• Journal of the Korea Institute of Information Security & Cryptology
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• v.20 no.2
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• pp.73-90
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• 2010

Recently, Tan et al. introduced a serverless search protocol in which a mobile reader maintains a tag authentication list and authenticates a tag using the list without connecting authentication server. A serverless RFID system is different from general RFID systems which use on-line server models. In the serverless RFID system, since the mobility of a personalized reader must be considered, we have to protect not only the privacy of a tag but also the privacy of a mobile reader. In this paper, we define new security requirements for serverless RFID search system and propose a secure serverless RFID search system. In our system, since tag authentication information maintained by a reader is updated in every session, we can provide the backward untraceability of a mobile reader. Also we use an encrypted timestamp to block a replay attack which is major weakness of search protocols. In addition, we define a new adversary model to analyze a serverless RFID search system and prove the security of our proposed system using the model.

• Structural Engineering and Mechanics
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• v.77 no.4
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• pp.441-461
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• 2021

Reinforced concrete (RC) columns are crucial in building structures and they are of higher vulnerability to terrorist threat than any other structural elements. Thus it is of great interest and necessity to achieve a comprehensive understanding of the possible responses of RC columns when exposed to high intensive blast loads. The primary objective of this study is to derive analytical formulas to assess vulnerability of RC columns using an advanced numerical modelling approach. This investigation is necessary as the effect of blast loads would be minimal to the RC structure if the explosive charge is located at the safe standoff distance from the main columns in the building and therefore minimizes the chance of disastrous collapse of the RC columns. In the current research, finite element model is developed for RC columns using LS-DYNA program that includes a comprehensive discussion of the material models, element formulation, boundary condition and loading methods. Numerical model is validated to aid in the study of RC column testing against the explosion field test results. Residual capacity of RC column is selected as damage criteria. Intensive investigations using Arbitrary Lagrangian Eulerian (ALE) methodology are then implemented to evaluate the influence of scaled distance, column dimension, concrete and steel reinforcement properties and axial load index on the vulnerability of RC columns. The generated empirical formulae can be used by the designers to predict a damage degree of new column design when consider explosive loads. With an extensive knowledge on the vulnerability assessment of RC structures under blast explosion, advancement to the convention design of structural elements can be achieved to improve the column survivability, while reducing the lethality of explosive attack and in turn providing a safer environment for the public.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.2
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• pp.201-211
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• 2022

According to the recent change in the cybersecurity paradigm, research on anomaly detection methods using machine learning and deep learning techniques, which are AI implementation technologies, is increasing. In this study, a comparative study on data preprocessing techniques that can improve the anomaly detection performance of a GRU (Gated Recurrent Unit) neural network-based intrusion detection model using NGIDS-DS (Next Generation IDS Dataset), an open dataset, was conducted. In addition, in order to solve the class imbalance problem according to the ratio of normal data and attack data, the detection performance according to the oversampling ratio was compared and analyzed using the oversampling technique applied with DCGAN (Deep Convolutional Generative Adversarial Networks). As a result of the experiment, the method preprocessed using the Doc2Vec algorithm for system call feature and process execution path feature showed good performance, and in the case of oversampling performance, when DCGAN was used, improved detection performance was shown.

• Advances in aircraft and spacecraft science
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• v.10 no.2
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• pp.107-125
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• 2023

Drag reduction is significant research in aircraft design due to its effect on the cost of operation and carbon footprint reduction. Aircraft currently use conventional solid winglets to reduce the induced drag, adding extra structural weight. Fluidic on-demand winglets can effectively reduce drag for low-speed flight regimes without adding any extra weight. These utilize the spanwise airflow from the wingtips using hydraulic actuators to create jets that negate tip vortices. This study develops a computational model to investigate fluidic on-demand winglets. The well-validated computational model is applied to investigate the effect of injection velocity and angle on the aerodynamic coefficients of a rectangular wing. Further, the turbulence parameters such as turbulent kinetic energy (TKE) and turbulent dissipation rate are studied in detail at various velocity injections and at an angle of 30°. The results show that the increase in injection velocity shifted the vortex core away from the wing tip and the increase in injection angle shifted the vortex core in the vertical direction. Further, it was found that a 30° injection is efficient among all injection velocities and highly efficient at a velocity ratio of 3. This technology can be adopted in any aircraft, effectively working at various angles of attack. The culmination of this study is that the implementation of fluidic winglets leads to a significant reduction in drag at low speeds for low aspect ratio wings.

• International Journal of Computer Science & Network Security
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• v.23 no.4
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• pp.95-102
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• 2023

The emergence of Internet of Things (IoT) into our daily lives has grown rapidly. It's been integrated to our homes, cars, and cities, increasing the intelligence of devices involved in communications. Enormous amount of data is exchanged over smart devices through the internet, which raises security concerns in regards of privacy evasion. This paper is focused on the forensics and intrusion detection on one of the most common protocols in IoT environments, especially smart home environments, which is the Message Queuing Telemetry Transport (MQTT) protocol. The paper covers general IoT infrastructure, MQTT protocol and attacks conducted on it, and multiple network forensics frameworks in smart homes. Furthermore, a machine learning model is developed and tested to detect several types of attacks in an IoT network. A forensics tool (MQTTracker) is proposed to contribute to the investigation of MQTT protocol in order to provide a safer technological future in the warmth of people's homes. The MQTT-IOT-IDS2020 dataset is used to train the machine learning model. In addition, different attack detection algorithms are compared to ensure the suitable algorithm is chosen to perform accurate classification of attacks within MQTT traffic.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.99-109
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• 2023

