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Trend Forecasting and Analysis of Quantum Computer Technology (양자 컴퓨터 기술 트렌드 예측과 분석)

  • Cha, Eunju;Chang, Byeong-Yun
    • Journal of the Korea Society for Simulation
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    • v.31 no.3
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    • pp.35-44
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    • 2022
  • In this study, we analyze and forecast quantum computer technology trends. Previous research has been mainly focused on application fields centered on technology for quantum computer technology trends analysis. Therefore, this paper analyzes important quantum computer technologies and performs future signal detection and prediction, for a more market driven technical analysis and prediction. As analyzing words used in news articles to identify rapidly changing market changes and public interest. This paper extends conference presentation of Cha & Chang (2022). The research is conducted by collecting domestic news articles from 2019 to 2021. First, we organize the main keywords through text mining. Next, we explore future quantum computer technologies through analysis of Term Frequency - Inverse Document Frequency(TF-IDF), Key Issue Map(KIM), and Key Emergence Map (KEM). Finally, the relationship between future technologies and supply and demand is identified through random forests, decision trees, and correlation analysis. As results of the study, the interest in artificial intelligence was the highest in frequency analysis, keyword diffusion and visibility analysis. In terms of cyber-security, the rate of mention in news articles is getting overwhelmingly higher than that of other technologies. Quantum communication, resistant cryptography, and augmented reality also showed a high rate of increase in interest. These results show that the expectation is high for applying trend technology in the market. The results of this study can be applied to identifying areas of interest in the quantum computer market and establishing a response system related to technology investment.

Design of detection method for malicious URL based on Deep Neural Network (뉴럴네트워크 기반에 악성 URL 탐지방법 설계)

  • Kwon, Hyun;Park, Sangjun;Kim, Yongchul
    • Journal of Convergence for Information Technology
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    • v.11 no.5
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    • pp.30-37
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    • 2021
  • Various devices are connected to the Internet, and attacks using the Internet are occurring. Among such attacks, there are attacks that use malicious URLs to make users access to wrong phishing sites or distribute malicious viruses. Therefore, how to detect such malicious URL attacks is one of the important security issues. Among recent deep learning technologies, neural networks are showing good performance in image recognition, speech recognition, and pattern recognition. This neural network can be applied to research that analyzes and detects patterns of malicious URL characteristics. In this paper, performance analysis according to various parameters was performed on a method of detecting malicious URLs using neural networks. In this paper, malicious URL detection performance was analyzed while changing the activation function, learning rate, and neural network structure. The experimental data was crawled by Alexa top 1 million and Whois to build the data, and the machine learning library used TensorFlow. As a result of the experiment, when the number of layers is 4, the learning rate is 0.005, and the number of nodes in each layer is 100, the accuracy of 97.8% and the f1 score of 92.94% are obtained.

A Study on Punitive Damages System in Technology Protection Related Laws: Focusing on Patent Act, TSPA, ITPA, FTSA, MBCA (기술보호 관련 법률에서의 징벌적 손해배상제도에 대한 고찰: 특허법, 영업비밀보호법, 산업기술보호법, 하도급법, 상생협력법을 중심으로)

  • Cho, Yongsun
    • Korean small business review
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    • v.42 no.1
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    • pp.19-41
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    • 2020
  • In Korea, punitive damages were introduced in the 2011 Fair Transactions in Subcontracting Act(FTSA), and in 2019 the Patent Act, Trade Secret Protection Act(TSPA), Industrial Technology Protection Act(ITPA), and Mutually Beneficial Cooperation Act(MBCA). In punitive damages, the judgment of 'intentional' is especially important, and it is necessary to refer to US precedents since there is no accumulated case. Major Company can avoid intentional counseling through the advice of lawyers, but SMEs may have to punish punitive damages due to a lack of awareness of the system. In the case of TSPA, ITPA, FTSA, and MBCA, except for Patent Act, the provisions related to proof of damage have not been well maintained yet. Therefore, the data submission order system of these laws needs to be revised to the level of patent Act need to be. TSPA needs to be amended in the future to estimate the amount of the royalties in estimating the amount of damages so that it can receive the 'reasonably' estimated amount rather than the usual amount. On the other hand, ITPA, FTSA, and MBCA do not have any provisions for the estimation of damages. Besides, it is difficult to evaluate the technology value in the case of leakage or deodorization of new technologies. Therefore, valuation needs to be carried out by a credible institution along with the development of a model for calculating damages.

Static Identification of Firmware Linux Kernel Version by using Symbol Table (심볼 테이블을 이용한 펌웨어 리눅스 커널 버전 정적 식별 기법)

  • Kim, Kwang-jun;Cho, Yeo-jeong;Kim, Yun-jeong;Lee, Man-hee
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.32 no.1
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    • pp.67-75
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    • 2022
  • When acquiring a product having an OS, it is very important to identify the exact kernel version of the OS. This is because the product's administrator needs to keep checking whether a new vulnerability is found in the kernel version. Also, if there is an acquisition requirement for exclusion or inclusion of a specific kernel version, the kernel identification becomes critical to the acquisition decision. In the case of the Linux kernel used in various equipment, sometimes it becomes difficult to pinpoint the device's exact version. The reason is that many manufacturers often modify the kernel to produce their own firmware optimized for their device. Furthermore, if a kernel patch is applied to the modified kernel, it will be very different from its base kernel. Therefore, it is hard to identify the Linux kernel accurately by simple methods such as a specific file existence test. In this paper, we propose a static method to classify a specific kernel version by analyzing function names stored in the symbol table. In an experiment with 100 Linux devices, we correctly identified the Linux kernel version with 99% accuracy.

