Detection of Malicious Attacks by Meta Classification Algorithms

We address the problem of malicious node detection in a network based on the characteristics in the behavior of the network. This issue brings out a challenging set of research papers in the recent contributing a critical component to secure the network. This type of work evolves with many changes in the solution strategies. In this work, we propose carefully the learning models with cautious selection of attributes, selection of parameter thresholds and number of iterations. In this research, appropriate approach to evaluate the performance of a set of meta classifier algorithms (Ad Boost, Attribute selected classifier, Bagging, Classification via Regression, Filtered classifier, logit Boost, multiclass classifier). The ratio between training and testing data is made such way that compatibility of data patterns in both the sets are same. Hence we consider a set of supervised machine learning schemes with meta classifiers were applied on the selected dataset to predict the attack risk of the network environment. The trained models were then used for predicting the risk of the attacks in a web server environment or by any network administrator or any Security Experts. The Prediction Accuracy of the Classifiers was evaluated using 10-fold Cross Validation and the results have been compared to obtain the accuracy.