Anomaly Detection Via Network Intrusion Using A Hybrid CNN And LSTM

Abstract

The challenge posed by the measurement of quality of service (QOS) in network environments has become increasingly critical, especially in light of the constantly evolving land scape of cybersecurity threats. While various research endeavors have utilized metrices such as the false positive rate to evaluate the effectiveness of intrusion Detection systems (IDSs) in safeguarding network integrity the strategies employed by malicious actors often prioritize disrupting network and consuming resources. This problem has stimulated to undergo intensive research to develop a hybrid convolutional neural Network and Long Short-Term Memory algorithm for network anomaly intrusion detection. To evaluate the performance of the model, the precision, recall, and f1-score evaluation metrics was applied.  The process of hybridization involves stacking the output of the CNN layers with the LSTM layer. The experiment revealed that network anomaly detection classifier shows a significant performance for data split of 70:30 training to testing. LSTM-CNN model was demonstrated with exceptional performance, achieving an accuracy of 99.99% after only 2 epochs. This result indicates the robustness and efficiency of the proposed hybrid mode in detecting network intrusion.