Because of the tremendous growth in the network based services as well as the sharing of sensitive data, the network security becomes a challenging task. The major risk in the network is the intrusion. Among various hardening system, intrusion detection system (IDS) plays a significant role in providing network security. Several traditional techniques are utilized for network security but still they lack in providing security. The major drawbacks of these network security algorithms are inaccurate classification results, increased false alarm rate, etc. to avoid these issues, an Integrated Perceptron Kernel Classifier is proposed in this work. The input raw data are preprocessed initially for the purpose of removing the noisy data as well as irrelevant data. Then the features form the preprocessed data are extracted by clustering it depending up on the Fuzzy C-Mean Clustering. Then the clustered features are extracted by employing the Density based Distance Maximization approach. After this the best features are selected using Modified Ant Colony Optimization by improving the convergence time. Finally the extracted best features are classified for identifying the network traffic as normal and abnormal by introducing an Integrated Perceptron Kernel Classifier. The performance of this framework is evaluated and compared with the existing classifiers such as SVM and PNN. The results prove the superiority of this framework with better classification accuracy.