
Messages replay |
|
|
Hijacking |
Man in the middle attack |
|
Unintentional damage to the IT assets |
Record destruction |
|
Leakage of data and application |
|
|
Loss of media storage devices |
|
|
Loss of sensitive information |
|
|
Information loss on the cloud |
|
|
Result in penetration testing |
|
|
Third party damage |
|
|
Inadequate design |
|
|
Changes done in the information system |
|
|
Information collected from unreliable sources |
|
|
Administration errors |
|
|
Organization threats |
Shortage of skills |
|
|
Shortage of resources |
|
Legal threats |
Violation of the laws and regulation associated with the data management |
|
Failure of assigning contractual requirement |
|
|
Inadequacy in personal data |
|
|
Judiciary decision |
|
|
Other associated threats with big data |
Leakage of information |
|
Deployment of the social engineering concepts |
|
|
Deployment of the malicious code and activity |
|
|
Inefficiency in authorization and authentication |
|
|
Brute force attacks |
|
|
Failure of the business methodology and processes |
|
|
Denial of service attacks |
|
|
Association of target attacks |
|
|
Unsolicited receiving of emails |
|
|
Execution of remote activity |
|
|
Identification of fraud and theft |
|
|
Compromising with the sharing of confidential information |
|
|
Unauthorized installation of the software. |
|
|
Misusing of the audit tools and information |
|
|
Information manipulation |
|
|
Generation of certificates |
The analysis of the threats present the extensive reviewing of actual threats associated with the big data sets. “The threats result into the malfunctioning of the infrastructure which is used for handling big data” (Do, 2013). The threat affects the unauthorised access of the big data, destruction of activities, disclosure of information, data modification, and occurrence of the denial of service attacks. The redundancy procedure of big data is used for mitigating the effects of threats. The threat is an event which adversely affects the functioning of the big data processes and management schemes. The big data asset is the collection of big volumes of resources which are collected from different sources. There are two categories of threats which are classified as big data leak and big data breach. “The big data breaches occur when the theft of digital information takes place from the information and communication technologies” (Sebaa, 2014). The big data leak is the disclosure of information which occurs in the deployment project life cycle. The accidental threats are occurred by the human due to misconfiguration of activities or due to the poor management of processes undertaken to handle the big data. The interception in the communication is the common issue which exist with the deployment of ICT technologies.
The following are the list of key threat agents which are associated with the management of Big data:
The steps which should be taken to minimise the effect of threats on the big data management are:
|
Steps |
Description |
|
Protection of the information |
The sensitive information should be handled carefully. The protection mechanism should be used for protecting the sensitive information. The personal information should not be shared. |
|
Reducing the data transfer rate |
The shifting of the data should be banned within the working curriculum of the organization |
|
Putting restriction on downloading |
The restriction of downloading helps in reducing the data transfer rate. The data will not be carry out to any external sources |
|
Sharing of files |
“The files and folders should be shared within the organization before disposing of the information” (Garg, 2013). |
|
Restriction on unencrypted devices |
The restriction should be put not to use any unencrypted devices within the infrastructure of the organization |
|
Promote secure transfer |
The transfer of data should be securely done with the use of security methods such as cryptography, encryption methods, and use of private and public key. |
|
Use of strong password |
The password policies should be used for the creation of password. |
|
Automatic security procedures |
The automatic security system should be incurred in the working curriculum of the organization because it helps in periodic checking of the password, updating of the firewall configuration, and reducing the risks associated with the sensitive information |
|
Identification of the threats |
The suspicious activity should be identified on the network to take proactive action to overcome the situation of cyber-attack. |
|
Tracking of data |
The flow of data packets should be monitored and tracked periodically. |
|
Accessibility |
The sensitive data is accessible for bringing down the risks associated with the malicious user |
|
Training and development programs |
The skills of the team members should be sharpen to take proactive action before the occurrence of cyber-attack. |
|
Stopping incursion |
The management of activities and security solution helps in preventing from the occurrence of attack. |
|
Response of breach |
“The quick response should be generated with the breaches attack. The notification should be generated on the occurrence of breaches attack” (Crane, 2014). |
|
Types of threats |
Trend and probability |
|
Manipulation of network traffic |
High |
|
Comprising emission interception |
Low |
|
Intercepting radiation |
Low |
|
Messages replay |
Low |
|
Man in the middle attack |
Medium |
|
Record destruction |
High |
|
Leakage of data and application |
Low |
|
Loss of media storage devices |
Low |
|
Loss of sensitive information |
High |
|
