Quantum-secured networking constitutes a layering architecture comprising a user network and a quantum key distribution network (QKDN). In the wavelength division multiplexing-based quantum-secured optical networking, wavelength and quantum key are scarce resources in the user network and QKDN, respectively; hence, an effective resource dimensioning algorithm is required for practical quantum-secured services. Therefore, we investigated a resource dimensioning problem for the standardization-compatible quantum-secured networking model, where a data routing path in the user network and a quantum key relay path in the QKDN are interdependent. As benchmarking solutions, we first developed integer linear programming (ILP) formulations for wavelength resource and quantum key resource optimal quantum-secured networking. To obtain the tractable solution, an iterative heuristic algorithm was proposed, in which a path of a quantum-secured service was calculated by considering the statuses of both wavelength and quantum key resources. Simulation study on practical backbone topologies revealed that the proposed heuristic algorithm reduced manifold order-of-magnitude in the algorithm computation time at the costs of acceptable overheads in the wavelength and quantum key resources, from those of the ILP optimizations.

In order to effectively utilize the costly quantum resource in the practical quantum-secured networking, we proposed lifetime-aware quantum key relay algorithm. The proposed algorithm calculates an end-to-end quantum key relay path, based on the combination of the lifetime and the number of quantum keys of links. In the practical condition, the proposed algorithm reduces 21% of blocking count of quantum-secured service, from that of the conventional key relay algorithm.

Currently, data security is one of the most concerning research topics. The traditional RSA encryption system has become vulnerable to quantum algorithms such as Grover and Shor, leading to the development of new security systems for the quantum. As a result, quantum cryptography is gaining importance as a key element of future communication security. This study focuses on quantum key distribution protocols for data quantum encryption, aiming to achieve quantum robustness in all stages of quantum cryptography communication processes. Quantum cryptography communication requires robust quantum encryption not only between end-nodes but also between all components. Therefore, this study demonstrates the process of end-to-end data quantum encryption and proves the overall quantum robustness in this process.

To fix network congestion resulting from the increase in high volume traffic in data-intensive science and the increase in internet traffic due to COVID-19, there has been a necessity of traffic engineering through traffic prediction. For this, there have been various attempts from a statistical method such as ARIMA to machine learning including LSTM and GRU. This study aimed to collect and learn KREOENT backbone and subscribers’ traffic volume through diverse machine learning techniques (e.g., SVR, LSTM, GRU, etc.) and predict maximum traffic on the following day.

As it has been recently proven that the public key-based RSA algorithms that are currently used in encryption can be unlocked by Shor’s algorithm of quantum computers in a short time, conventional security systems are facing new threats, and accordingly, studies have been actively conducted on new security systems. They are classified into two typical methods: Post Quantum Cryptography (PQC) and Quantum Key Distribution (QKD). PQC aims to design conventional cryptography systems in a more robust way so that they will not be decrypted by a quantum computer in a short time whereas QKD aims to make data tapping and interception physically impossible by using quantum mechanical characteristics. In this paper, we design a quantum key management system, which is most crucial for constructing a QKD network and analyze the design requirements to apply them to Korea Research Environment Open NETwork (KREONET). The quantum key management system not only manages the lifecycle, such as storage, management, derivation, allocation, and deletion of the symmetric key generated in QKD but also enables many-to-many communication in QKD communication based on the key relay function and P2P communication to overcome the limitation of distance, which is a disadvantage of QKD. We have validated the designed quantum key management system through simulations to supplement the parts that were not considered during the initial design.

The nature of quantum mechanics provides us with an opportunity to statistically detect eavesdropping in quantum key distribution (QKD) protocols, which is unimaginable in classical digital communications. By utilizing Hoeffding’s inequality, this study analyzes the upper bounds of the false-positive ratio (FPR) and false-negative ratio (FNR) of eavesdropping detection in the Bennett–Brassard-84 (BB84) QKD protocol, where eavesdropping is detected if the measured quantum bit error rate (QBER) is equal to or higher than a threshold. The analysis clarifies the trade-off between the accuracy of eavesdropping detection and the economy of quantum resources in the BB84 protocol. Owing to the central limit theorem, the QBER measured by 300 quantum bits (qubits) is sufficient to guarantee lower than 0.009% of the FPR and FNR of eavesdropping detection. To deal with rapidly varying quantum channel conditions, this study further introduces grouped BB84 protocol and combinatory eavesdropping detection algorithms. A polarization basis is changeable for a group of qubits, and eavesdropping is judged by a combination of criteria between QBER and group-QBER in the proposed protocol and algorithms. In our extensive simulation study, the grouped BB84 protocol with 300 qubits comparison guarantees at least 99.92% accuracy in eavesdropping detection under rapidly varying quantum channel conditions.

In our present technological age, most manual and semi-automated tasks are being automated for efficient productivity or convenience. In particular, industrial sites are rapidly being automated to increase productivity and improve work efficiency. However, while networks are increasingly deployed as an integral part of the automation of industrial processes, there are also many resultant dangers such as security threats, malfunctions, and interruption of industrial processes. In particular, while the security of business networks is reinforced and their information is not easily accessible, intruders are now targeting industrial networks whose security is relatively poor, wherein attacks could directly lead to physical damage. Therefore, numerous studies have been conducted to counter security threats through network traffic monitoring, and to minimize physical loss through the detection of malfunctions. In the case of industrial processes, such as in nuclear facilities and petroleum facilities, thorough monitoring is required as security issues can lead to significant danger to humans and damage to property. Most network traffic in industrial facilities uses proprietary protocols for efficient data transmission, and these protocols are kept confidential because of intellectual property and security reasons. Protocol reverse engineering is a preparatory step to monitor network traffic and achieve more accurate traffic analysis. The field extraction method proposed in this study is a method for identifying the structure of proprietary protocols used in industrial sites. From the extracted fields, the structure of commands and protocols used in the industrial environment can be derived. To evaluate the feasibility of the proposed concept, an experiment was conducted using the Modbus/TCP protocol and Ethernet/IP protocol used in actual industrial sites, and an additional experiment was conducted to examine the results of the analysis of conventional protocols using the file transfer protocol.

The amount of computation and the number of parameters of neural networks are increasing rapidly as the depth of convolutional neural networks (CNNs) is increasing. Therefore, it is very crucial to reduce both the amount of computation and that of memory usage. The pruning method, which compresses a neural network, has been actively studied. Depending on the layer characteristics, the sparsity level of each layer varies significantly after the pruning is conducted. If weights are sparse, most results of convolution operations will be zeroes. Although several studies have proposed methods to utilize the weight sparsity to avoid carrying out meaningless operations, those studies lack consideration that input activations may also have a high sparsity level. The Rectified Linear Unit (ReLU) function is one of the most popular activation functions because it is simple and yet pretty effective. Due to properties of the ReLU function, it is often observed that the input activation sparsity level is high (up to 85%). Therefore, it is important to consider both the input activation sparsity and the weight one to accelerate CNN to minimize carrying out meaningless computation. In this paper, we propose a new acceleration method called Direct Conversion that considers the weight sparsity under the sparse input activation condition. The Direct Conversion method converts a 3D input tensor directly into a compressed format. This method selectively applies one of two different methods: a method called image to Compressed Sparse Row (im2CSR) when input activations are sparse and weights are dense; the other method called image to Compressed Sparse Overlapped Activations (im2CSOA) when both input activations and weights are sparse. Our experimental results show that Direct Conversion improves the inference speed up to 2.82× compared to the conventional method.