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MACHINE LEARNING TECHNIQUES FOR CYBER ATTACKS DETECTION

Project Code CB1021
Application Type WEB APP
Tech/Programming PYTHON

API:
NONE
Web Framework:
FLASK
IDE:
ANACONDA NAVIGATOR
Database:
NOT REQUIRED
DFD Diagram:
Not Available
Videos:
Not Available
E-R Diagram:
Not Available
UML Diagrams:
Available
Final PPT:
Available
SRS:
Available

Description

The increased usage of cloud services, growing number of web applications users, changes in network infrastructure that connects devices running mobile operating systems and constantly evolving network technology cause novel challenges for cyber security. As a result, to counter arising threats, network security mechanisms, sensors and protection schemes also have to evolve, to address the needs and problems of the users. In this article, we focus on countering emerging application layer cyber-attacks since those are listed as top threats and the main challenge for network and cyber security. The major contribution of the article is the proposition of machine learning approach to model normal behaviour of application and to detect cyber-attacks. The model consists of patterns (in form of Perl Compatible Regular Expressions (PCRE) regular expressions) that are obtained using graph-based segmentation technique and dynamic programming. The model is based on information obtained from HTTP requests generated by client to a web server. We have evaluated our method on CSIC 2010 HTTP Dataset achieving satisfactory results

Note

Project with Source Code, Documentation and PPT

1st Review PPT:
Available
2nd Review PPT:
Available
3rd Review PPT:
Available
Architecture Diagram:
Available

Literature Survey:
Available
Test Cases:
Not Available
Data Dictionary:
Not Available
Future Enhancement:
Available

SRS:
Available
All Modules:
Available
Documentation:
Available
Screen Shots:
Available

How to Install Softwares :Not Available
How to Execute Project :Not Available
Project Explanation Video :Not Available

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