Jongkwan Lee

Unmanned Aerial Vehicles (UAVs) increasingly rely on fog-assisted tactical networks and must roam across multiple fog nodes (FNs) under disrupted, intermittent, and limited (DIL) connectivity. Roaming authentication in this setting faces stringent latency constraints, partial trust in FNs, and global state inconsistencies (e.g., stale or revoked credentials). This paper proposes a lightweight mutual authentication and session key agreement protocol that decouples a fast off-chain three-message handshake from permissioned-blockchain state anchoring, enabling consistent revocation and key-version enforcement without a centralized authority. Using only hash-based primitives (HMAC/HKDF), the protocol incurs low overhead (138 bytes per session) and is analyzed in a game-based security model. We explicitly define a scope-aware boundary: confidentiality is not claimed under exposure of an active FN masking key together with access to on-chain masked records; under compromise, the goal is to enforce revocation/version consistency and bound the exposure window via rotation. A trace-driven tail-sensitivity evaluation under Normal and DIL conditions shows stable roaming responsiveness, supporting practical deployment in tactical UAV networks.

Unmanned aerial vehicle (UAV) enabled integrated sensing and communication (ISAC) has become a promising solution in recent wireless networks for delivering joint communication and localization services to users. In this letter, we delve into the UAV deployment scheme for such UAV-assisted ISAC scenario under uncertainty of UAV position. Existing terrestrial base stations (TBS) on ground are utilized to perform positioning of the UAV, which then cooperates with the TBSs to provide ISAC service to users. We formulate the problem into a two-stage weighted optimization problem balancing out between communication and localization needs. Based on such approach, the optimal UAV location aiming to jointly optimize communication and localization performance is obtained. In the first stage we derive the UAV’s optimal altitude that maximizes accuracy over viable region. Next, we apply an iterative algorithm to find the UAV’s optimal horizontal location satisfying ISAC service needs. Finally, we provide numerical results that validate the superiority of our proposed idea in terms of UAV communication power and user localization accuracy.

The VHF Data Exchange System (VDES) is emerging as a next-generation standard for maritime wireless communication, extending the Automatic Identification System (AIS) and voice services with higher data rates. Although the Secure Communication (SECOM) protocol provides end-to-end protection at the application layer, it does not support link-layer broadcast authentication, which is essential in open maritime environments. This paper proposes a lightweight, delay-tolerant authentication framework for VDES that enables secure broadcast messaging without requiring modifications to the existing infrastructure. The layered framework integrates Timed Efficient Stream Loss-Tolerant Authentication (TESLA) at the link layer with SECOM at the application layer. A systematic parameterization strategy was developed to address maritime-specific constraints, including burst loss, synchronization errors, and vessel mobility. Analytical modeling and MATLAB-based simulations were conducted to evaluate authentication reliability, latency, and resistance to forgery. The proposed TESLA-based scheme achieved high success rates, low delay, and strong robustness against forgery across diverse operating conditions. The recommended configuration (80-bit MAC, T_int = 500 ms, d = 2) demonstrated reliable performance with minimal overhead, thereby ensuring suitability for resource-constrained maritime networks. Overall, this study provides a quantitative baseline for secure broadcast authentication in VDES. The findings offer practical insights for future field trials, prototype VDES transceivers, and international standardization led by ITU-R and IALA.

This paper presents a novel dynamic slot allocation algorithm for wireless sensor networks designed to overcome resource constraints. Our algorithm operates without explicit request messages and adapts the transmission period and data transmission amount according to changes in the monitored environment. As such, it addresses the challenge of allocating wireless resources efficiently and fairly while considering packet drop rate, transmission delay, channel status, and transmission opportunities. However, networks with limited resources can only collect and process some of the necessary information for optimal allocation. Therefore, our proposed scheme uses accumulative information on allocated resources instead of additional overhead information. Our algorithm outperforms the reservation-based resource allocation scheme in conditions where packet arrival rates change dynamically, which achieves over 15% improvement in terms of network throughput, indicating its suitability for networks operating under constrained resources.

미래 전장환경의 주요 구성요소인 자율 무인체계들은 군집화되어 팀 단위로 활동하고 작전 목적 및 작전 상황에 따라 팀 구성은 매우 유동적일 것이다. 즉, 독립된 작전에 투입되어 운영되던 무인체계 팀들이 하나의 팀으로 통합되거나 하나의 무인체계 팀이 두 개 이상의 팀으로 분할될 수 있다. 새로운 팀으로 재편될 때마다 안전한 인증절차가 필요하다. 무인체계는 인간 전투원과 달리 종합적인 상황판단을 통한 인증이 제한되기 때문에 적 무인체계에 의한 기만적 활동에 취약할 수 있다. 본 논문은 미래 전장에서 안전한 무인체계 활용을 위한 경량화된 인증 기법을 제안한다. 제안하는 기법에서 무인체계는 개별 고유식별번호와 CRT(Chinese Remainder Theorem)를 통해 같은 팀에 소속된 무인체계들은 동일한 정수를 추출하고, 추출한 정수만큼 해시 연산을 반복하여 암호키를 개별적으로 생성한다. 그리고 수신된 암호화 메시지를 복호화함으로써 수신 메시지가 아군으로부터 수신된 것임을 인증한다. 제안하는 기법의 성능분석을 통해 제안하는 기법이 메시지 재전송 공격, 위장 공격, 메시지 변형 공격 등에 안전하고 기존 유사 기법에 비해 적은 연산량이 요구됨을 확인하였다.

