Attribute Normalization Techniques and Performance of Intrusion Classifiers: A Comparative Analysis
نویسندگان
چکیده
Network traffic have several attributes with different range of values. These attributes can be qualitative or quantitative in nature. Attributes with large values significantly influence the performance of intrusion classifier making it bias towards them. Attribute normalization eliminates such dominance of the attributes by scaling the values of all the attributes within a specific range. The paper discusses various normalization techniques and their influence on intrusion classifiers such as Random Forest, Bayes Net, Naive Bayes, NB Tree and Decision Tree. Furthermore, the concept of hybrid normalization is applied by normalizing the qualitative and quantitative attributes differently. Experiments on KDD Cup 99 suggests that the hybrid normalization can achieve better results as compared to conventional normalization. [Ihsan Z, Idris MY, Abdullah AH. Attribute Normalization Techniques and Performance of Intrusion Classifiers: A Comparative Analysis. Life Sci J 2013;10(4): 2568-2576] (ISSN:1097-8135). http://www.lifesciencesite.com. 343
منابع مشابه
Mining Techniques in Network Security to Enhance Intrusion Detection Systems
In intrusion detection systems, classifiers still suffer from several drawbacks such as data dimensionality and dominance, different network feature types, and data impact on the classification. In this paper two significant enhancements are presented to solve these drawbacks. The first enhancement is an improved feature selection using sequential backward search and information gain. This, in ...
متن کاملPerformance Comparison of Intrusion Detection System Classifiers Using Various Feature Reduction Techniques
In this paper, we compare the performance of Intrusion Detection System Classifiers using various feature reduction techniques. To enhance the learning capabilities and reduce the computational intensity of competitive learning neural network classifiers, different dimension reduction techniques have been proposed. These include: Principal Component Analysis, Linear Discriminant Analysis, Indep...
متن کاملApplication of ensemble learning techniques to model the atmospheric concentration of SO2
In view of pollution prediction modeling, the study adopts homogenous (random forest, bagging, and additive regression) and heterogeneous (voting) ensemble classifiers to predict the atmospheric concentration of Sulphur dioxide. For model validation, results were compared against widely known single base classifiers such as support vector machine, multilayer perceptron, linear regression and re...
متن کاملNormalization of qPCR array data: a novel method based on procrustes superimposition
MicroRNAs (miRNAs) are short, endogenous non-coding RNAs that function as guide molecules to regulate transcription of their target messenger RNAs. Several methods including low-density qPCR arrays are being increasingly used to profile the expression of these molecules in a variety of different biological conditions. Reliable analysis of expression profiles demands removal of technical variati...
متن کاملA Hybrid Machine Learning Method for Intrusion Detection
Data security is an important area of concern for every computer system owner. An intrusion detection system is a device or software application that monitors a network or systems for malicious activity or policy violations. Already various techniques of artificial intelligence have been used for intrusion detection. The main challenge in this area is the running speed of the available implemen...
متن کامل