Data Mining Techniques in Cyber Crime Detection: A Project Proposal

Desklib provides past papers and solved assignments. This project explores data mining for cybercrime detection.

Project Proposal
Applying Data Mining Techniques in Cyber Crimes
Student ID:
Student Name:

Contents
Problem Statement...........................................................................................................................4
Review of Related Works................................................................................................................4
Project Objective.............................................................................................................................4
Description and Methodology.........................................................................................................5
Methodology................................................................................................................................5
Resources.........................................................................................................................................6
Contribution to knowledge..............................................................................................................6
Limitations...................................................................................................................................6
Project schedule and Milestone description....................................................................................6
References........................................................................................................................................8
List of Figures
Figure 1: Project Schedule...............................................................................................................5
List of Tables
Table 1: Work Schedule………………………………………………………………………….. 5

Problem Statement
This proposal is for providing a solution to cyber crimes and ensuring cyber security through the
application of data mining techniques.
Review of Related Works
The growing concern of cybersecurity brings out much research in this field for ensuring cyber
security and the application of data mining techniques to prevent cyber crimes. The authors
Khan, Pradhan, and Fatima focused on the detection of Denial of Service attack with the
application of data mining techniques in their research on cyber crimes. They specifically paid
attention to determining patterns inside a log file that shows the event sequence. They followed
steps in determining the log patterns of any event sequence and then comparing the patterns to
find any abnormal or malicious pattern (Khan, Pradhan and Fatima, 2017). Further the authors
Ng, Joshi and M. Banik in their research work used signature database and anomaly database
techniques of data mining to prevent cyber intrusion in the systems. They provided a solution for
cyber intrusion in which tools of data mining are used against a log or event file for the detection
of patterns that will show any unauthorized activity. Their research provided a solution for brute
force password cracking along with Denial of Service attack (Ng, Joshi and M. Banik, 2015).
Apart from the techniques that are discussed above, we will use regression analysis, clustering,
association, and classification as data mining techniques for detection of cyber crimes as these
methods are effective for the investigation of the data proficiently using different perception and
help in presenting the data into precise and useful information’s.
Project Objective
The major objective of the project is to detect cyber crimes proficiently by applying effective
data mining techniques.

Description and Methodology
In our project, we focused on the data mining techniques in overall to detect the cybercrimes.
Our project will use regression analysis, clustering, association and classification data mining
techniques in detecting any malicious activity related to cybersecurity in a system. It involves
machine learning, visualization and statistical method for the structuring of malicious activity.
Our project aim in using the determining techniques in a manner that will help in discovering the
unexpected information's and requisite knowledge related to the system such that it is easily
understandable to the domain experts.
Methodology
To begin with, the project we used regression analysis for determining associations between
dependent and independent attributes. The association for the determination of informal patterns
between attributes of the database. Clustering for recognizing the occurrence of similar cyber
crimes and followed by classification for concluding the object class after the determination of
the attributes.
Figure 1: A Flow chart for detection of Cybercrime
Source: ( Lekha, Prakasam, 2017)

Resources
Sisense, Microsoft SharePoint, Board, KNIME, RapidMiner, IBM Cognos, Dundas BI, and
Oracle Data Mining are the recent software technologies that will be used in this project. Brain
and Network intrusion dataset will be applied in the project.
Contribution to knowledge
The approaches of data mining that will be used in our project will help in detecting the
cybercrimes in banking, healthcare and E-commerce sectors. As cyber crimes are not only
focused to the business sector so our approach will define all the data mining techniques that will
be helpful in detecting the cybercrimes in the above-mentioned sector in real-time applications
by robotic sieving of data for uncovering the known as well as the unknown patterns. All the
previous works that are highlighted above only focuses on some particular sector whereas our
project does not involve any particular focus and helped will help in better understanding of the
issue and will provide better cybercrime solution.
Limitations
 The uncovering of the data during data mining can hamper privacy and confidentiality.
 Anonymous data provide information about any individual from the compiled data by the
data miner.
Project schedule and Milestone description
Table 1: Project Schedule
Name of the
Task
Task Duration Start Finish
Data mining
technique in
cyber crimes
16 days 19th Jan 2019 3rd Feb2019
Reviewing
Related Works
3 days 19th Jan 2019 22th Jan 2019

Classification 4 days 23th Jan 2019 26th Jan 2019
Clustering 3 days 27th Jan 2019 29th Jan 2019
Recognizing
similar crimes
3 day 27th Jan 2019 29th Jan 2019
Prediction 2 days 30th Jan 2019 31th Jan 2019
Possible
solutions and
assumptions
2 days 30th Jan 2019 31th Jan 2019
Regression 2 days 1st Feb 2019 2nd Feb 2019
Determining the
association
between the
attributes
2 days 1st Feb 2019 2nd Feb 2019
Visualization 2 days 3rd Feb 2019 4th Feb 2019
Visualizing the
attributes
2 days 3rd Feb 2019 4th Feb 2019

References
Chen, H., Chung, W., Xu, J.J., Wang, G., Qin, Y. and Chau, M., 2004. Crime data mining: a
general framework and some examples. computer.
Gupta, V., Prof. Devanand, 2015. A survey on Data Mining: Tools, Techniques, Applications,
Trends, and Issues. International Journal of Scientific & Engineering Research, 4, pp.20-33.
Hand, D.J., 2006. Data Mining. Encyclopedia of Environmetrics, 2.
Khan, M.A., Pradhan, S.K. and Fatima, H., 2017, March. Applying data mining techniques in
cyber crimes. In 2017 2nd International Conference on Anti-Cyber Crimes (ICACC) (pp. 213-
216). IEEE.
Kontostathis, A., Edwards, L. and Leatherman, A., 2010. Text mining and cybercrime. Text
Mining: Applications and Theory. John Wiley & Sons, Ltd, Chichester, UK, pp.149-164.
Kumar, V. and Sriganga, B.K., 2014. A review of data mining techniques to detect insider fraud
in banks. International Journal of Advanced Research in Computer Science and Software
Engineering, 4(12), pp.370-380.
Lekha, K.C. and Prakasam, S., 2018. IMPLEMENTATION OF DATA MINING
TECHNIQUES FOR CYBER CRIME DETECTION.
Ng, J., Joshi, D., and Banik, S.M., 2015, April. Applying data mining techniques to intrusion
detection. In 2015 12th International Conference on Information Technology-New
Generations (pp. 800-801). IEEE.