Abstract
The potential of machine learning mechanisms played a key role in improving the intrusion detection task. However, other factors such as quality of data, overfitting, imbalanced problems, etc. may greatly affect the performance of an intelligent intrusion detection system (IDS). To tackle these issues, this paper proposes a novel machine learning-based IDS called i-2NIDS. The novelty of this approach lies in the application of the nested cross-validation method, which necessitates using two loops: the outer loop is for hyper-parameter selection that costs least error during the run of a small amount of training set and the inner loop for the error estimation in the test set. The experiments showed significant improvements within NSL-KDD dataset with a test accuracy rate of 99.97%, 99.79%, 99.72%, 99.96%, and 99.98% in detecting normal activities, DDoS/DoS, Probing, R2L and U2R attacks, respectively. The obtained results approve the efficiency and superiority of the approach over other recent existing experiments.
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