Six new species of a new genus of the Parabathynellidae are described from the Kimberley Region, Western Australia. The erection of the new genus, Kimberleybathynella gen. nov., is based on the twosegmented antenna, the partial fusion of the second and third segments of the maxilla, and the setal condition of the uropodal exopodite, where the outer seta is longer than the inner one. The onesegmented exopodite of thoracopods I-VII and hemispherical form of male thoracopod VIII suggest the close relationship of Kimberleybathynella to the genus Atopobathynella.

As communication technology advances, various and heterogeneous data are communicated in distributed environments through network systems. Meanwhile, along with the development of communication technology, the attack surface has expanded, and concerns regarding network security have increased. Accordingly, to deal with potential threats, research on Network Intrusion Detection Systems (NIDS) has been actively conducted. Among the various NIDS technologies, recently interest is focused on artificial intelligence(AI)-based anomaly detection systems, and various models have been proposed to improve the performance of NIDS. However, there still exists the problem of data imbalance, in which AI models cannot sufficiently learn malicious behavior and thus fail to detect network threats accurately. In this study, we propose a novel AI-based network intrusion detection system that can efficiently resolve the data imbalance problem and improve the performance of the previous systems. To address the aforementioned problem, we leveraged a state-of-the-art generative model that could generate plausible synthetic data for minor attack traffic. In particular, we focused on the reconstruction error and Wasserstein distancebased generative adversarial networks, and autoencoder-driven deep learning models. To demonstrate the effectiveness of our system, we performed comprehensive evaluations over various datasets and demonstrated that the proposed systems significantly outperformed the previous AI-based NIDS.

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With the increased complexity of man‐made systems and software and the higher quality R&D results that are expected in today's R&D environment, it has become increasingly necessary to effectively define and improve the R&D standard processes specifically tailored for our organization and projects. To meet these needs, we have defined R&D standard processes based on international standards for systems and software engineering and on our existing traditional work methods, and then applied them to our ongoing projects. Now, we are improving our defined R&D standard processes continuously by monitoring of our processes and products and checking feedback from application results. In this paper, we introduce the R&D standard processes developed by our organization, the E Research Institute in Korea, and define the requirements and approaches used for their development and improvement. We compare our defined R&D standard processes with ISO/IEC 15288, which is the international standards for systems engineering, and summarize what we have learned through the development and improvement of our processes. © 2006 Wiley Periodicals, Inc. Syst Eng 9: 35–44, 2006

Arisubathynella gen. nov. is established for a new species, A. cheongmiensis sp. nov. from South Korea. An illustrated description is given. The new genus stands out in the family Parabathynellidae by six-segmented antennules, three-segmented antenna without medial seta on inner margin of third segment, absence of mandibulartooth of the ventral edge, bare proximal segment of maxilla, two-segmented exopods of thoracopods II-VII, and male thoracopod VIII with elongated external lobe. Arisubathynella gen. nov. is the fourth genus known from South Korea and foreshadows the great diversity of bathynellaceans in the Far East.