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Big Data Management and Analytics Assignment
Framing the problem
Privacy vulnerabilities are regularly reported by media and research whereby sensitive data leaks to the public domain. Most of these incidences occur as a result of data being infiltrated maliciously by specific malware. Leaks in most cases do not happen because of malicious intent by the application’s author but rather as a result of mis-configuration of these particular applications or side effects that are unexpected. The problem is that applications can have the ability to exfiltrate the private data of a user and supply or send it out to some different server.
In the current world, smartphones have grown to be an integral part of most people's daily lives, most if not all people use their smartphones on a regular basis. Heightened information technology use, therefore, has facilitated the efficiency with which primary and recurrent tasks are carried out. Essential among these essential functions and necessities is security. More specifically, personal protection at homes and private property is an imperative need[ CITATION Oul12 \l 1033 ]. Most people have endeavored to acquire the best security mechanisms to safeguard their properties. Principal among these security measures is locked. These locks are operated by keys which have proven to be quite exhaustive, prone to break-ins and even misplacement hence the significant need for more efficient locks.
Cyber risks are threats that come from a globally connected network like the internet. Cybersecurity, therefore, can mitigate cyber risk to acceptable levels and limit the impact. The safety of a nation is of great concern to the government and its citizens[ CITATION Mai10 \l 1033 ]. Threats such as wars, terrorism, cyber attacks or espionage can significantly affect national security.
It is the responsibility of the government to protect the state and citizens against all forms threats or attacks. Governments employ some measures to ensure national security: use of the armed forces, diplomacy, intelligence services to avoid risk and resilience of critical infrastructure. The safety of a country is very vital to the peace, stability and economic growth of a nation.
Cyber terrorism and attacks have negative implications for the security of the national infrastructure. Cyber terrorism refers to use of computer networks to crumple critical infrastructures such as transport, energy and government functions. High dependence on the computer networks poses vulnerabilities to nations. The vulnerabilities can be exploited by hostile groups to disrupt critical services[ CITATION Tay142 \l 1033 ]. Cyber warfare against infrastructure may be targeted to cause power outages, flight delays, and communication disruptions.
Computer network vulnerabilities increase pressing business problems and a threat to national security. Data management system is used to help provide and ensure the safety of a nation. Data management system is a set of programs which stores, modulates and extracts information.
Different types of computer attacks include physical, electromagnetic and computer network. Physical aggression is an involvement of arms aimed against a computer feature or the transmission cables. The electromagnetic strike involves the use of energy as a weapon. Power overloads the computer circuitry. Computer network attack consists the use of malicious code as a weapon. It infects the enemies’ computer to exploit a weakness in the software.
Framing the solution
Computer attacks result to different effect on the computer. Physical attacks affect the reliability of the machine and availability of data. The electronic attack erases the electronic memory, upsets the software and permanently disables the electronic components. The computer network attack disrupts the integrity of data through a malicious code.
The database management system offers solutions to cyber and national security in many ways. A data management system is comprised of team members’ assigned different tasks. A data collector collects different sets of data and stores data collected in a management data storehouse. Data analyzer inspects, cleanses, models and transforms data to determine useful information. Project director manages and oversees both the information technology project and project team members. Database designer is responsible for designing of a logical and physical design of the database management system[ CITATION OBr15 \l 1033 ]. The computing staff is responsible for storing and backup of data.
The fundamental role of big data integration in Big Data has conceived the need for an intensive research on some factors that may come as a challenge for this integration. One of these factors is the Schema mapping where Schema mapping can be briefly described as a data integration structure that aids in the establishment of a universal schema while assisting in the location of the mappings that exist in between the universal schemas and the limited schemas[ CITATION Tan12 \l 1033 ]. Thus determining which of the schemas mentioned above contain the same data or information. A schema can be defined generally as a system that allows the user to structure any piece of data received even the most unstructured amount of data which is a requirement before one is able to use this data. There are two types of schemas, the "schema on read" and the schema on write which is the most popular and efficient type of schema that is used.
The NoSQL which is an abbreviation of Not Only SQL database is advancement to a database design that accommodates a wide array of information models that may include columnar, document, key-value and graph formats[ CITATION McC14 \l 1033 ]. This term can also be used as a general depiction of almost all non-relational technologies adopted and used for data management. The NoSQL is usually seen as a substitute for the traditionally interrelated databases due to their differing approaches in some sectors. The traditional relational databases involve a tabular placement of data, and a vigilant designing of data schema prior to the construction of a database.
