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UPSI Digital Repository (UDRep)
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| Abstract : Universiti Pendidikan Sultan Idris |
| This research aims to assist the developers of intrusion detection systems (IDS) to make the right
selection decision of a suitable classification model. Many classification algorithms have been
developed to be used in an IDS detection engine. Developers of IDS have been facing challenges
in how to evaluate and benchmark classifiers. Different perspectives and multiple, conflicting
importance evaluation criteria represent the challenges in evaluation, benchmarking and selecting
suitable IDS classifiers. The current evaluation studies depend on evaluating the IDS classifier
from a single incomplete perspective. In each study, the evaluations have been achieved with
reference to some security-related evaluation criteria and ignore performance criteria. Furthermore,
the weighting process that reflects the importance of each criterion depended on a personal
subjective perspective. The goal of this thesis is to set a new standardisation and benchmarking
framework based on a set of standardised criteria and set of unified multi-criteria decision-making
(MCDM) methods that overcome the shortage. This study attempts to establish and standardise
IDS classifier evaluation criteria and construct a decision matrix (DM) based on crossover of the
standardised criteria and 12 classifiers. This DM was evaluated using datasets consist of 125,973
records; each record consists of 41 features. Subsequently, the classifiers are evaluated and ranked
using unified MCDM techniques. The proposed framework consists of three main parts: the first
for standardising evaluation criteria, the second for constructing the DM and the third for
developing weighting and ranking unified MCDM methods and IDS classifiers evaluation and
benchmarking. The fuzzy Delphi method (FDM) has been used for criteria standardisation.
Integrated weighting methods using direct rating and the entropy objective method are developed
to calculate the weights of the criteria. The Vlse Kriterijumska Optimizacija Kompromisno Resenje
(VIKOR) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) ranking
methods were integrated into a unified method for ranking the selected classifiers. The Borda voting
method was used to unify the different ranks and perform a group ranking context. An objective
validation process has been used to validate the ranking results. The mean ± standard deviation was
computed to ensure that the classifier ranking underwent systematic ranking. The following results
were confirmed. (1) FDM is a suitable way to reach a standard set of evaluation criteria. (2) Using
an integrated (subjective, objective) weighting method can find the suitable criteria weights. (3) A
unified ranking method that integrates VIKOR and TOPSIS effectively solves the classifier
selection problem and (4) the objective validation shows significant differences between the
groups’ scores, indicating indicates that the ranking results of the proposed framework were valid.
(5) The evaluation of the proposed framework shows an advantage over the benchmarked works
with a percentage of 100%. The implications of this study benefit IDS developers in making the
right decisions in selecting the best classification model. Researchers can use the proposed
framework for evaluation and selection in similar evaluation problems. |
| References |
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