The darknet is frequently referred to as the hub of illicit online activity. In order to keep track of real-time applications and activities taking place on Darknet, traffic on that network must be analysed. It is without a doubt important to recognise network traffic tied to an unused Internet address in order to spot and investigate malicious online activity. Any observed network traffic is the result of mis-configuration from faked source addresses and another methods that monitor the unused space address because there are no genuine devices or hosts in an unused address block. Digital systems can now detect and identify darknet activity on their own thanks to recent advances in artificial intelligence. In this paper, offer a generalised method for deep learning-based detection and classification of darknet traffic. Furthermore, analyse a cutting-edge complicated dataset that contains a lot of information about darknet traffic. Next, examine various feature selection strategies to choose a best attribute for detecting and classifying darknet traffic. For the purpose of identifying threats using network properties acquired from darknet traffic, devised a hybrid deep learning (DL) approach that combines Recurrent Neural Network (RNN) and Bidirectional LSTM (BiLSTM). This probing technique can tell malicious traffic from legitimate traffic. The results show that the suggested strategy works better than the existing ways by producing the highest level of accuracy for categorising darknet traffic using the Black widow optimization algorithm as a feature selection approach and RNN-BiLSTM as a recognition model.

• Journal of Information Technology Services
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• v.22 no.5
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• pp.99-108
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• 2023

As the computerization of hospitals becomes more advanced, security issues regarding data generated from various medical devices within hospitals are gradually increasing. For example, because hospital data contains a variety of personal information, attempts to attack it have been continuously made. In order to safely protect data from external attacks, each hospital has formed an internal team to continuously monitor whether the computer network is safely protected. However, there are limits to how humans can monitor attacks that occur on networks within hospitals in real time. Recently, artificial intelligence models have shown excellent performance in detecting outliers. In this paper, an experiment was conducted to verify how well an artificial intelligence model classifies normal and abnormal data in network traffic data generated from medical devices. There are several models used for outlier detection, but among them, Random Forest and Tabnet were used. Tabnet is a deep learning algorithm related to receive and classify structured data. Two algorithms were trained using open traffic network data, and the classification accuracy of the model was measured using test data. As a result, the random forest algorithm showed a classification accuracy of 93%, and Tapnet showed a classification accuracy of 99%. Therefore, it is expected that most outliers that may occur in a hospital network can be detected using an excellent algorithm such as Tabnet.

• Journal of the Korea institute for structural maintenance and inspection
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• v.12 no.6
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• pp.129-139
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• 2008

Due to uncertainty of numerous variables in durability model, a probalistic approach is increasing. Monte Carlo simulation (Level III method) is an easily accessible method, but requires a lot of repeated operations. This paper evaluated the effectiveness of First Order Second Moment method (Level II method), which is more convenient and time saving method than MCS, to predict the corrosion initiation in harbor concrete structure. Mean Value First Order Second Moment method (MV FOSM) and Advanced First Order Second Moment method (AFOSM) are applied to the error function solution of Fick's second law modeling chloride diffusion. Reliability index and failure probability based on MV FOSM and AFOSM are compared with the results by MCS. The comparison showed that AFOSM and MCS predict the similar reliability index and MV FOSM underestimates the probability of corrosion initiation by chloride attack. Also, the sensitivity of variables in durability model to corrosion initiation probability was evaluated on the basis of AFOSM. The results showed that AFOSM is a simple and efficient method to estimate the probability of corrosion initiation in harbor structures.

• Korean Journal of Construction Engineering and Management
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• v.15 no.5
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• pp.40-48
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• 2014

As CBR(Chemical, Biological, and Radiological) attack increases, the importance of CBR protective facilities is being emphasized. When CBR warfare emerges, a task force team, who exist outside of CBR protective facility, should enter the CBR protective facility through neutralizing process in CCA(Contamination Control Area) and TFA(Toxic Free Area). If a bottleneck occurs in the process or zones, the task force team cannot enter the CBR protective facility efficiently and may cause inefficiency in its operation performance or result in casualties. The current design criteria of the CBR protective facility is only limited to ventilation system and it does not consider how much time it takes to enter the facility. Therefore, this research aims to propose the entering time estimation model with discrete event simulation. To make the simulation model, the procedure performed through CCA and TFA is defined and segmented. The actual time of the procedure are measured and adapted for the simulation model. After running the simulation model, variables effecting the entering time are selected for alternatives with adjustments. This entering time estimation model for CBR protective facility is expected to help take time into consideration during the designing phase of CBR protective facility and help CBR protective facility managers to plan facility operation in a more realistic approach.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.46 no.1
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• pp.41-51
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• 2018

Downwash from helicopter rotor blades and external winds from various maneuvering make an unguided rocket change its trajectory and range. For the prediction of the trajectory and range, it is essential to consider the downwash effect. In this study, an algorithm was developed to calculate 6-Degree-Of-Freedom(6 DOF) forces and moments exerting on the rocket, and total flight trajectory of a 2.75-inch unguided rocket in a helicopter downwash flow field. Using Actuator Disk Model(ADM) analysis result, the algorithm could analyze the entire trajectory in various initial launch condition such as launch angle, launch velocity, and external wind. The algorithm that considered the interference between a fuselage and external winds could predict the trajectory change more precisely than inflow model analysis. Using the developed algorithm, the attitude and trajectory change mechanism by the downwash effect were investigated analyzing the effective angle of attack change and characteristics of pitching stability of the unguided rocket. Also, the trajectory and range changes were analyzed by considering the downwash effect with external winds. As a result, it was concluded that the key factors of the rocket range change were downwash area and magnitude which effect on the rocket, and the secondary factors were the dynamic pressure of the rocket and the interference between a fuselage and external winds. In tailwind case which was much influential on the range characteristics than other wind cases, the range of the rocket rose as increasing the tailwind velocity. However, there was a limit that the range of the rocket did not increase more than the specific tailwind velocity.