Multiple-Input Auto-Encoder Guided Feature Selection for IoT Intrusion Detection Systems

Konsep Inti

Novel neural network architecture MIAEFS enhances IoT IDS performance through feature selection.

Abstrak

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.

Statistik

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.

Kutipan

"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."

Wawasan Utama Disaring Dari

Multiple-Input Auto-Encoder Guided Feature Selection for IoT Intrusion Detection Systems

by Phai Vu Dinh... pada arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.15511.pdf

Pertanyaan yang Lebih Dalam

How can the proposed model adapt to evolving threats in the IoT landscape

The proposed model, Multiple-Input Auto-Encoder Guided Feature Selection (MIAEFS), can adapt to evolving threats in the IoT landscape by its ability to handle heterogeneous data with different dimensions. This flexibility allows the model to process inputs from various sources and characteristics, making it adaptable to new types of attacks that may emerge in the IoT environment. Additionally, MIAEFS incorporates a feature selection layer that helps in selecting informative features from the representation vector, enabling it to focus on relevant aspects of data for intrusion detection. By continuously updating and refining these selected features based on evolving threat patterns, the model can effectively adjust and improve its detection capabilities over time.

What are potential limitations or biases introduced by the feature selection process

While feature selection is crucial for enhancing the accuracy and efficiency of intrusion detection systems like MIAEFS, there are potential limitations and biases introduced through this process. One limitation is related to the subjective nature of determining which features are considered important or redundant. The criteria used for feature selection may vary depending on factors such as dataset characteristics, domain knowledge, or specific use cases, leading to potential biases in what features are prioritized.
Another limitation could arise from overfitting during feature selection if not carefully managed. Selecting features solely based on their performance within a specific dataset without considering generalizability could lead to an overly complex model that performs well only on training data but fails when applied to unseen data.
Additionally, there might be inherent biases present in the original dataset itself that get carried forward into the selected features. If certain groups or classes are underrepresented or misrepresented in the training data used for feature selection, this bias can impact the effectiveness of intrusion detection models like MIAEFS when deployed in real-world scenarios.

How might advancements in AI impact the future development of intrusion detection systems

Advancements in AI have significant implications for future developments in intrusion detection systems (IDS). With AI technologies becoming more sophisticated and capable of handling large volumes of diverse data efficiently, IDSs stand to benefit greatly from these advancements.
One key impact is improved accuracy and speed in detecting anomalies and identifying potential security threats within IoT networks. Advanced AI algorithms can analyze complex patterns across multiple sources rapidly and accurately detect abnormal behavior indicative of cyberattacks.
Furthermore, AI enables IDSs like MIAEFS to adapt dynamically to changing attack vectors by learning from new data patterns without requiring manual intervention constantly. This adaptive capability enhances resilience against emerging threats while reducing false positives commonly associated with traditional rule-based approaches.
Moreover, advancements such as deep learning techniques allow IDSs to uncover hidden relationships within vast datasets that might go unnoticed using conventional methods. By leveraging neural networks' capacity for nonlinear pattern recognition coupled with unsupervised learning mechanisms like autoencoders as seen in MIAEFS - IDSs can enhance their predictive capabilities significantly.