In text classification task , models have shown remarkable accuracy across various datasets. However, confusion often arises when certain categories within the dataset are too similar, causing misclassification of certain samples. This paper proposes an improved method for this problem, through the creation of a three-layer text graph for the corpus, which is used to calculate the Category Correlation Matrix (CCM). Additionally, this paper introduces category-adaptive contrastive learning for text embedding from the encoder, enhancing the model’s ability to distinguish between samples in confusable categories that are easily confused. Soft labels are generated using this matrix to guide the classifier, preventing the model from becoming overconfident with one-hot vectors. The efficacy of this approach was demonstrated through experimental evaluations on three text encoders and six different datasets.

With the surge in various types of network traffic and the widespread application of encryption technology, the classification of encrypted traffic plays an increasingly important role in ensuring network security, enhancing quality of service, and managing network traffic. However, most existing methods often suffer from issues such as excessive reliance on manual feature extraction and expert knowledge, unstable classification performance, and lack of transfer learning capabilities. To address these challenges, this paper proposes a high-performance hybrid encrypted traffic classification framework, FERNN-AC. It directly extracts features from raw traffic and fully explores and utilizes the spatiotemporal information of traffic data by integrating specially designed feature enhancement module and temporal feature extraction module in a reasonable manner. It introduces angle constraints and can be combined with meta-learning, thereby improving classification performance while possessing certain transfer learning capabilities. The experiments are conducted on three datasets, and the results shows that compare with relevant baseline methods, FERNN-AC has excellent and stable classification performance.

In this paper, we focus on mining attacks targeting the Proof of Work (PoW) consensus mechanism in blockchain-based systems. Specifically, we model mining as a game and propose a mining attack – the Selfish mining-based denial of service (SDoS) attack. By studying the choices (mining or stopping) of honest miners under the attack and the adversary’s revenue, we demonstrate that selfish mining is incentive-compatible with game-level denial of service attack, and that SDoS can be more threatening than existing mining attacks. Even under the worst assumption, the adversary only needs to master more than 19.6% of the total mining power to increase the revenue, and can launch a 51% attack with much less than 50%. In addition, we show that honest miners may make decisions based on the overall or current utility, and choosing the current utility is more beneficial to the adversary.