Thesis( M. A. (Computer Science)) -- University of Limpopo , 2021 / In Software Defined Cognitive Radio Network (SDCRN), network security is a
significant issue. This issue arises when Software Defined Network (SDN) architecture
integrates with the Cognitive Radio Network (CRN) technology. SDN is designed to
improve network resource management, while CRN technology is meant at improving
spectrum management. These technologies are vulnerable to several malicious
attacks. These attacks include Distributed Denial of Service (DDoS) and Primary User
Emulation (PUE). Both the DDoS and PUE can be disrupt services in the SDCRN. To
curb these attacks, schemes which hardens the security of SDCRN need to be
designed. Thus, in this study we propose a security mechanism called
Extreme_Controller_Mechanism (XCM) that reduce the effects of DDoS and PUE. The
proposed XCM scheme was designed and evaluated in three simulation environment,
the OMNeT++, Octave, and MATLAB simulators. The SDCRN data set was generated
using the Neural Network back propagation algorithms. The data set was then used
in Matlab to evaluate the effectiveness of the prosed XCM scheme. XCM proved to be
effective and efficient at detection and prevention of DDoS and PUE attacks in
SDCRN. In terms of memory and processor utilisation, XCM proved to the best when
compared to other schemes such as the Advanced Support Vector Machine (ASVM)
and deep learning convolution network (CDLN). But in terms of detection time, the
ASVM was found to be the best performing scheme. Regarding our test for detection
rate, false positive and false negative, the XCM, ASVM and CDLM performed the
same. The results of the XCM were therefore the best and superior to the ASVM and
CDLM. This can be attributed to the fact that the XCM scheme is optimised for DDoS
and PUE attacks. We can therefore conclude that our XCM scheme is the best
performing scheme compared to the ASVM and CDLN schemes.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:ul/oai:ulspace.ul.ac.za:10386/3732 |
| Date | January 2021 |
| Creators | Sibanda, Brian |
| Contributors | Mthulisi, V. |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | ix, 106 leaves |
| Relation |
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