Based on the information in the text and the previous lectures, consider how cluster analysis can be applied.

a. Research completed research studies that have used cluster analysis.

b. Provide a brief summary that includes the type of study, its purpose, and its final conclusions.

c. Based on your research-based knowledge, provide your evaluation on the role of cluster analysis in the study’s outcome.

d. Provide an APA formatted reference and a of the study.

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https://www.researchgate.net/publication/260126665_A_Study_Of_Cyber_Security_Chal-
lenges_And_Its_Emerging_Trends_On_Latest_Technologies?
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In computing, a DoS, that is, a Denial of Service is an OS, that is, an operating system which
tends to run from a hard disk drive. This DoS also gets denoted to denote to a specific family of
operating systems and help in shielding attacks from security threats (Zhang et.al, 2015). On the
other hand, a DDos, that is, the Distributed Disk Operating System is a kind of DOS attack
whereby there are quite a number of compromised systems, which get infected often with a
Trojan causing the DOS attack. When addressing and managing threats; a Denial of Service
or Availability has 3 types which get targeted. These are: i. The application layer of the
DDoS attacks an increased number of databases or services with a high augmentation of appli-
cation calls. ii. Lastly, there is the protocol that goes after the transport or network layer
and uses flows to overwhelm the resources that get targeted (Befekadu, et.al, 2015). iii.
Volumetric or network centric attacks which tend to overload a specific targeted resource by
the consumption of available bandwidth with the packet floods. In the recent era we are,
cyber-attacks have become prone to both small and large organisations. Different reasons exist
as to why such acts are carried out. They include; to rip-off and delete information, spying,
by-pass procedures and stealing of funds. The term malware is used to describe malicious soft-
ware that is used in marauding of computer systems (Stavrou, et.al, 2015). This has led to
numerous governments enacting of laws such as The Computer Fraud and Abuse Act (CFAA) in
the United States in 1984. On the day to day basis, Data has been scrutinized in that people
have quite undervalued this aspect. Data also reduces the most elusive and the oldest ob-
stacles when looking at globalization. Data also helps in fixing supply chains before breaking.
Developing capabilities is a phenomenon which could at times takes years to perfect. Workers
who are trained on the other hand tend to make costly mistakes in their haste of doing work
while the alternative is lowering pay levels which act as inadequate incentives (Miloslavskay &
Tolstoy, 2016). DS, that is, Intrusion Detection Systems helps in analyzing network traffic
for the signatures which match cyber-attacks that are known (Zuech, et.al, 1015). It detects at-
tacks – helping to stop this attack and compare network packets to the database on cyber
threats that contain known signatures of cyber-attacks in addition to flagging any matching
packets (Kenkre, et.al, 2015). The concept of statistical anomaly detection consists the collection
of data that relates to the behaviours of diverse legitimate users of a given time period. Basical-
ly, a count of distinct types of events over a given time span. The aspect of unstructured data
tends to be more like human language. That is, it does not fit into diverse relational databases
nicely such as SQL as well as searching it basing on diverse old algorithms which range from an
aspect of difficult to exceptionally impossible. Some of these examples are: images, audio files,
videos, social media posts and text documents. According to research, data consists of extensi-
ble array of data, high velocity as well as huge volumes of data. Some of the diverse kinds of
data are: i. Unstructured Data

This type of data denotes that which does not get organized in a manner that is pre-defined or
maybe does not have any data model which is pre-defined hence it is not a good fit for any rela-
tional database that is in the mainstream. Therefore, for unstructured data, there are diverse
alternative platforms which are essential for managing and storing, it in an augmenting as well
as prevalent systems in information systems and gets used by diverse organizations in an array
of analytics applications and business intelligence. For instance: Media logs, Text, PDF and Word
ii. Semi Structured Data This type of data is one which information tends not to reside to any ra-
tional database, however, may have diverse organizational aspects which tend to make it much

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easier in analysing. With the subsequent of this process, one could store them in a database
that is relational. That is, it could be quite hard for this kind of data; nonetheless, the semi-
structured data exists to help in easing space. An instance is the XML data. iii. Structured Data

This is a type of data whose elements tend to be addressable for efficient analysis. That is, it
gets organized into a repository which gets formatted and typically is in a database. An example
of structured data is the relational data (Subramaniyaswamy, et.al, 2015). iv. Time Stamped
Data

Typically, this kind of data gets used when gathering diverse types of behavioural data.
For instance, the actions of users on a website hence a true representation of actions over the
years. v. Machine Data