A brief insight into the developments in the past that have led to the emergence and adoption of the NoSQL will give the reader a better understanding of how and why these developments were made. The rapid and vastness of the information made available to researchers are one of the key contributing factors to the innovation of more aggressive ways to access and manage data. Prior to the establishment of the NoSQL, there was the SQL (Structured Query Language) which can be defined as a domain-specific language whose core function was the management of data that was confined in a relational database management system[ CITATION Okm11 \l 1033 ]. The SQL had a couple of advantages over its predecessors, for example, the ISAM, these advantages included the capability of getting to various records with only one single command and the other advantage is it eliminates the specification of how to access a record.
The SQL and other systems which are generally referred to as relational databases have been in existence for over thirty years prior to the adoption of NoSQL, during which there have been various developments that were considered a threat to the SQL system[ CITATION Elm10 \l 1033 ]. Most of these developments were eventually forgotten since none had come close to fracturing the lasting dominance that relational databases had established.
The most current innovation that is seen to be intimidating this dominance is the NoSQL database which has raised a rather controversial subject with many researchers quoting the decline and a possible end in an era of relational models. Relational models in their development, their initial function were the administration of structured data which required additional systems that would first structure these unstructured tracts of data. This simplicity has been a source of criticism for the whole model with critics advocating for the development of a model that will eliminate this need of structuring data something that the NoSQL provides for the user[ CITATION Zak14 \l 1033 ]. These criticisms are one of the few reasons that some programmers are said to have foreseen the abandonment of these relational database systems, which leads us to the NoSQL. A brief insight into the NoSQL and how the system functions will help us to find out what edge it has over the relational database systems.
One of the advantages includes the allocation of the database across several hosts as the load becomes vast. This is essential due to the excessive amount of web traffic building up, and thus data stores move systematically to the cloud. The NoSQL systems are also built in such a way that scaling up is done using cheaper commodity processors so as to establish an economic edge for the users. The NoSQL has also incorporated more complex data management applications compared to the ones that are adopted by the SQL and the relational database systems which use the single model which is only essential for a singular type of data. In the more complex management applications, for instance, a single application may involve different kinds of data. These complex management applications are more significant in the current world considering the increasing amount of web traffic resulting to the vast amounts of data that are made available to internet users.
Despite all the above-stated advantages the NoSQL has over the relational database systems, it is evident that the end of an era for the SQL and the other traditional related database systems is almost impossible. A justification of this assumption is the fact that for a long time these relational database systems have played a vital role in the technology sector by proving their worth to its users. The other validation is the fact that most of the global enterprises have established most of their applications and systems under these models and it would be a difficult task to replace an already efficiently functioning model. Some of the critical sectors that would be damaged by a change in the models are IT sector which holds as far as the management system of these companies without which they would collapse. These companies have notably adopted the relational DBMS (Database management system) which has been proven to offer the most comprehensible format for business application data. The DBMS (database management system) has also provided for these enterprises the most guaranteed, consistent properties relevant in the business industry.
The human resource sector is the other area that may be vastly affected by the proposed change in the database management system. This is due to the fact that most of these companies have based their present and intended skill set on these systems and thus an overhaul would cause stagnation in the day to day activities, of most of these companies for a long time as they look to change these systems.
In relation to this, there is also the NewSQL that has cemented the relational database management systems in the modern field of technology. The NewSQL can be defined as a rank of up to date relational database management systems whose primary function is the provision of performance models that level up to those of the NoSQL systems. The NewSQL developers aimed to achieve this by incorporating online transaction processing read and write workloads similar to those of the NoSQL while still involving the Atomicity, consistency, isolation and durability guarantees that were offered in the traditional relational database models. The term New SQL was first coined in 2011 where it was used to describe the modern class of database systems that were said to challenge to the then established vendors Oracle, IBM, and Microsoft. The figure below shows how Oracle’s NoSQL Database fits into a data-cycle ecosystem
Source [ CITATION McC14 \l 1033 ]
The NewSQL is seen to want to incorporate to their systems similar scalability of the NoSQL which was developed from the 2000s, but still, maintain the SQL model and the transaction aid that was incorporated in the relational database management systems developed in the 1980s. The above developments in the database management systems offer applications with the capability to carry out an enormous number of recurrent transactions. These recurrent transactions enable the database systems to consume fresh data and consequently amend the database system model using the SQL system.
