Classification of Intrusion Using CNN with IQR (Inter Quartile Range) Approach

Cyber-attacks are getting more and more complicated, using intricate patterns that are challenging to find using conventional techniques. IDS (Intrusion Detection System) are essential for defending computer networks from online risks. This article undertakes a thorough review of three preprocessing methods used with a Convolutional Neural Network (CNN) for intrusion detection along with SMOTE, Z-score, and IQR (Inter Quartile Range) which will be used for feature extraction. The study carefully evaluates the evaluation parameters such as accuracy, precision, and recall, to ascertain the most efficient preprocessing approach. When dealing with sequential data in intrusion detection systems, utilizing a CNN to classify intrusion is a potent technique. The accuracy and dependability of an intrusion detection model can be improved by combining CNN with preprocessing methods like IQR. By handling outliers using the IQR approach, the CNN model is trained on a more accurate and reliable dataset. From the results obtained proposed IQR+CNN produces Accuracy of 90.3%, Precision of 0.90, Recall of 0.87 and F Measure of 0.9. The tool used is Jupyter Notebook and language used is python.