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With the recent intensification of geopolitical tensions, cyber-attacks have become increasingly sophisticated and dynamic. Traditional machine learning-based anomaly detection techniques, which rely on pre-trained data, often suffer from performance degradation when exposed to novel attack types not seen during training. To address this limitation, this study proposes a continual learning-based anomaly detection framework capable of incrementally incorporating new attack patterns without forgetting previously learned information. The proposed method consists of three key stages: first, preprocessing and data augmentation are applied to construct high-quality, balanced datasets; second, a base anomaly detection model is trained; and third, new attack data are incrementally integrated to continuously update and evaluate the model. To enhance adaptability and efficiency, the framework incorporates Memory-LGBM, a lightweight architecture that combines a prototype-based memory module with a gradient-free LGBM classifier. The model maintains class-wise latent representations instead of raw samples, enabling compact, memory-efficient learning. Experimental results on the CICIDS 2017 dataset demonstrate that the proposed approach outperforms existing continual learning methods in accuracy, adaptability, and resistance to forgetting, making it a practical and scalable solution for real-world anomaly detection scenarios that demand rapid adaptation, strong knowledge retention, and low computational overhead.

It is essential to build a practical environment of the training/test site for cyber training and weapon system test evaluation. In a military environment, cyber training sites should be continuously developed according to the characteristics of the military. Weapons with cyber security capabilities should be deployed through cyber security certification. Recently, each military has been building its own cyber range that simulates its battlefield environment. However, since the actual battlefield is an integrated operation environment, the cyber range built does not reflect the integrated battlefield environment that is interconnected. This paper proposes a configuration plan and operation function to construct a multi-cyber range reflecting the characteristics of each military to overcome this situation. In order to test the multi-cyber range, which has scenario authoring and operation functions, and can faithfully reflect reality, the impact of DDoS attacks is tested. It is a key to real-world mission-based test evaluation to ensure interoperability between military systems. As a result of the experiment, it was concluded that if a DDoS attack occurs due to the infiltration of malicious code into the military network, it may have a serious impact on securing message interoperability between systems in the military network. Cyber range construction technology is being developed not only in the military, but also in school education and businesses. The proposed technology can also be applied to the construction of cyber ranges in industries where cyber-physical systems are emphasized. In addition, it is a field that is continuously developing with the development of technology, such as being applied as an experimental site for learning machine learning systems.

North Korean cyber-attack groups such as Kimsuky, Lazarus, Andariel, and Venus 121 continue to attempt spear-phishing APT attacks that exploit social issues, including COVID-19. Thus, along with the worldwide pandemic of COVID-19, related threats also persist in cyberspace. In January 2022, a hacking attack, presumed to be Kimsuky, a North Korean cyber-attack group, intending to steal research data related to COVID-19. The problem is that the activities of cyber-attack groups are continuously increasing, and it is difficult to accurately identify cyber-attack groups and attack origins only with limited analysis information. To solve this problem, it is necessary to expand the scope of data analysis by using BGP archive data. It is necessary to combine infrastructure and network information to draw correlations and to be able to classify infrastructure by attack group very accurately. Network-based infrastructure analysis is required in the fragmentary host area, such as malware or system logs. This paper studied cyber ISR and BGP and a case study of cyber ISR visualization for situational awareness, hacking trends of North Korean cyber-attack groups, and cyber-attack tracking. Through related research, we estimated the origin of the attack by analyzing hacking cases through cyber intelligence-based profiling techniques and correlation analysis using BGP archive data. Based on the analysis results, we propose an implementation of the cyber ISR visualization method based on BGP archive data. Future research will include a connection with research on a cyber command-and-control system, a study on the cyber battlefield area, cyber ISR, and a traceback visualization model for the origin of the attack. The final R&D goal is to develop an AI-based cyber-attack group automatic identification and attack-origin tracking platform by analyzing cyber-attack behavior and infrastructure lifecycle.

With the advancement of IT technology, intelligent devices such as autonomous vehicles, unmanned equipment, and drones are rapidly evolving. Consequently, the proliferation of defense systems based on these technologies is increasing worldwide. In response, the U.S. Department of Defense is implementing the RMF (Risk Management Framework) to ensure the cybersecurity of defense systems and conducting cybersecurity T&E (test and evaluation) concurrently. However, RMF and cybersecurity T&E conducted during the acquisition phase of defense systems often result in fragmented cybersecurity assessments, excluding the operational environment of the defense systems. This omission fails to account for the complex network integration, data exchange functionalities, and mission-specific requirements in actual cyber attack scenarios. For these reasons, vulnerabilities in defense systems that remain unidentified during the acquisition phase can potentially pose significant cybersecurity threats during operational phases, necessitating substantial costs and efforts for remediation. Therefore, this paper proposes a mission-based cybersecurity T&E model using a Multi-Cyber Range to effectively apply these two systems in a practical manner. The Multi-Cyber Range integrates independently operated cyber ranges into a network to expand the evaluation environment, which better reflects the mission environment of defense systems. The proposed model’s effectiveness is validated using a cyber attack simulation system targeting a virtualized arbitrary defense system. This paper not only presents an enhanced model for mission-based cybersecurity T&E, but also contributes to the advancement of cybersecurity T&E methodologies by providing a concrete application process.

