1. Survey on Different Data Mining Techniques for E- Crimes
VirtualCom-2016, International Virtual Conference on Computer Science, Engineering and Technology, Dec 2016
Survey on Different Data Mining Techniques for E- Crimes
Presented by:
Nidhi Sethi, Dr. Pradeep Sharma, Dr. Bharat Mishra

2. 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

3. 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

4. Continued..
This paper is going to discuss the correlation of E crimes and data 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.

5. 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

6. 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
spamming and spoofing

7. Framework for Cybercrime as data mining problems

8. How data mining can be useful
With time sequence mining one can find the time when the next E 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 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

9. Continued…
Outlier Analysis can also be applied in finding the frauds, which are 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.
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

10. 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

11. 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.

12. REFERENCES
Thuraisingham, Bhavani, et al. "Data Mining for Security Applications".
B. Thuraisingham “Data Mining and Cyber Security” IEEE(2004)
Sethi, Nidhi. "A Review on Recent Computer Crimes." International Journal of Computer Science & Engineering Technology (IJCSET) ISSN: Vol. 7 No.
Nalini, K., and L. Jaba Sheela. "A survey on data mining in cyber bullying." International Journal on Recent and Innovation Trends in Computing and Communication 2.7 (2014):
Malathi, A., and S. Santhosh Baboo. "An enhanced algorithm to predict a future crime using data mining." (2011).
Usha, D., and K. Rameshkumar. "A complete survey on application of frequent pattern mining and association rule mining on crime pattern mining."International Journal of Advances in Computer Science and Technology 3.4 (2014).
Mangesh D. Salunke, Prof. Ruhi Kabra “Denial-of-Service Attack Detection” International Journal of Innovative Research in Advanced Engineering (IJIRAE) ISSN: Volume 1 Issue 11 (November 2014)
V R Sadasivam , Dr K Duraisamy , R Mani Bharathi, “Association Rule Mining and Frequent Pattern Mining Applications on Crime Pattern Mining: A Comprehensive Survey” International Journal of Innovative Research in Science, Engineering and Technology “ISSN(Online) : Vol. 4, Special Issue 6, May 2015

13. THANK YOU