Modeling and Analysis of Intentional And Unintentional Security Vulnerabilities in a Mobile Platform Page: 27
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In the above {X1, X2, ... , X1 } are the components of the pattern vector X, P(Ci) is the prior
probability of the class C, and P(X) = P(X1, X2, ... , X1) is the probability of the sample vector.
Multilayer Perceptron
In this approach, a feed-forward neural network was used. During training phase, the pat-
tern vectors with known class labels are presented at the input layer, and the outputs are observed.
The weights connecting the neurons in the penultimate layer to those in the output layer should be
adjusted so as to minimize the following mean square error function:
(2) E =Z(tk - Ok)2
k=1
where tk and Ok are the target (expected) and actually observed output values of the kth output
neuron. Since this weight training takes place in the backward direction, this algorithm is known
as the back-propagation learning algorithm. Once the network is trained with all the training
patterns, it can be used to classify the unknown patterns.
This model consisted of h number of hidden layers where h was calculated using the equa-
tion 3.
(3) h numberof attribs + nrumberof classes
2
The system was trained by 500 epochs with the learning rate of 0.2.
Random Forest
Random Forest is a classifier formed by an ensemble of decision tree classifiers {h(X, ek), k
1, -"- } where the {8k} are independent and identically distributed random vectors, and X is the
input vector. The random forest classifies X into the class with maximum vote considering the
votes for the most popular class at X by the constituent classifiers.
For the random forest approach I used 100 trees in the model.27
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Mohamed Issadeen, Mohamed Fazeen. Modeling and Analysis of Intentional And Unintentional Security Vulnerabilities in a Mobile Platform, dissertation, December 2014; Denton, Texas. (https://digital.library.unt.edu/ark:/67531/metadc700067/m1/41/: accessed July 18, 2024), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; .