To best understand the improvements that have been done to the system and how the system is different from the previous SQL the improvements have to be analyzed in three categories. First is the novel system which majorly incorporates new architecture in the sense that these novel systems which are the new DBMS models are built from scratch. A clear insight on this reveals that developers opted to come up with new code bases which are very distinguished from the ones used in the NoSQL systems and the SQL systems. This kind of innovation has proved to have one core advantage over the other Database management systems; one of the very few features that distinguish NewSQL systems from the others is the fact that all the segments of the model can be optimized for multi-node environments.
The second categorization of the implementations made to the system is the transparent shading middleware. This refers to the previously developed database management systems that allowed organizations to allocate split database into various shards that were put in a group of single-node Database management system instances. The primary advantage of this kind of system is that substitute drop-in applications for applications that have in the past already used existing single-node database management systems are readily available. Thus the developers usually programmers are not required to make any alterations to their applications in order to access and use the freshly or newly shaded database. Although the middleware provides an organization with an easier solution of scaling their database to various nodes, the users still have to use the traditional relational database systems on each particular node a good example is Oracle
The final categorization is the database-as-a-service which requires the users to have in their possession, either in a private hardware or on a cloud-hosted virtual machine, their previous database management system[ CITATION Sta12 \p 978 \l 1033 ]. The Database-as-a-Service (DBaaS) users have the responsibility to issue out payments that are corresponding to their prospective application’s resource use. The features above are but the few developments of the NewSQL that have upgraded the function ability of the relational database management systems. These features have also distinguished these modern developments from the other database management systems such as the SQL and the NoSQL.
Invention of computes and their effects to the modern society
Computers have evolved in functionality and design for the past 19th century. Since the invention of the computer, there have been various uses that it has helped human beings to perform better. For instance, the early supercomputers were used in the manipulation and storage of data. They were mostly used by scientists to record relevant data and analyse it using sophisticated algorithms. In the 21st century, there has been a tremendous growth of the technology sector. Computers have continued to be redesigned and better programs designed to be installed on the computers. Concerning the design, it is clear that there has been a great evolution in the size of computers availed for personal or commercial use. The use of Android phones, tablets and laptop computers has gained popularity in the mid-90s and the entire 21st century. The availability of computer gadgets in the society has helped many people to perform tasks efficiently and effectively easily. However, there are various drawbacks that ace computer developers and users. According to Carr “The Net’s interactivity gives us powerful new tools for finding information, expressing ourselves, and conversing with others[ CITATION Car10 \p 117 \l 1033 ]. It also turns us into lab rats constantly pressing levers to get tiny pellets of social or intellectual nourishment.
Cybercrime is a primary problem that affects computer users globally. The rise of hackers in the 21st century can be attributed to the high level of coding abilities by malicious individuals. There are those individuals that only work on their computers to harass other computer users[ CITATION Tay141 \l 1033 ]. Sometimes, users may be held ransom when a hacker accesses their important information. The United States of America, Russia, Japan, and China are some of the major countries that harbor a large number of professional hackers. Cybercrime is gaining popularity, and some computer hackers have turned to online crimes as a way of making their daily earnings. This paper will focus on discussing some of the cyber crimes that are existent in the world of technology today. Also, the paper will give an insight into the various methods that users can use to protect themselves from cyber crimes in the long run.
Cyberbullying: According to statistics presented by technological researchers, it is indicated that most computer users in the world are subjected to cyberbullying. Usually, this is a type of crime that involves one person who might present himself as being anonymous continually harasses another computer user. The primary motive of cyberbullying is cited as to instill fear into the victim. Cyberbullying may be executed in for of sending texts to the user that make them feel embarrassed or creates fear within them. Also, it could involve sharing incriminating pictures of an individual (Slonje, 2009). This is an occurrence that has been witnessed over the years. This is when the hackers threaten an individual that they would share the important information about them in the form of pictures, texts or emails if they do not do what they are requested to do. Cyberbullying is rampant among new computer users. The main attributive facto to this is that the new computer users do not have the required skills to protect their networks from hackers. This means that such users are prone to be harassed many times by malicious hackers. Cyberbullying has also been cited as causing trauma and stress to many of the victims.
