A Review on Challenges and Future Research Directions for Machine Learning-Based Intrusion Detection System

Thakkar, Ankit; Lohiya, Ritika

doi:10.1007/s11831-023-09943-8

Cited by 13 publications

(2 citation statements)

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“…After passing through the ReLU activation function again, the data are sent to the max-pooling layer. This step further abstracts features and reduces dimensionality, helping to strengthen the feature extraction of the model and generalization capabilities, as shown in Equation (8).…”

Section: The Dvacnn-based Intrusion Detection Modelmentioning

confidence: 99%

“…To address this challenge, some approaches propose aggregating local datasets from devices for deep learning training. However, this practice may raise privacy concerns because sensitive information from devices could potentially be exposed to unauthorized personnel during data transmission [ 8 ]. Therefore, in researching network intrusion detection systems, it is essential to balance data security and model effectiveness to ensure that systems protect IIoT security while respecting user privacy rights.…”

Section: Introductionmentioning

confidence: 99%

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Improved Intrusion Detection Based on Hybrid Deep Learning Models and Federated Learning

Huang,

Chen,

Liu

et al. 2024

Sensors

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The security of the Industrial Internet of Things (IIoT) is of vital importance, and the Network Intrusion Detection System (NIDS) plays an indispensable role in this. Although there is an increasing number of studies on the use of deep learning technology to achieve network intrusion detection, the limited local data of the device may lead to poor model performance because deep learning requires large-scale datasets for training. Some solutions propose to centralize the local datasets of devices for deep learning training, but this may involve user privacy issues. To address these challenges, this study proposes a novel federated learning (FL)-based approach aimed at improving the accuracy of network intrusion detection while ensuring data privacy protection. This research combines convolutional neural networks with attention mechanisms to develop a new deep learning intrusion detection model specifically designed for the IIoT. Additionally, variational autoencoders are incorporated to enhance data privacy protection. Furthermore, an FL framework enables multiple IIoT clients to jointly train a shared intrusion detection model without sharing their raw data. This strategy significantly improves the model’s detection capability while effectively addressing data privacy and security issues. To validate the effectiveness of the proposed method, a series of experiments were conducted on a real-world Internet of Things (IoT) network intrusion dataset. The experimental results demonstrate that our model and FL approach significantly improve key performance metrics such as detection accuracy, precision, and false-positive rate (FPR) compared to traditional local training methods and existing models.

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Section: The Dvacnn-based Intrusion Detection Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%