Intrusion Detection Model Using Naive Bayes and Deep Learning Technique
Mohammed Tabash, Mohamed Abd Allah, and Bella Tawfik
Faculty of Computers and Informatics, Suez
Canal University, Egypt
Abstract: The increase of
security threats and hacking the computer networks are one of the most
dangerous issues should treat in these days. Intrusion Detection Systems
(IDSs), are the most appropriate methods to prevent and detect the attacks of
networks and computer systems. This study presents several techniques to
discover network anomalies using data mining tasks, Machine learning technology
and dependence of artificial intelligence techniques. In this research, the
smart hybrid model was developed to explore any penetrations inside the
network. The 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) on respectively. At the end of the first stage combining Naïve
Bayes classifier (NB) and Decision Table (DT) using NSL-KDD data set divided
into two separate groups for training and testing. The second stage completely
depends on the first stage outputs (predicted class) and reclassified with
multilayer perceptrons using Deep Learning4J (DL) and the use of algorithm
Stochastic Gradient Descent (SGD). In order to improve the performance in terms
of the accuracy in classification of penetrations, raising the average of
discovering and reducing the false alarms. The comparison of the proposed model
and conventional models show the superiority of the proposed model and the
previous conventional hybrid models. The result of the proposed model is
99.9325 of classification accuracy, the rate of detection is 99.9738 and
0.00093 of false alarms.
Keywords: Classification, intrusion detection, deep
learning, NSL-KDD, genetic algorithm, naïve bayes.
Received December 30, 2017; accepted April
17, 2018