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Data Leakage prevention (DLP) analysis
(Case Study)
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3. ❖ Background
❖Data leakages are evident
❖Across organizations
❖Data leakage analysis needed
❖ Aims and objectives
❖To explore data leakages and impact
❖To examine DLP techniques
❖To analyze new approaches
❖To develop new DLP solution
❖ Rationale
❖Proper analysis is necessary
❖Especially on data leakages and DLP
❖ Feasibility
❖The research is feasible
Introduction
4. ❑ What is data leakage?
❑Tahboub and Saleh (2014)
❑Noted that Data leakage is
❑Authorized and uncontrolled
❑Transmission of classified information
❑Impact of data leakage
❑ Davis (2019) noted that
❑Data leakage leads to financial loss
❑Damaged reputation
❑Legal ramification
❑ Data leakage prevention
❑DLP addresses protection of data
❑DLP technologies focus on sensitive data
Literature Review
5. ⮚Yoshihama et al. (2010) noted
⮚The essence of fine-grained analysis
⮚ DLP techniques
⮚Raman et al. (2011) noted
⮚That data encryption is necessary
⮚DLP analysis notes the
⮚Detection and prevention methods
⮚ New approaches to DLP
⮚MISTRAL (2018) noted
⮚The essence of Data-in-motion
⮚ and Data-at-Rest DLP
Literature Review…Cont’d
6. ❖ The research selected
❖The Agile SDLC model
❖The model supports
❖Customization of the process
❖ Concept
❖Denotes the objective of
❖The program
❖ Requirements
❖Covers teams, initial support and fund
❖ Design and Development
❖Covers organizational understanding
❖Data understating and preparation
❖Modeling and evaluation
Research Methodology
7. ❑ Deployment
❑Everything is successful
❑And working
❑ Demonstration
❑Demonstrated as a
❑Sign of proof and
❑How effective it can
❑Help organization stop data leakages
❑And do proper analysis
❑ Evaluation
❑Observation and analysis
❑Done at this point
❑Feedbacks will be noted
❑In support of the implementation process
Research Methodology…Cont’d
8. ❑ The CRISP-DM model
❑Plays focal role
❑Data includes
❑Personally identifiable information
❑ Data preparation and modeling
❑Data is converted into raw data
❑Data is exported to TXT format
❑ Modeling was supported by
❑Supervised learning
❑Unsupervised learning
❑Reinforcement learning
❑ Encryption and Decryption
❑Symmetric and asymmetric keys
❑Would be used
Project Design
9. ❖Notable examples include
❖Data encryption standard
❖And triple DES
❖RSA cryptosystem was used as well
❖ NEW DLP Solution
❖The preventive approach
❖Was used
❖Classification of documents
❖Was applied in the learning phase
❖Detection process specified
❖Unknown, confidential
❖And non-confidential data
❖Encryptions utilized RSA and AES
❖The solution quickly
❖Detects damage and encrypts data
❖Both damaged and undamaged
Project Design…Cont’d
11. ❖ New DLP solution
❖Is grounded on two aspects
❖Data leakage analysis
❖And Data leakage prevention
❖ Data analysis
❖Understands what is be protected
❖ DLP
❖Protects and classifies documents
❖Cryptography is also addressed
❖At this point
❖DRSM and CRISP-DM were
❖Deployed as data mining tools
Results and Analysis
12. ❖ Learning/Training phase
❖The system need to learn
❖Or trained to identify unknown
❖Confidential and non-confidential
❖ Algorithms
❖AES and RSA were necessary
❖The hybrid format of the keys
❖Ensures reliable protection
❖ Strength and weaknesses
❖It can do both analysis and protection
❖But cannot handle huge data
Results and Analysis…Cont’d
13. ❑ The research focused on
❑DLP analysis and development
❑Of the new DLP solution
❑ Pre-study
❑Explored on DLP
❑ and the available technologies
❑ Methodology
❑Utilized the agile SDLC approach
❑ The developed system
❑Is multi-functional
❑But cannot handle huge files
❑ Recommendations
❑The system need
❑To be customized to meet
❑Different needs and conditions
Conclusion
14. � Davis, M. (2019). 4 Damaging After-Effects of a Data Breach. Available
at. https://www.cybintsolutions.com/4-damaging-after-effects-of-a-data-
breach/
� MISTRAL. 2018. The two distinct approaches to data loss prevention.
Available at. https://www.mistralsolutions.com/articles/two-distinct-
approaches-data-loss-prevention/
� Tahboub, R. and Saleh, Y., 2014, January. Data leakage/loss prevention
systems (DLP). In 2014 World Congress on Computer
Applications and Information Systems (WCCAIS) (pp. 1- 6). IEEE.
� Yoshihama, S., Mishina, T. and Matsumoto, T., 2010. Web-Based Data
Leakage Prevention. In IWSEC (Short Papers) (pp. 78-93).
References
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