LG-BiTCN: A Lightweight Malicious Traffic Detection Model Based on Federated Learning for Internet of Things

The rapid growth of IoT devices has increased security attack behaviors, posing a challenge to IoT security. Some Federated-Learning-based detection methods have been widely used to detect malicious attacks in the IoT by analyzing network traffic; because of the nature of Federated Learning, these methods can protect user privacy and reduce bandwidth consumption. However, existing malicious traffic detection models are often complex, requiring significant computational resources for training. In addition, high-dimensional input features often contain redundant information, which further increases computational overhead. To mitigate this, many model lightweighting techniques are utilized, and many non-end-to-end dimensionality reduction methods are employed; however, these lightweighting methods still struggle to meet the computational demands, and these feature downscaling methods tend to compromise the model’s generalizability and accuracy. In addition, existing methods are unable to dynamically select long-term dependencies when extracting traffic time-series features, limiting the performance of the model when dealing with long time series. To address the above challenges, this paper proposes a lightweight malicious traffic detection model, named the lightweight gated bidirectional temporal convolutional network (LG-BiTCN), based on Federated Learning. First, we use global average pooling (GAP) and a pointwise convolutional layer as a classification module, significantly reducing the model’s parameter count. We also propose an end-to-end adaptive PCA dimension adjustment algorithm for automatic dimensionality reduction to reduce computational complexity and enhance model generalizability. Second, we incorporate gated convolution into the LG-BiTCN architecture, allowing for the dynamic selection of long-term dependencies, enhancing detection accuracy while maintaining computational efficiency. We evaluated the LG-BiTCN’s effectiveness by comparing it with three advanced baseline models on three generic datasets. The results show that the LG-BiTCN achieves over 99.6% accuracy while maintaining the lowest computational complexity. Additionally, in a Federated Learning setup, it requires just two communication rounds to reach 96.75% accuracy.