Core Concepts
Novel neural network architecture MIAEFS enhances IoT IDS performance through feature selection.
Abstract
The content introduces the Multiple-Input Auto-Encoder (MIAE) and its extension, MIAEFS, for IoT intrusion detection systems. It addresses challenges of diverse data in IoT devices and proposes a feature selection layer to improve classification accuracy. Experimental results show superior performance compared to conventional methods.
Introduction to IoT IDS Challenges:
Heterogeneity of IoT devices poses challenges for intrusion detection systems.
Data diversity complicates training effective machine learning models.
Proposed Solution - MIAE and MIAEFS:
Introduction of Multiple-Input Auto-Encoder (MIAE) for transforming heterogeneous inputs.
Addition of a feature selection layer in MIAEFS to retain relevant features and discard redundant ones.
Experimental Results:
Superior performance of MIAE and MIAEFS over other methods on benchmark datasets.
Combined with Random Forest classifier, achieves 96.5% accuracy in detecting sophisticated attacks like Slowloris.
Data Quality Evaluation Metrics:
Measures include between-class variance, within-class variance, and data quality to assess representation characteristics.
Experimental Settings:
Utilization of TensorFlow and Scikit-learn frameworks for experiments.
Grid search used to tune hyperparameters of classifiers.
Datasets Used:
NSLKDD, UNSW-NB15, IDS2017 datasets employed for evaluations.
Performance Analysis:
Comparison with conventional classifiers, dimensionality reduction models, unsupervised representation learning methods, and unsupervised feature selection models.
Complexity Analysis:
Running time and model size considerations discussed for practical deployment.
Stats
Moreover, MIAE and MIAEFS combined with the Random Forest (RF) classifier achieve accuracy of 96.5% in detecting sophisticated attacks, e.g., Slowloris.
The average running time for detecting an attack sample using RF with the representation of MIAE and MIAEFS is approximate 1.7E-6 seconds.
Quotes
"The explosive growth of IoT devices has improved every corner of our lives but also leads to hundreds of thousands of malwares on the networks."
"Developing a robust IDS for IoT systems is a challenging task due to the complexity of data harvested from diverse origins."