In recent years, the popularity of network intrusion detection systems
(NIDS) has surged, driven by the widespread adoption of cloud
technologies. Given the escalating network traffic and the continuous
evolution of cyber threats, the need for a highly efficient NIDS has
become paramount for ensuring robust network security. Typically,
intrusion detection systems utilize either a pattern-matching system or
leverage machine learning for anomaly detection. While pattern-matching
approaches tend to suffer from a high false positive rate (FPR), machine
learning-based systems, such as SVM and KNN, predict potential attacks
by recognizing distinct features. However, these models often operate on
a limited set of features, resulting in lower accuracy and higher FPR.
In our research, we introduced a deep learning model that harnesses the
strengths of a Convolutional Neural Network (CNN) combined with a
Bidirectional LSTM (Bi-LSTM) to learn spatial and temporal data
features. The model, evaluated using the NSL-KDD dataset, exhibited a
high detection rate with a minimal false positive rate. To enhance
accuracy, K-fold cross-validation was employed in training the model.
This paper showcases the effectiveness of the CNN with Bi-LSTM algorithm
in achieving superior performance across metrics like accuracy,
F1-score, precision, and recall. The binary classification model trained
on the NSLKDD dataset demonstrates outstanding performance, achieving a
high accuracy of 99.5% after 10-fold cross-validation, with an average
accuracy of 99.3%. The model exhibits remarkable detection rates
(0.994) and a low false positive rate (0.13). In the multiclass setting,
the model maintains exceptional precision (99.25%), reaching a peak
accuracy of 99.59% for k-value=10. Notably, the Detection Rate for
k-value=10 is 99.43%, and the mean False Positive Rate is calculated as
0.214925.