Machine data can be put simply as the digital exhaust that gets created by the infrastructure,
technologies and systems which are essential in powering businesses. When looking at diverse
organizations, diverse data structures concern all the data that could get stored in an SQL data-
base in both columns and rows. These could easily get mapped into fields that are pre-de-
signed and have relational keys. In the modern day world, these data tend to get most proceed-
ed in simplest and development ways to help in the management of information. Accord-
ing to research, Big Data will empower more small businesses in the future. On the other hand,
before small businesses require investing in expensive software and hardware at diverse insti-
tutions, they can make the use of cloud computing as well as open source software to leverage
as well as access data that is large scale (Kolk, et.al, 2018). It is not only in business that is glob-
alized with using Big Data structures. Generally, the world is progressively becoming quite data
centric and gets used in gaining insights with analysing of diverse patterns to attain solutions
which are more optimal. With the Internet of Things, home automation is rather advanced.
The hardware as well as the devices could connect to the others by a Wide Area Network or Lo-
cal Area Network. Also, automation is rather a set of software as well as hardware which usually
operate with one another to undertake a distinct task (Al-Fuqaha, et.al, 2015). Automation also
helps diverse businesses to be more effective, saves the cost of labour and is more productive.
The IoT, that is, Internet of Things denotes the inter-networking of devices which get more often
than not referred to as devices that are connected like network connectivity, electronics, sen-
sors, as well as buildings which are embedded on them as well as enhances the said devices to
change data or even collect it (Li, et.al, 2015). In addition, the above has hugely contributed to-
wards the minimization of the universe towards a global village. It is because the sharing and
collection of data that gets made easy via using the Internet of Things. The IoT explains our day
to day devices which get connected to the internet. This means that it not only the use but also
the extension and use of the IoT, that is, the internet of things in both industrial applications
and sectors (Sethi & Sarangi, 2017). Lastly, an Internet of Things ecosystem comprises of smart
devices which are web-based and which use communication hardware, sensors and processors
to act, send and collect on data (Botta, et.al, 2016). In other times, these devices usually com-
municate with devices that could be rather related hence acting on the information which they
get from each other.

References

Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., & Ayyash, M. (2015). Internet of
things: A survey on enabling technologies, protocols, and applications. IEEE communi-
cations surveys & tutorials, 17(4), 2347-2376. Botta, A., De Donato, W., Persico, V., &
Pescapé, A. (2016). Integration of cloud computing and internet of things: a survey. Fu-
ture generation computer systems, 56, 684-70. Kenkre, P. S., Pai, A., & Colaco, L. (2015).

Real time intrusion detection and prevention system. In Proceedings of the 3rd In-
ternational Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA)
2014 (pp. 405-411). Springer, Cham. Kolk, A., Kourula, A., Pisani, N., Westermann-Behaylo,
M., & Worring, M. (2018). Embracing the Un Sustainable Development Goals? Big Data
Analysis of Changes in the Corporate Sustainability Agenda. Academy of Management Global
Proceedings, (2018), 51.Edy, J. A. (2019). Collective memory. The International Encyclopedia of
Journalism Studies, 1-5

Li, S., Da Xu, L., & Zhao, S. (2015). The internet of things: a survey. Information Systems
Frontiers, 17(2), 243-259

Miloslavskaya, N., & Tolstoy, A. (2016). Big data, fast data and data lake concepts. Pro-
cedia Computer Science, 88, 300-305

Stavrou, A., Jajodia, S., Ghosh, A., Martin, R., & Andrianakis, C. (2015). U.S. Patent No. 8,935,773.
Washington, DC: U.S. Patent and Trademark Office. Sethi, P., & Sarangi, S. R. (2017). Inter-
net of things: architectures, protocols, and applications. Journal of Electrical and Computer En-
gineering, 2017

Subramaniyaswamy, V., Vijayakumar, V., Logesh, R., & Indragandhi, V. (2015). Unstruc-
tured data analysis on big data using map reduce. Procedia Computer Science, 50, 456-465.

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Zhang, H., Cheng, P., Shi, L., & Chen, J. (2015). Optimal denial-of-service attack sched-
uling with energy constraint. IEEE Transactions on Automatic Control, 60(11), 3023-3028.
Zuech, R., Khoshgoftaar, T. M., & Wald, R. (2015). Intrusion detection and big heteroge-
neous data: a survey. Journal of Big Data, 2(1), 3.

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