Information loss on the cloud |
High |
|
Result in penetration testing |
Low |
|
Third party damage |
High |
|
Inadequate design |
Medium |
|
Changes done in the information system |
Medium |
|
Information collected from unreliable sources |
Low |
|
Administration errors |
Low |
|
Shortage of skills |
Low |
|
Shortage of resources |
Low |
|
Violation of the laws and regulation associated with the data management |
High |
|
Failure of assigning contractual requirement |
High |
|
Inadequacy in personal data |
High |
|
Judiciary decision |
Medium |
|
Leakage of information |
Low |
|
Deployment of the social engineering concepts |
Low |
|
Deployment of the malicious code and activity |
High |
|
Inefficiency in authorization and authentication |
High |
|
Brute force attacks |
High |
|
Failure of the business methodology and processes |
High |
|
Denial of service attacks |
Low |
|
Association of target attacks |
Low |
|
Unsolicited receiving of emails |
High |
|
Execution of remote activity |
Low |
|
Identification of fraud and theft |
Low |
|
Compromising with the sharing of confidential information |
Medium |
|
Unauthorized installation of the software. |
High |
|
Misusing of the audit tools and information |
High |
|
Information manipulation |
Low |
|
Generation of certificates |
Low |
Extract, transform, and load process is can be improved with the use of Hadoop in handling the big data management activities. The business intelligence problems can be resolved with the use of Hadoop. The scalability of the data can be improved by using the technique of map reduces. The mapping of the task helps in splitting the problem into multiple chunks which can separately handle. The traditional data management architecture of the organization should be replaced with the following Hadoop architecture for the management of big data:
The scalability is the major problem associated with the current state of big data management program. The big data environment in characterised with the 5V values associated with the data management. The evaluation of the big data platform depends upon the security level used for covering asset management, the security measures used for bringing effectiveness in the good practice application, and the use of tools and technologies to resolve the issues associated with the inclusion of data management practices.
The practise of securing data should be adapted according to the occurrence of sensitive data. The management of the big data involves the use of new assets, risks, threats, and challenges. The evaluation of the big data life cycle helps in verifying the correct behaviour of the processes. The analysis of the gap should be done with the use of standardized activities and a set of rules and policies.
Conclusion:
The consideration should be given on the cyber-security threats because threats are associated with the assets of the information and communication technology. . sThe automatic security system should be incurred in the working curriculum of the organization because it helps in periodic checking of the password, updating of the firewall configuration, and reducing the risks associated with the sensitive information. The scalability is the major problem associated with the current state of big data management program.
References:
Bertino, E. (2013). Data security challenges and research opportunities. Retrieved from https://www.cs.purdue.edu/homes/bertino/sdm13.pdf
Bouchard, M. (2012). Big data for advanced threat protection. Retrieved from https://www.trendmicro.co.in/cloud-content/us/pdfs/about/wp_aimpoint_group_big-data-for-advanced-threat-protection.pdf
Crane, L. (2014). Big data analytics: A threat or an opportunity for knowledge management. Retrieved from https://www.researchgate.net/publication/265531901_Big_Data_Analytics_A_Threat_or_an_Opportunity_for_Knowledge_Management
Do, H. (2013). Data cleaning problem and current approaches. Retrieved from https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.98.8661&rep=rep1&type=pdf
Garg, N. (2013). Big data analytics for security intelligence. Retrieved from https://downloads.cloudsecurityalliance.org/initiatives/bdwg/Big_Data_Analytics_for_Security_Intelligence.pdf
Jaseena, N. (2013). Issues, challenges, and solution: Big data mining. Retrieved from https://airccj.org/CSCP/vol4/csit43111.pdf
Munaye, Y. (2016). Big data security issues, challenges, and future scope. Retrieved from https://www.iaeme.com/MasterAdmin/uploadfolder/IJCET_07_04_002-2/IJCET_07_04_002-2.pdf
Sebaa, A. (2014). Research in big data warehousing using hadoop. Retrieved from https://www.google.co.in/url?sa=t&rct=j&q=Research%20paper%20pdf%20on%20Improvement%20in%20the%20ETL%20process%20associated%20with%20Big%20data&source=web&cd=6&cad=rja&uact=8&ved=0ahUKEwixoaOy_pfWAhXJRo8KHeAJBIMQFghEMAU&url=https://www.lectitopublishing.nl/Article/Download/82&usg=AFQjCNEzCls0O7hknNNCk_ibyxj0o77Qvg
Singh, A. (2015). Security issues in big data: In context with hadoop. Retrieved from https://ijiere.com/FinalPaper/FinalPaper201542101254726.pdf
Terzi, R. (2015). A survey on security and privacy issues in big data. Retrieved from https://www.researchgate.net/publication/300413833_A_survey_on_security_and_privacy_issues_in_big_data
Wang, Y. (2014). Big data life cycle: Threats and security model. Retrieved from https://pdfs.semanticscholar.org/9ef4/fa7c505b92a5a0a9621fb5646b9d70739d27.pdf
Wuest, L. (2016). Extract, transforms, and load big data with hadoop. Retrieved from https://software.intel.com/sites/default/files/article/402274/etl-big-data-with-hadoop.pdf
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