This paper proposes an enhanced Transformer-based intrusion detection model to tackle the challenges of lengthy training time, inaccurate detection of overlapping classes, and poor performance in multi-class classification of current intrusion detection models. Specifically, the proposed model includes the following: (i) A data processing strategy that initially reduces the data dimension using a stacked auto-encoder to speed up training. In addition, a novel under-sampling method based on the KNN principle is introduced, along with the Borderline-SMOTE over-sampling method, for hybrid data sampling that balances the dataset while addressing the issue of low detection accuracy in overlapping data classes. (ii) An improved position encoding method for the Transformer model that effectively learns the dependencies between features by embedding the position information of features, resulting in better classification accuracy. (iii) A two-stage learning strategy in which the model first performs rough binary prediction (determining whether it is an illegal intrusion) and then inputs the prediction value and original features together for further multi-class prediction (predicting the intrusion category), addressing the issue of low accuracy in multi-class classification. Experimental results on the official NSL-KDD test set demonstrate that the proposed model achieves an accuracy of 88.7% and an F1-score of 88.2% in binary classification and an accuracy of 84.1% and an F1-score of 83.8% in multi-class classification. Compared to existing intrusion detection models, our model exhibits higher accuracy and F1-score and trains faster than other models.

Location-based services (LBS) bring convenience to people’s lives but are also accompanied with privacy leakages. To protect the privacy of LBS users, many location privacy protection algorithms were proposed. However, these algorithms often have difficulty to maintain a balance between service quality and user privacy. In this paper, we first overview the shortcomings of the existing two privacy protection architectures and privacy protection technologies, then we propose a location privacy protection method based on blockchain. Our method satisfies the principle of k-anonymity privacy protection and does not need the help of trusted third-party anonymizing servers. The combination of multiple private blockchains can disperse the user’s transaction records, which can provide users with stronger location privacy protection and will not reduce the quality of service. We also propose a reward mechanism to encourage user participation. Finally, we implement our approach in the Remix blockchain to show the efficiency, which further indicates the potential application prospect for the distributed network environment.