The research work titled “Smart Intrusion Detection System Comprised of Machine Learning and Deep Learning” was published in European Journal for Engineering and Technology Research (EJERS) online journal in the October edition where a smart IDS model was proposed. In this present work, validation of the IDS model is conducted. KDD Cup'99 intrusion detection dataset was used to build the IDS model. A unique method is incorporated to test the performance of the model. Here, training is conducted by using the KDD'99 dataset. But testing is done through the NSL-KDD dataset. Testing is conducted in three-stage. In the first stage, using generic 41 features the accuracy, sensitivity, and FPR of detecting attack was 95.240%, 93.103%, 1.936% respectively for Random Forest and for MLP it is 87.811%, 90.065%, and 15.168% respectively. In the second stage selective 15 features are used where accuracy, sensitivity, and FPR of detecting attack is 70.808%, 81.992%, 43.971% respectively for Random Forest and for MLP it is 67.637%, 87.660%, 54.266% respectively. In the third stage selective 22 features are used where accuracy, sensitivity, and FPR of detecting attack is 97.001%, 96.643%, 2.272% for Random Forest respectively and for MLP it is 85.442%, 82.350 and 10.472 respectively. Total 3,11,021 record is used for training and 22,544 record is used for testing purpose. The final accuracy, sensitivity and FPR of the model can be resulted as 95.24%, 70.808%, 96.988% for 41 features, 93.103%, 87.68%, 97.233% for 15 features, 1.936%, 43.97%, 3.36% for 22 features. Therefore, the IDS model is efficient and effective.

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The research work titled “Smart Intrusion Detection System Comprised of Machine Learning and Deep Learning” was published in European Journal for Engineering and Technology Research (EJERS) online journal in the October edition where a smart IDS model was proposed. In this present work, validation of the IDS model is conducted. KDD Cup'99 intrusion detection dataset was used to build the IDS model. A unique method is incorporated to test the performance of the model. Here, training is conducted by using the KDD'99 dataset. But testing is done through the NSL-KDD dataset. Testing is conducted in three-stage. In the first stage, using generic 41 features the accuracy, sensitivity, and FPR of detecting attack was 95.240%, 93.103%, 1.936% respectively for Random Forest and for MLP it is 87.811%, 90.065%, and 15.168% respectively. In the second stage selective 15 features are used where accuracy, sensitivity, and FPR of detecting attack is 70.808%, 81.992%, 43.971% respectively for Random Forest and for MLP it is 67.637%, 87.660%, 54.266% respectively. In the third stage selective 22 features are used where accuracy, sensitivity, and FPR of detecting attack is 97.001%, 96.643%, 2.272% for Random Forest respectively and for MLP it is 85.442%, 82.350 and 10.472 respectively. Total 3,11,021 record is used for training and 22,544 record is used for testing purpose. The final accuracy, sensitivity and FPR of the model can be resulted as 95.24%, 70.808%, 96.988% for 41 features, 93.103%, 87.68%, 97.233% for 15 features, 1.936%, 43.97%, 3.36% for 22 features. Therefore, the IDS model is efficient and effective.

No abstract