Authors:
Marwa Amara
1
;
2
;
Nadia Smairi
2
and
Mohamed Jaballah
2
Affiliations:
1
Depatment of Computer Sciences, Faculty of Sciences, Northern Border University, Arar, Saudi Arabia
;
2
LARIA UR22ES01, ENSI, Manouba University, Tunisia
Keyword(s):
Stacked Ensemble Model, Class Imbalance, CICIDS2017 Dataset, Cyber Threat Detection.
Abstract:
Intrusion Detection Systems (IDS) are critical for addressing the growing complexity of cyber threats in the Internet of Things (IoT) domain. This paper introduces a novel stacked ensemble approach combining Convolutional Neural Networks (CNN), Temporal Convolutional Networks (TCN), and Long Short-Term Memory (LSTM) models through a logistic regression meta-model. The proposed approach leverages the distinct strengths of each classifier; sequential pattern recognition by LSTMs, temporal dependency modeling by TCNs, and spatial feature extraction by CNNs to create a robust and reliable detection framework. To address the class imbalance problem, we applied various balancing techniques, including Oversampling, Undersam-pling, and a hybrid Meet-in-the-Middle method. The effectiveness of the approach is demonstrated on the CICIDS2017 dataset, achieving an accuracy of 99.99% and an F1-score of 100% with Oversampling, and 99.93% accuracy with the Meet-in-the-Middle technique.