This paper proposes a blockchain system with a consensus algorithm and timeslot allocation policy for TDMA-based tactical wireless networks with constrained resources. A blockchain can serve as a distributed database that ensures all nodes can store the same data even in unreliable network environments. Blockchain technology has numerous benefits, such as decentralization, tamper resistance, traceability, and audibility. Due to these characteristics, the application range of blockchains has dramatically expanded from cryptocurrency, financial services, risk management, and IoT to various services in both public and non-public domains. In addition, there has been an increasing demand for using blockchain technology in wireless network environments. Unlike wired networks, communication resources in wireless networks for data transmission and reception are relatively limited; as such, it is necessary to design a consensus algorithm with low resource consumption and an efficient resource allocation policy for these environments. The proposed system allocates timeslots to nodes based on their contribution to blockchain management, which prevents nodes from participating in an unnecessary competition for block generation, thereby preventing the associated waste of energy. We could verify the proposed system’s practicality through numerical analysis and computer simulations under various conditions.

In this paper, we propose an opportunistic data relay scheme in narrowband multihop combat radio networks. Narrowband networks have physical restrictions on high-speed transmission. Furthermore, the topology changes dynamically due to the jamming of the enemy, signal interference between friendly forces, and movement of network entities. Therefore, the traditional relay scheme that collects topology information and calculates a relay path before transmission is unsuitable for such networks. Our proposed scheme does not collect topology information and transmits data opportunistically. The scheme can cause unnecessary data relaying that is not related to data delivery to the destination node. However, for small networks, the effect of increasing network throughput by not gathering topology information is much greater than the effect of reducing throughput by unnecessary data relays. We demonstrate the performance superiority of the proposed scheme through simulation in the worst case of network topology.

2020년 초 데이터 3법이 국회를 통과하면서 4차 산업혁명 시대의 핵심 자원인 데이터를 활용할 수 있는 법적 근거가 마련 되었고, 개인정보에 대한 활용 및 연구가 활성화됨에 따라 개인정보의 관리가 더욱 중요해졌다. 이와 관련해 신원 관리의 개 념도 과거 개별신원 모델, 연합신원 모델을 거쳐 이제는 개인의 정보를 자신이 직접 관리할 수 있는 자기주권신원 (Self-Sovereign Identity, SSI) 모델로 진화하고 있다. 최근 민간분야에서는 블록체인 기반의 분산신원증명(Distributed Identity, DID) 기술의 개발과 활용이 두드러지게 늘어나고 있으나 상대적으로 군에서는 DID 도입 및 활용이 미진한 상태이 다. 본 논문에서는 사설 블록체인 기반의 국방 DID 기술을 개발하고, 개발된 국방 DID 기술을 활용한 군부대 출입통제체계 구축방안을 제안한다. 또한, 국방 DID 생태계 구축 및 확장을 통해 민·관·군 DID 서비스 확대방안을 제안한다.

In this paper, we propose a classification system that uses multiple autoencoder models for identifying malware images. It is crucial to accurately classify malware before we can deploy appropriate countermeasures to prevent them from spreading. Rapid malware classification is the first step in preparing effective countermeasures. Typical approaches to this problem, which can be divided into static or dynamic methods, are not suitable for efficient malware classification because they require either fixed malware patterns or lots of time to investigate, respectively. If the malware analysts have enough time and resources, they can analyze any malware thoroughly. However, finite resources mean they always suffer from a lack of time due to the malware that needs analyzing increasing at a dramatic rate. In the real world, new malware and variants of existing malware are constantly emerging. To address this issue, many researchers have developed approaches using machine learning techniques. However, to date these systems have had difficulty responding appropriately to the rapidly changing malware environment and also suffer from data imbalance problems in the training data. The system proposed in this paper consists of multiple autoencoder models that classify malware that has been converted to an image. Each autoencoder model classifies only one type of malware and is trained using only samples from the corresponding family, this allows the system to update quickly and mitigates the data imbalance problem. We demonstrate our method’s superior performance through various experiments compared to other state-of-the-art techniques using the Malimg dataset.

본 논문은 컴퓨팅 및 네트워크 자원이 제한적인 단말에서 딥러닝 서비스를 효과적으로 활용하기 위한 직교 특징벡터 추출기법을 제안한다. 단말에서 딥러닝 서비스를 이용하기 위한 방안 중 하나는 분석하고자 하는 데이터를 원격 서버에 전송하여 서비스를 이용하는 것이다. 이러한 방안은 대용량 컴퓨팅 자원이 필요하지 않을 수 있다. 하지만 전송하는 데이터의 크기가 크다면 원격서버와의 통신을 위한 자원이 많이 필요하다. 또한 원격 서버가 물리적으로 멀리 이격되어 있는 경우에는 과도한 지연시간의 발생으로 원활한 서비스 이용이 제한된다. 따라서 딥러닝 서비스의 성능 저하 없이 단말에서 전송하는 데이터의 크기를 되도록 줄여야 한다. 제안하는 기법은 이러한 필요성을 충족하기 위해 단말에서 원본데이터로부터 저용량의 직교 특징벡터를 다수 추출하고 특징벡터들간의 직교성을 이용하여 전송되는 데이터의 크기를 줄인다. 또한 엣지 서버에서는 다수의 직교 특징벡터들을 이용한 앙상블 모델을 통해 딥러닝 서비스의 성능을 향상시킨다. Fashion-MNIST 데이터셋 대상으로 실험한 결과, 제안하는 기법이 전송데이터의 크기는 줄이면서 딥러닝 서비스의 성능을 향상시킬 수 있음을 확인하였다.