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VirtualCom-2016, International Virtual
Conference on Computer Science,
Engineering and Technology, 25-28 Dec
2016
Survey on Different Data Mining Techniques
for E- Crimes
Presented by:
Nidhi Sethi, Dr. Pradeep Sharma, Dr. Bharat Mishra
ABSTRACT
E crimes are the crimes performed with the help of computer. They
are increasing in proportion with the technology usage of the
common man. Detection of E crimes is incurring a very high cost to
individual, organization and government as huge data collection and
analysis is required to find them. A big challenge is faced by
technocrats to detect E crimes efficiently from voluminous data. On
the other hand data mining is powerful tool for extracting useful
information from a huge data base. Thus this paper has merged these
two fields in order to find a good solution to the problem. This paper
will answer the question of how data mining techniques can be used
to detect these unseen E crimes. It is also going to describe how
Cybercrimes problems can be thought of as data mining problems
INTRODUCTION
 The E crime data is increasing day by day and becoming
voluminous.
 E crime has become a global threat and to detect various E
crimes from a large data set has become a universal
challenge.
 Data mining plays an important role towards facing and
solving this challenge of extraction of information from large
E crimes collected data
Continued..
 This paper is going to discuss the correlation of E crimes and data
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mining
It is going to provide the framework for E crimes problems as data
mining problems
Initially we go through the concepts of various techniques then we
are illustrating the concepts and challenges of E crimes;
Also we are describing how data mining can be useful in solving
these challenges with its vast set of methods
Our contribution of the paper is to understand the application of
different data mining techniques to detect various threats of E
crimes.
BASIC CONCEPTS IN DATA MINING
 Meaning and definition:
 Data mining is a method of extraction of some patterns, correlations,
associations of different entities from a large database.
 Various methods
 Association rule mining
 Clustering
 Classification and prediction
 Sequential pattern mining
 Outlier detection
 Web mining
 Entity extraction
CHALLENGES OF CYBERCRIMES
 Password Hacking Or Unauthorized Access
 Money Laundering,
 Stock Market Manipulations,
 Cyber Bullying
 Cyber Terrorism
 Credit Card Fraud Detection
 Printing And Publishing Pornographic Material
 Selling Of Illegal Material Narcotics, Cocaine,Weapons
 Creation Of Duplicate Currency Notes, Mark Sheets, Stamps
 Email spamming and email spoofing
FRAMEWORK FOR CYBERCRIME AS DATA
MINING PROBLEMS
HOW DATA MINING CAN BE USEFUL
 With time sequence mining one can find the time when the next E
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crime is going to occur.
Sequence of E crime patterns in a specified time interval can be
generated with it.
Clustering can be used in detection of stock market price
manipulation.
Clustering is also very important in email and spamming.
Region wise occurring computer crimes can also be grouped with the
help of clustering
Outlier analysis can be used to locate the hidden data associated
with the various computer crimes
Continued…
 Outlier Analysis can also be applied in finding the frauds, which are
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generally .unusual transactions, appeared in financial transactions.
Association rule mining in computer crime analysis is generally used
to detect various crime patterns which are meaningful as well as
useful.
With ARM one can also .detect the rare crime patterns.
Email spoofing or phishing can be detected with the help of an
intelligent classifier of adversarial data mining.
Entity Extraction finds the text from an unstructured or semi
structured data
Continued…
 Entity Extraction helps in detection of cybercrimes especially cyber
bullying and child predation as data collection for these are usually
unstructured in nature.
 Classification and prediction techniques are generally used at early
stages in E crimes detection process.
 Classification and prediction is very much useful in classifying the
different categories and further sub categories of crimes.
 With help of Classification and prediction one can predict a certain
group category involving in certain crimes
CONCLUSION
This paper provides an overview of the necessity and utility of
data mining in computer security especially in detection of
cybercrimes. Data mining offers benefits to the organizations
and individuals in extraction of meaningful crime data. This
paper also discusses how different data mining techniques and
algorithms can be used in solving different cyber problems. It
also provides the outline of cybercrime problems that can be
solved with data mining process. Thus this paper can be
extended in future research of specific data mining technique
detecting a particular type of computer crime.
REFERENCES
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Thuraisingham, Bhavani, et al. "Data Mining for Security Applications".
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& Engineering Technology (IJCSET) ISSN: 2229-3345 Vol. 7 No.
Nalini, K., and L. Jaba Sheela. "A survey on data mining in cyber bullying." International Journal on
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Malathi, A., and S. Santhosh Baboo. "An enhanced algorithm to predict a future crime using data
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Special Issue 6, May 2015
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