Main Page
About FCIT
History
Strategy
Administration>
Current Administration
Prevouis Administration
Organization Strucutre
Industrial Advisory Board
PhotoAlbum
Lab Guides
Departments
Computer Science
Information Technology
Information Systems
Academics
Bachelor Programs
Graduate Programs
Executive Programs
Academic Calendar
Admission
Bachelor Degree & Transferring
Admission from the Foundation Year
Transferring to the Faculty
Graduate Studies
Graduate Programs
Executive Programs
Scientific Research
Groups and Units
Research Groups
Research Interests
Distinguished Scientists Program
Faculty Journal
Faculty and Staff
Faculty
CS Department
IT Department
IS Department
Staff
Accreditation Integration & Management System (AIM
Development and Quality Unit
Work at FCIT
Capabilities Under the Spotlight
Code of Ethics
Students
Bachelor
ِAcademic Services
Preparatory Year Courses
Students' Guide
Academic Advising
Laboratories and Facilities
Student rights and duties
Graduate
Polices and Regulations
Students' Guide
Student's Handbook
New Student Orientation
Templates of proposals and theses for masters and
Courses
CS Program
IT Program
IS Program
Alumni Registration
Students Activities
Entrepreneurship Club
Cybersecurity Club
Data Science Club
Programming Club
Community
Industrial partnerships
Cisco Academy
Microsoft Academy
Oracle Academy
Files
Researches
Contact Us
عربي
English
About
Admission
Academic
Research and Innovations
University Life
E-Services
Search
Faculty of Computing and Information Technology
Document Details
Document Type
:
Thesis
Document Title
:
High Performance Data Mining Techniques for Intrusion Detection
تقنيات عالية الأداء التعدين البيانات لكشف التسلل
Subject
:
High performance computing, data mining, intrusion detection
Document Language
:
English
Abstract
:
The rapid growth of computers transformed the way in which information and data was stored. With this new paradigm of data access, comes the threat of this information being exposed to unauthorized and unintended users. Many systems have been developed which scrutinize the data for a deviation from the normal behavior of a user or system, or search for a known signature within the data. These systems are termed as Intrusion Detection Systems (IDS). These systems employ different techniques varying from statistical methods to machine learning algorithms. Intrusion detection systems use audit data generated by operating systems, application softwares or network devices. These sources produce huge amount of datasets with tens of millions of records in them. To analyze this data, data mining is used which is a process to dig useful patterns from a large bulk of information. A major obstacle in the process is that the traditional data mining and learning algorithms are overwhelmed by the bulk volume and complexity of available data. This makes these algorithms impractical for time critical tasks like intrusion detection because of the large execution time. Our approach towards this issue makes use of high performance data mining techniques to expedite the process by exploiting the parallelism in the existing data mining algorithms and the underlying hardware. We will show that how high performance and parallel computing can be used to scale the data mining algorithms to handle large datasets, allowing the data mining component to search a much larger set of patterns and models than traditional computational platforms and algorithms would allow. We develop parallel data mining algorithms by parallelizing existing machine learning techniques using cluster computing. These algorithms include parallel backpropagation and parallel fuzzy ARTMAP neural networks. We evaluate the performances of the developed models in terms of speedup over traditional algorithms, prediction rate and false alarm rate. Our results showed that the traditional backpropagation and fuzzy ARTMAP algorithms can benefit from high performance computing techniques which make them well suited for time critical tasks like intrusion detection.
Supervisor
:
Joohan Lee
Thesis Type
:
Master Thesis
Publishing Year
:
1425 AH
2004 AD
Added Date
:
Monday, February 21, 2011
Researchers
Researcher Name (Arabic)
Researcher Name (English)
Researcher Type
Dr Grade
Email
معظم صديقي
Siddiqui, Muazzam
Researcher
Doctorate
maasiddiqui@kau.edu.sa
Files
File Name
Type
Description
29205.docx
docx
Back To Researches Page