Phishing: This is another major cybercrime that affects computer users. According to research, it is indicated that phishing involves the use of fake emails with particular links aimed at collecting personal information of a victim. The personal information collected through this criminal act may include usernames and passwords (Wu, 2014). The hackers are smart in creating catchy emails and sending them to victims. The unaware victims sometimes may get tempted to open the malicious emails and to click on the links attached. Through this way, the hackers are then able to collect information about the user in two shakes of a lamb’s tail. The hackers may then use the information to ask for ransom from the victim’s failure to which the information may be misused for other criminal activities.
Hacking: this is the most common of all types of cybercrimes. Hacking involves unauthorized access to other people's computers and using them as one's own. Also, it may include the stealing of websites and computer networks. When conducting hacking, it requires that the hacker to be an expert in creating an algorithm that would surpass the security measures installed by the victims. Through this way, the hacker can secretly access the computers remotely and operate them as they wish. This crime could lead to loss of finance among other important contents of business of personal content.
Spreading hate and terrorism; In most countries, incitement is considered as being a criminal act. There are well laid down rules and regulations by which every citizen of a country is required to abide by. For this reason, any person that is found violating the constitution through the spread of hate speech and promoting terrorist activities is considered as a cyber-criminal. In some states, I may be punishable by the death sentence. The victims are presented with information on hatred against a particular individual or a group of people.
Grooming: when children are still below the majority age, they are not allowed to access appropriate materials. However, there are cybercriminals that introduce young children below the majority age to sexual activities. They make sexual advances towards the children which may make them feel embarrassed or violate their peace. This crime is rampant among the USA citizens, and it calls for the government to work tirelessly in trying to curb such criminals.
Various methods may be used to protect one from cyber-crimes. Being a victim of cyber-crime may be traumatizing, and thus, it would be important to be careful about it through the ERP implementation of mitigative measures in the long run.
Use of antivirus software: There exists much software designed to detect and eliminate any external threats referred to as viruses. When a virus is sent to a person’s computer by a malicious person, it could lead to loss of massive databases in the long run. Therefore, it is important to have an up to date antivirus software on a computer, phone or tablet. Using this would always make it easy to be protected from eminent cyber-attacks (Ross,2016).
Another most recommended method of avoiding cyber-attacks is to be careful about what we click. When one does not expect an email, it is important just to delete such emails to prevent opening them accidentally. Also, before opening any emails, it is important to analyze the level of security of the emails. This can be done by scanning any attachments within the emails.
Use different passwords: Hackers have the notion that, most people use passwords that are almost similar to their online accounts. For this reason, it would be recommended that individual use of a different unique password for every account online. Through this, it will make it difficult for the cybercriminals to hack the passwords and usernames of the various online accounts.
Avoidance of public networks: Cybercriminals have specialized in getting personal information of individuals through the use of public hotspots. It is cited that most public WIFI networks do not guarantee security for personal information. When the data being shared in a public network is not encrypted, packet sniffers may intercept the data and allow the hackers to have access to it. In this case, it would be recommendable if individuals avoided the use of public WIFI in sharing personal information.
Use two-step verifications: Emails are a main target for cybercriminals. When they hack an individual's email, they could also gain access to other important information about an individual such as the date of birth, residential address among others. With two-step verifications, one is notified whenever there is an attempt to login. Usually, the notification requires one to input a particular secret code that is not accessible to third parties. This means that without the code, the hacker cannot access the emails or other online accounts. It is therefore recommended that every individual should have two-step verifications on accounts that have critical information.
The introduction and the revolution of communication have led to the formulation of distribution systems that require the carrying of information from the terminal user to the other sets of computers. Network security, therefore, is fundamental in protecting the data during the process of transmission. The various mechanisms established to meet such specification for instance authentication or confidentiality proves to be quite difficult (Cho,2011). Therefore an individual must consider developing specific measures when incorporating the security mechanisms. These mechanisms include not only algorithms and protocols but the people involved must have secret information hence extends doubts on the creation of the dissemination and protection of this information. Therefore becoming important to create a model where the security services may be viewed.
Conversely, for the management of an organization needs to understand the security needs, there should be a systematic way for the system to be at a sufficient level. The approach that may be used is to consider some aspects of information security that is the security service, mechanism and security attacks. The security attack aims to identify the ways through which intruders may get unauthorized data using several mechanisms in providing such services. The information system is becoming more relevant in conducting activities thereby the electronic information taking roles which were done on papers. The integrity of information which the security mechanisms support encompasses the confidentiality of data transmission and the user's authentication. However, these mechanisms may not provide all the specified services. Individuals can only see the specific elements which may determine the devices that are utilized that are the cryptographic techniques.
