A classifier trained on one dataset rarely works on other datasets obtained under different conditions because of domain shifting. Such a problem is usually solved by domain adaptation methods. In this paper, we propose a novel unsupervised domain adaptation (UDA) method based on Interchangeable Batch Normalization (InterBN) to fuse different channels in deep neural networks for adversarial domain adaptation.Specifically, we first observe that the channels with small batch normalization scaling factor have less influence on the whole domain adaption, followed by a theoretical proof that the scaling factors for some channels will definitely come close to zero when imposing a sparsity regularization. Then, we replace the channels that have smaller scaling factors in the source domain with the mean of the channels which have larger scaling factors in the target domain or vice versa. Such a simple but effective channel fusion scheme can drastically increase the domain adaption ability.Extensive experimental results show that our InterBN significantly outperforms the current adversarial domain adaptation methods by a large margin on four visual benchmarks. In particular, InterBN achieves a remarkable improvement of 7.7% over the conditional adversarial adaptation networks (CDAN) on VisDA-2017 benchmark.

Recently, many password guessing algorithms have been proposed, seriously threatening cyber security. In this paper, we systematically review over thirty methods for password guessing published between 2016 and 2023. First, we introduce a taxonomy for classifying the existing methods into trawling guessing and targeted guessing. Second, we present an extensive benchmark dataset that can assist researchers and practitioners in successive works. Third, we conduct a bibliometric analysis to present trends in this field and cross-citation between reviewed papers. Further, we discuss the open challenges of password guessing in terms of diverse application scenarios, guessing efficiency, and the combination of traditional and deep learning methods. Finally, this review presents future research directions to guide successive research and development of password guessing.

Recently, text style transfer has become a very hot research topic in the field of natural language processing. However, the conventional text style transfer is unidirectional, and it is not possible to obtain a model with multidirectional transformations through training once. To address this limitation, we propose a new task called multidirectional text style transfer. It aims to use a single model to transfer the underlying style of text among multiple style attributes and keep its main content unchanged. In this paper, we propose Unified Generative Adversarial Networks (UGAN), a practical approach that combines target vector and generative adversarial techniques to perform multidirectional text style transfer. Our model allows simultaneous training of multi-attribute data on a single network. Such unified structure makes our model more efficient and flexible than existing approaches. We demonstrate the superiority of our approach on three benchmark datasets. Experimental results show that our method not only outperforms other baselines, but also reduces training time by an average of 13%.

Accurate identification and classification of Darknet traffic is a critical technical challenge for network security supervision. Existing methods predominantly adopt single-modal features and independent classification strategies, making it difficult to effectively handle the hierarchical structural characteristics and complex encryption patterns of Darknet traffic. This paper proposes E2E-MDC (End-to-End Multi-modal Darknet Classification), an end-to-end deep learning framework based on conditional hierarchical mechanism for three-level hierarchical classification of Darknet traffic. The framework integrates four complementary feature extractors—byte-level CNN, packet sequence TCN, bidirectional LSTM, and Transformer—to comprehensively capture traffic patterns from multiple perspectives. A soft conditional hierarchical classification architecture explicitly models dependencies among Level 1 (Darknet type), Level 2 (application category), and Level 3 (specific behavior) by using upper-level prediction probability distributions as conditional input for lower-level classification. On the self-collected Tor dataset containing 8 applications and 8 behavior types, the system achieves 94.90% cascade accuracy, with Level 3 fine-grained classification accuracy reaching 95.02%. On the public Darknet-2020 dataset, cascade accuracy reaches 92.65%, representing improvements of 12% and 15% over existing state-of-the-art methods, respectively, while reducing hierarchical violation rate to below 0.8%. Experimental results demonstrate that the conditional hierarchical mechanism and multi-modal fusion strategy significantly enhance the accuracy and robustness of Darknet traffic classification, providing effective technical support for network security protection.