With the rapid development of the internet, network security threats are becoming increasingly complex and diverse, making traditional intrusion detection systems (IDSs) inadequate for handling the growing variety of sophisticated attacks. In particular, traditional methods based on rule matching and manual feature extraction demonstrate significant limitations in dealing with small samples and unknown attacks. This paper proposes an intrusion detection system based on multi-level feature extraction and inductive learning (MFEI-IDS) to address these challenges. The model innovatively integrates Fully Convolutional Networks (FCNs) with the Transformer architecture (FCN–Transformer) for feature extraction and utilizes an inductive learning component for efficient classification. The FCN–Transformer Encoder extracts multi-level features from raw network traffic, capturing local spatial patterns and global temporal dependencies, significantly enhancing the representation of network traffic while reducing reliance on manual feature engineering. The inductive learning module employs a dynamic routing mechanism to map sample feature vectors into robust class vector representations, achieving superior generalization when detecting unseen attack types. Compared to existing FCN–Transformer models, MFEI-IDS incorporates inductive learning to handle data imbalance and small-sample scenarios. Experiments on ISCX 2012 and CIC-IDS 2017 datasets show that MFEI-IDS outperforms mainstream IDS methods in accuracy, precision, recall, and F1-score, excelling in cross-dataset validation and demonstrating strong generalization capabilities. These results validate the practical potential of MFEI-IDS in small-sample learning, unknown attack detection, and dynamic network environments.

Smart terminals use passwords and physiological characteristics such as fingerprints to authenticate users. Traditional authentication methods work when users unlock their phones, but they cannot continuously verify the user’s legal identity. Therefore, the one-time authentication implemented by conventional authentication methods cannot meet security requirements. Implicit authentication technology based on user behavior characteristics is proposed to achieve the continuous and uninterrupted authentication of savvy terminal users. This paper proposes an implicit authentication method that fuses keystroke and sensor data. To improve the accuracy of authentication, a neural network-based feature extraction model that integrates keystroke data and motion sensor data is designed. A feature space with dual-channel fusion is constructed, and a dataset collected in real scenarios is built by considering the changes in user activity scenarios and the differences in terminal holding postures. Experimental results on the collected data show that the proposed method has improved the accuracy of user authentication to a certain extent.