الفهرس 
IntroductionOnly 14 pages are availabe for public view
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Abstract
Intrusion Detection Systems (IDSs), are the most
appropriate methods to prevent and detect the attacks of
networks and computer systems. The security system
development, in the computing world, still requires
accurate work. Artificial intelligence technique can make
IDSs easier than before. As always, the most important
thing is to know more about smart systems through training
to acquire the truth things. This thesis focuses on creating
an environment for IDSs to teach them to practice the work
such as a security officer. The study presents several ways
to discover network anomalies using data mining tasks,
deep learning technology. In this thesis, two smart hybrid
systems were developed to explore any penetrations inside
the network. The first model divides into two basic stages.
The first stage includes the Genetic Algorithm (GA) in
selecting the characteristics with depends on a process of
extracting, Discretize And dimensionality reduction
through Proportional k-Interval Discretization (PKID) and
Fisher Linear Discriminant Analysis (FLDA) respectively.
At the end of the first stage combining classifier Naïve
Bayes and Decision Table classifier using NSL-KDD data
divided into two separate groups for training and testing.
The second stage completely depends on the first stage
outputs in order to improve the performance in terms of the
maximum accuracy in classification of penetrations, raising
the average of discovering and reducing of the average of
false alarms through participation with the Deep Learning
(DL) technology and collaboration with an algorithm
(SGD). The second hybrid model relies upon Particle