Intrusion-Miner: A Hybrid Classifier for Intrusion Detection using Data Mining
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2019, Vol 10, Issue 4
Abstract
With the rapid growth and usage of internet, number of network attacks have increase dramatically within the past few years. The problem facing in nowadays is to observe these attacks efficiently for security concerns because of the value of data. Consequently, it is important to monitor and handle these attacks and intrusion detection system (IDS) has potentially diagnostic ability to handle these attacks to secure the network. Numerous intrusion detection approaches are presented but the main hindrance is their performance which can be improved by increasing detection rate as well as decreasing false positive rates. Optimizing the performance of IDS is very serious issue and challenging fact that gets more attention from the research community. In this paper, we proposed a hybrid classification approach ‘Intrusion-Miner’ with the help of two classifier algorithm for network anomaly detection to get optimum result and make it possible to detect network attacks. Thus, principal component analysis (PCA) and Fisher Discriminant Ratio (FDR) have been implemented for the feature selection and noise removal. This hybrid approach is compared with J48, Bayesnet, JRip, SMO, IBK and evaluate the performance using KDD99 dataset. Experimental result revealed that the precision of the proposed approach is measured as 96.1 % with low false positive and high false negative rate as compare to other state-of-the-art algorithm. The simulation result evaluation shows that perceptible progress and real-time intrusion detection can be attained as we apply the suggested models to identify diverse kinds of network attacks.
Authors and Affiliations
Samra Zafar, Muhammad Kamran, Xiaopeng Hu
Keywords
Related Articles
- EP ID EP551404
- DOI 10.14569/IJACSA.2019.0100440
- Views 99
- Downloads 0
A Fresnelet-Based Encryption of Medical Images using Arnold Transform
Medical images are commonly stored in digital media and transmitted via Internet for certain uses. If a medical information image alters, this can lead to a wrong diagnosis which may create a serious health problem. More...
Off-Line Arabic (Indian) Numbers Recognition Using Expert System
This paper proposes an effective approach to automatic recognition of printed Arabic numerals which are extracted from digital images. First, the input image is normalized and pre-processed to an acceptable form. From th...
Evaluation of Spectral Efficiency, System Capacity And Interference Effects on CDMA Communication System
Wireless communication technology have been developed based on exploring new mobile communications frequency bands, reasonable use of frequency resources and minimization, portability and multifunction’s of mobile statio...
An Optimal Load Balanced Resource Allocation Scheme for Heterogeneous Wireless Networks based on Big Data Technology
An important issue in heterogeneous wireless networks is how to optimally utilize various radio resources. While many methods have been proposed for managing radio resources in each network, these methods are not suitabl...
Prediction of Visibility for Color Scheme on the Web Browser with Neural Networks
In this study, we propose neural networks for predicting the visibility of color schemes. In recent years, most of us have accessed websites owing to the spread of the Internet. It is necessary to design web pages that a...