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Simultaneous imaging of various facets of intact biological systems across multiple spatiotemporal scales is a long-standing goal in biology and medicine, for which progress is hindered by limits of conventional imaging modalities. Here we propose using the refractive index (RI), an intrinsic quantity governing light–matter interaction, as a means for such measurement. We show that major endogenous subcellular structures, which are conventionally accessed via exogenous fluorescence labelling, are encoded in three-dimensional (3D) RI tomograms. We decode this information in a data-driven manner, with a deep learning-based model that infers multiple 3D fluorescence tomograms from RI measurements of the corresponding subcellular targets, thereby achieving multiplexed microtomography. This approach, called RI2FL for refractive index to fluorescence, inherits the advantages of both high-specificity fluorescence imaging and label-free RI imaging. Importantly, full 3D modelling of absolute and unbiased RI improves generalization, such that the approach is applicable to a broad range of new samples without retraining to facilitate immediate applicability. The performance, reliability and scalability of this technology are extensively characterized, and its various applications within single-cell profiling at unprecedented scales (which can generate new experimentally testable hypotheses) are demonstrated. Jo et al. develop a broadly applicable deep-learning approach to predict fluorescence (FL) based on label-free refractive index (RI) measurements, ‘RI2FL’ (RI to FL). The trained model can be used across cell types without retraining.

Optical aberrations significantly impair microscopic image quality across various domains, including cell biology and histopathology diagnostics. Traditional adaptive optics techniques, such as wavefront shaping and guide star utilization, face challenges, especially in imaging biological tissues. Here, we introduce a computational adaptive optics approach tailored for optically thick samples. Utilizing the tilt-tilt correlation from the optical memory effect, our method detects phase differences in aberrations caused by small tilts in the incident waves. Experimental validation demonstrates our technique’s capacity to enhance imaging of thick human tissues under substantial aberration conditions using a transmission-mode holotomography setup. Remarkably, our approach works robustly against sample movement, which is essential for enhanced imaging accuracy in critical biomedical applications. Optical aberrations can hinder the quality of imaging outputs, affecting research across many disciplines. Here, using the tilt-tilt correlation from the optical memory effect, the authors are able to correct aberrations in challenging conditions, including highly aberrated or moving samples.

Three-dimensional (3D) quantitative phase imaging (QPI) enables long-term label-free tomographic imaging and quantitative analysis of live individual bacteria. However, the Brownian motion or motility of bacteria in a liquid medium produces motion artifacts during 3D measurements and hinders precise cell imaging and analysis. Meanwhile, existing cell immobilization methods produce noisy backgrounds and even alter cellular physiology. Here, we introduce a protocol that utilizes hydrogels for high-quality 3D QPI of live bacteria maintaining bacterial physiology. We demonstrate long-term high-resolution quantitative imaging and analysis of individual bacteria, including measuring the biophysical parameters of bacteria and responses to antibiotic treatments.

A major challenge in three-dimensional (3D) microscopy is to obtain accurate spatial information while simultaneously keeping the microscopic samples in their native states. In conventional 3D microscopy, axial resolution is inferior to spatial resolution due to the inaccessibility to side scattering signals. In this study, we demonstrate the isotropic microtomography of free-floating samples by optically rotating a sample. Contrary to previous approaches using optical tweezers with multiple foci which are only applicable to simple shapes, we exploited 3D structured light traps that can stably rotate freestanding complex-shaped microscopic specimens, and side scattering information is measured at various sample orientations to achieve isotropic resolution. The proposed method yields an isotropic resolution of 230 nm and captures structural details of colloidal multimers and live red blood cells, which are inaccessible using conventional tomographic microscopy. We envision that the proposed approach can be deployed for solving diverse imaging problems that are beyond the examples shown here.A general method for the in-situ isotropic microtomography of freestanding specimens, exploiting complex wavefront shaping and optical tweezers.

Cells compartmentalize biomolecules in membraneless structures called biomolecular condensates. While their roles in regulating cellular processes are increasingly understood, tools for their synthetic manipulation remain limited. Here, we introduce RELISR (Reversible Light-Induced Store and Release), an optogenetic condensate system that enables reversible storage and release of proteins or mRNAs. RELISR integrates multivalent scaffolds, optogenetic switches, and cargo-binding domains to trap cargo in the dark and release it upon blue-light exposure. We demonstrate its utility in primary neurons and show that light-triggered release of signaling proteins can modulate fibroblast morphology. Furthermore, light-induced release of cargo mRNA results in protein translation both in vitro and in live mice. RELISR thus provides a versatile platform for spatiotemporal control of protein activity and mRNA translation in complex biological systems, with broad potential for research and therapeutic applications. Spatiotemporal control of biomolecules is a key challenge in cell biology. Here, authors introduce RELISR, an optogenetic condensate system enabling reversible, light-dependent sequestration and release of proteins or mRNA in cells and live animals.