Machine learning (ML)–based intrusion detection systems (IDS) often degrade when deployed across heterogeneous networks due to domain shifts in traffic and configuration. To mitigate this degradation, conventional domain adaptation (DA) methods aim to align source and target data distributions; however, they require access to source data during deployment—an impractical constraint that undermines scalability and reusability. To overcome this limitation, we propose TRANSFA-IDS (Transformer Source-Free Adaptation for IDS), which removes the need for source data during adaptation while preserving the knowledge encoded in the source-trained model. TRANSFA-IDS transforms tabular flow records into structured color image embeddings and employs a compact Vision Transformer with a Deep Support Vector Data Description (Deep-SVDD) head to learn domain-invariant representations of benign behavior. During deployment, it adapts to new environments using only a small portion of unlabeled target traffic by fine-tuning the last Transformer block, efficiently realigning feature distributions without retraining. Experiments across cross-dataset settings (CICIDS2018↔UNSW-NB15) show that TRANSFA-IDS achieves AUROC scores up to 0.908 and 0.873, outperforming traditional non-adaptive unsupervised baselines by over 40% while adapting more than twice as fast as conventional adaptive unsupervised. These results demonstrate that source-free adaptation can deliver both high accuracy and deployment practicality for scalable IDS across diverse network environments.

This live demonstration shows a low-cost but high-performance hardware IP for video stream encryption and authentication on real-time monitoring system. The proposed IP is essentially based on the established standard of lightweight cryptography (LWC) by NIST in 2023, which is called Ascon cipher. For maintaining the frame rate of the video, a high-speed and low-cost hardware IP is proposed with a step-by-step setup procedure introduced in this brief. Given that the recursive permutation of Ascon algorithm, the cost of the proposed Ascon cipher IP can be sustainably reduced by round-based design. The video stream encryption system is realized on Xilinx PYNQ-Z2 board with some peripheral IPs embedded in that FPGA board. Implementation results show the designed IP and the SoC system can perform better than the previous work.

In recent years, cloud computing has gradually become the mainstream of Internet services. When cloud computing environments become more perfect, the business and user will be an enormous amount of data stored in the remote cloud storage devices, hoping to achieve random access, data collection, reduce costs, facilitate the sharing of other services. However, when the data is stored in the cloud storage device, a long time, enterprises and users inevitably will have security concerns, fearing that the information is actually stored in the cloud is still in the storage device or too long without access to, has long been the cloud server removed or destroyed, resulting in businesses and users in the future can't access or restore the data files. Therefore, this scheme goal to research and design for data storage cloud computing environments that are proved. Stored in the cloud for data storage, research and develop a security and efficient storage of proof protocol, also can delegate or authorize others to public verifiability whether the data actually stored in the cloud storage devices.

Park et al. (see Proceedings of EuroCrypto '93, p.T131, 1993) previously proposed an efficient key distribution scheme for digital mobile communications. Two attacks are given in this paper to show that their scheme is not secure enough for session key distributions. A modified protocol is proposed to avoid these weaknesses.