Intrusion Detection System Based on FastICA and Multi-Grained Cascaded Forest

With the advancement of big data, microproces-sors, and other applications, the Internet of Things (IoT) has seen significant development. Owing to the lack of necessary security defense mechanisms, IoT devices are susceptible to being targeted and controlled by attackers. They can manipu-late a vast array of IoT devices to launch DDoS attacks on the network infrastructure of a country or region, leading to serious economic losses and social security risks. Intrusion detection methods based on deep learning generally rely on numerous high-quality training instances, making it difficult to apply them to network traffic lacking sufficient labeled data. Traditional machine learning (ML) methods have limited capabilities in extracting and representing features from high-dimensional data, making it challenging to discover underlying structures and patterns in the data. To address the above issues, this paper proposes an intrusion detection system (IDS) that integrates the Fast Independent Component Analysis (FastICA) module and the multi-Grained Cascade Forest (GcForest). By utilizing FastICA to preprocess the raw data and extract significant features, as well as improving the model's feature extraction capabilities with the aid of cascade modules. Experiments have indicated that FastICA-GcF achieves the binary classification accuracy of 99.95% on CIC-DDoS-2019, and the multiclass classification accuracy of 98.77%, outperforming existing DDoS attack detection models.