STID-Net: Optimizing Intrusion Detection in IoT with Gradient Descent | JoVE Visualize

Area of Science:

Cybersecurity
Network Security
Machine Learning

Background:

The proliferation of Internet of Things (IoT) devices in medical and industrial sectors has amplified network vulnerabilities.
Existing intrusion detection systems (IDS) often fail to capture complex, irregular patterns in dynamic IoT data, limiting their applicability.
A robust and scalable IDS is crucial for securing diverse IoT applications.

Purpose of the Study:

To propose STID-Net, a novel intrusion detection system designed to address the limitations of current methods in dynamic IoT environments.
To enhance the detection of spatial and temporal patterns, including long-term dependencies, in network intrusion data.
To evaluate the performance and robustness of STID-Net across different IoT application datasets.

Main Methods:

STID-Net integrates customized convolutional kernels for spatial feature extraction and Long Short-Term Memory (LSTM) layers for temporal sequence modeling.
An attention mechanism is incorporated to improve the detection of long-term dependencies within intrusion patterns.
The system was experimented with Mini-Batch Gradient Descent (MBGD) and Stochastic Gradient Descent (SGD) optimizers on Internet of Medical Things (IoMT) and Industrial Internet of Things (IIoT) datasets.

Main Results:

STID-Net achieved high accuracy, with SGD optimization yielding 98.58% on IoMT and 99.15% on IIoT datasets, surpassing MBGD optimization (97.14% and 97.85%, respectively).
The SGD optimizer demonstrated faster convergence and better weight adjustments, proving effective for noisy datasets.
STID-Net outperformed standalone Convolutional Neural Network (CNN) and LSTM models, showcasing its superior performance and robustness.

Conclusions:

STID-Net demonstrates superior capability in identifying irregular patterns and long-term dependencies in dynamic intrusion data.
The proposed model is robust and scalable for diverse IoT applications, particularly in the medical and industrial domains.
SGD optimization enhances STID-Net's performance, making it a reliable solution for real-world network security challenges.