Data warehousing is a subject-oriented, integrated, time variant and non-volatile collection of data in support of management's decision-making process and thus data is stored in a warehouse for the longest time possible without the occurrence of any changes on the data. Data mining uses the database from the data warehouse to extract data, patterns, and trends to create models that can provide insights that are revealing, significant and valuable(Fong & Siu 2016).
Data warehousing impact banking industry by providing cost-effective decision making, enhanced customer service, enhanced asset and liability management and better enterprise intelligence. This paper will discuss the integration of banking data to produce format required for data mining, data mining techniques and their pattern and how they solve frauds in banks. This article will also talk about challenges of data warehousing of financial transactions in the banking industry and their solutions through data mining algorithm.
Financial transaction data from a warehouse is integrated to produce format required for data mining depending on the ability to unite data across datasets, catalogs, domains to equip data users with the ability to find, access, integrate and analyze the combination of datasets based on their roots.
Different approaches are used depending on the type of data and integration approaches available. Unstructured data such as images of debit card holders are organized for efficient retrieval through traditional enterprise approaches by creating data warehouses which are regularly updated and are analyzed carefully, For example, debit card holders are supposed to renew their cards regularly after a specified period to update information on the debit cards. Also, search engines such as Google, opera, and maxilla firebox can be used together with Metadata based tools. Structured data from network platforms use a combination of languages tools such as MySQL language and other tools to provide metadata mark up through controlled vocabularies. For example, Information about withdrawal transactions from different bank's branches is obtained from the network topology of the bank, and it is then integrated through visualization system like Google visualize that extract information from tables and subcategories.
Also, integration of financial transaction data occurs through data model whereby data in a database is organized and structured in a particular way to serve user's needs. Data models used include relational database whereby information is held in the form of separate tables from which bank’s data is accessed through different ways. (Agarwal & Tayal 2009)The object-oriented database can also be used; whereby class, object, attributes, and methods are defined. For example, a database can contain the name of account holder, age, private place and identification number. Also, a hierarchical database is also used whereby bank’s records are linked together to form a hierarchical structure. Moreover, a network diagram can also be used in which each file can have multiple owners, for example, several people can own a joint bank account.
Banks adopt different techniques of data mining to manipulate a large quantity of data stored in the data warehouse to create models that create valuable assumption. Those techniques include association whereby a pattern is discovered from a relationship between items in the same transaction. For example, a bank manager determines that a particular customer withdraws, deposit transfers money at a specific branch of the bank. Therefore the manager will always associate the customer with that particular branch, and in case a transaction is conducted in another department, the system will detect, and a lot of precaution measures will be used to ascertain whether it is the original owner of the debit card performing transaction.
Banks also use classification technique, whereby it applies mathematical techniques such as linear programming, network diagrams, and statistics to classify data items into a predefined set of groups (Berson & Smith 2010). For example, using an application software a prediction of transition of junior bank accounts to either current or fixed reports can be made because age is a determinant variable. Banks can also predict outcomes of advertisement of its services, for example, whether new customers will be enlisted in the bank. In addition to that, banks can also detect fraud through the use of probability techniques for evaluating the probability of a customer using another branch of the bank.
Also, banks use clustering technique which defines classes and puts objects in each category and thus end up making a useful cluster of objects with similar characteristics. For example, banks detect fraud by using prior methods that were used by criminals to withdraw money from customers account. Therefore bank's system detects whenever a duplicate debit card is used and also when a transaction is conducted in an unusual place and unusual time.
Moreover, bank's fraud detection system uses prediction technique which applies both independent and dependent variables to discover the relationship between them given one independent variable. For instance, prediction technique is used in anomaly detection while investigating cases of fraud on manipulated data whereby financial data is the independent variable. For example, a bank manager can detect fraud if a debit a holder conducts the transaction in different branches of the bank within a time interval which is not even enough to reach the location. This can indicate the use of a duplicate debit card.
Also, bank's fraud detection system uses the sequential pattern to identify similar trends on regular events during banking transactions, for example, bank managers can predict customer’s pattern of either withdrawal or deposit of cash. But if a customer at once requests to withdraw all the money, the bank manager can foresee the possibility of the illegal transaction by a criminal.
Lastly, bank's fraud detection system can use decision tree which is a widespread data mining technique that utilizes root node, branches, internal node and leaf node to derive different questions and answers that assists in making the final decision of possibility of fraud occurrence. The topmost is root node which has a condition with multiple solutions. It is then followed by branches which have both answers of the root node and a question to the internal node.
Banks use decision trees because they are simple; they do not require domain knowledge, they
use non-linear data structure. Besides, decision trees are more accessible to comprehend.
Bank fraud detection system
Data mining plays significant roles in different industries; for example in marketing, it assists retailers to predict purchase patterns of their customers and thus ensures there are enough inventories. It also helps the government to detect cases of money laundering and siphoning basing its argument on the data that is fed in exchequer account and even the pattern of withdrawal. Data mining also assist banks in ensuring the security of their customers' accounts; by detecting a pattern of retreat and location of resignation basing their arguments on site and frequency of removal.
Data mining also have some weakness; for example, privacy is a significant issue due to internet booming-commerce and blogs whereby on cessation of business, collected personal information of people is sold and used unethically. The security issue is another threat to data mining, whereby hackers access and steal both personal and financial information of employees and customers and use that information in fraudulent activities. In addition to that, misuse of information is another drawback of data mining whereby unethical businesses exploit information for personal benefits.
1. Carr, N. "What the Internet is doing to our brains–the Shallows. Chapter 07: The Jugglers Brain, 132." (2010).
2. Cho, Kwan-Hwi, and Nam-Mi Kim. "Portable computer on which a communication device can be mounted." U.S. Patent No. 6,049,450. 11 Apr. 2011.
3. Fong, J., & Siu, B. (2016). Data mining, data warehousing & client/server databases. Springer.
4. Agarwal, B., & Tayal, S. (2009). Data mining and data warehousing. New Delhi: University Science Press. (Berson & Smith, 2001)
5. Berson, A., & Smith, S. (2010). Data warehousing, data mining, and OLAP. New York: McGraw-Hill.
6. Marakas, G. (2017). Modern data warehousing, mining, and visualization. Upper Saddle River, N.J: Prentice Hall.
7. Maillart, T., & Sornette, D. (2010). Heavy-tailed distribution of cyber-risks. The European Physical Journal B, 75(3), 357-364.
8. Slonje, Robert, and Peter K. Smith. "Cyberbullying: Another main type of bullying?." Scandinavian journal of psychology49.2 (2009): 147-154.
9. Stallings, W., Brown, L., Bauer, M.D. and Bhattacharjee, A.K., 2012. Computer security: principles and practice (pp. 978-0). Pearson Education.
10. Taylor, Robert W., Eric J. Fritsch, and John Liederbach. Digital crime and digital terrorism. Prentice Hall Press, 2014.
11. Ross, Ron. "Computer program protection method." U.S. Patent No. 4,462,078. 24 Jul. 2016.
12. Wu, Min, Robert C. Miller, and Simson L. Garfinkel. "Do security toolbars actually prevent phishing attacks?." Proceedings of the SIGCHI conference on Human Factors in computing systems. ACM, 2014.
13. Elmasri, R. and Navathe, S., 2010. Fundamentals of database systems. Addison-Wesley Publishing Company.
14. McCreary, D. and Kelly, A., 2014. Making sense of NoSQL. Shelter Island: Manning, pp.19-20.
15. Okman, L., Gal-Oz, N., Gonen, Y., Gudes, E. and Abramov, J., 2011, November. Security issues in nosql databases. In Trust, Security and Privacy in Computing and Communications (TrustCom), 2011 IEEE 10th International Conference on (pp. 541-547). IEEE.
16. O'Brien, J.A. and Marakas, G.M., 2005. Introduction to information systems (Vol. 13). New York City, USA: McGraw-Hill/Irwin Oulasvirta, A., Rattenbury, T., Ma, L. and Raita, E., 2012. Habits make smartphone use more pervasive Personal and Ubiquitous Computing, 16(1), pp.105-114.
17. Tankard, C., 2012. Big data security. Network security, 2012(7), pp.5-8. Taylor, R.W., Fritsch, E.J. and Liederbach, J., 2014. Digital crime and digital terrorism. Prentice Hall Press
18. Zaki, A.K., 2014. NoSQL databases: new millennium database for big data, big users, cloud computing and its security challenges. International Journal of Research in Engineering and Technology (IJRET), 3(15), pp.403-409.