Cyber attack group classification based on MITRE ATT&CK model (MITRE ATT&CK 모델을 이용한 사이버 공격 그룹 분류)

  • Choi, Chang-hee;Shin, Chan-ho;Shin, Sung-uk
    • Journal of Internet Computing and Services
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    • v.23 no.6
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    • pp.1-13
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    • 2022
  • As the information and communication environment develops, the environment of military facilities is also development remarkably. In proportion to this, cyber threats are also increasing, and in particular, APT attacks, which are difficult to prevent with existing signature-based cyber defense systems, are frequently targeting military and national infrastructure. It is important to identify attack groups for appropriate response, but it is very difficult to identify them due to the nature of cyber attacks conducted in secret using methods such as anti-forensics. In the past, after an attack was detected, a security expert had to perform high-level analysis for a long time based on the large amount of evidence collected to get a clue about the attack group. To solve this problem, in this paper, we proposed an automation technique that can classify an attack group within a short time after detection. In case of APT attacks, compared to general cyber attacks, the number of attacks is small, there is not much known data, and it is designed to bypass signature-based cyber defense techniques. As an attack model, we used MITRE ATT&CK® which modeled many parts of cyber attacks. We design an impact score considering the versatility of the attack techniques and proposed a group similarity score based on this. Experimental results show that the proposed method classified the attack group with a 72.62% probability based on Top-5 accuracy.

DoS/DDoS attacks Detection Algorithm and System using Packet Counting (패킷 카운팅을 이용한 DoS/DDoS 공격 탐지 알고리즘 및 이를 이용한 시스템)

  • Kim, Tae-Won;Jung, Jae-Il;Lee, Joo-Young
    • Journal of the Korea Society for Simulation
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    • v.19 no.4
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    • pp.151-159
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    • 2010
  • Currently, by using the Internet, We can do varius things such as Web surfing, email, on-line shopping, stock trading on your home or office. However, as being out of the concept of security from the beginning, it is the big social issues that malicious user intrudes into the system through the network, on purpose to steal personal information or to paralyze system. In addition, network intrusion by ordinary people using network attack tools is bringing about big worries, so that the need for effective and powerful intrusion detection system becomes very important issue in our Internet environment. However, it is very difficult to prevent this attack perfectly. In this paper we proposed the algorithm for the detection of DoS attacks, and developed attack detection tools. Through learning in a normal state on Step 1, we calculate thresholds, the number of packets that are coming to each port, the median and the average utilization of each port on Step 2. And we propose values to determine how to attack detection on Step 3. By programing proposed attack detection algorithm and by testing the results, we can see that the difference between the median of packet mounts for unit interval and the average utilization of each port number is effective in detecting attacks. Also, without the need to look into the network data, we can easily be implemented by only using the number of packets to detect attacks.

An Evaluation Technique for the Path-following Control Performance of Autonomous Surface Ships (자율운항선박의 항로추정성능 평가기법 개발에 관한 연구)

  • Daejeong Kim;ChunKi Lee;Jeongbin Yim
    • Journal of Navigation and Port Research
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    • v.47 no.1
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    • pp.10-17
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    • 2023
  • A series of studies on the development of autonomous surface ships have been promoted in domestic and foreign countries. One of the main technologies for the development of autonomous ships is path-following control, which is closely related to securing the safety of ships at sea. In this regard, the path-following performance of an autonomous ship should be first evaluated at the design stage. The main aim of this study was to develop a visual and quantitative evaluation method for the path-following control performance of an autonomous ship at the design stage. This evaluation technique was developed using a computational fluid dynamics (CFD)-based path-following control model together with a line-of-sight (LOS) guidance algorithm. CFD software was utilized to visualize waves around the ship, performing path-following control for visual evaluation. In addition, a quantitative evaluation was carried out using the difference between the desired and estimated yaw angles, as well as the distance difference between the planned and estimated trajectories. The results demonstrated that the ship experienced large deviations from the planned path near the waypoints while changing its course. It was also found that the fluid phenomena around the ship could be easily identified by visualizing the flow generated by the ship. It is expected that the evaluation method proposed in this study will contribute to the visual and quantitative evaluation of the path-following performance of autonomous ships at the design stage.

A Study on the Derivation of Port Safety Risk Factors Using by Topic Modeling (토픽모델링을 활용한 항만안전 위험요인 도출에 관한 연구)

  • Lee Jeong-Min;Kim Yul-Seong
    • Journal of Korea Port Economic Association
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    • v.39 no.2
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    • pp.59-76
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    • 2023
  • In this study, we tried to find out port safety from various perspectives through news data that can be easily accessed by the general public and domestic academic journal data that reflects the insights of port researchers. Non-negative Matrix Factorization(NMF) based topic modeling was conducted using Python to derive the main topics for each data, and then semantic analysis was conducted for each topic. The news data mainly derived natural and environmental factors among port safety risk factors, and the academic journal data derived security factors, mechanical factors, human factors, environmental factors, and natural factors. Through this, the need for strategies to strengthen the safety of domestic ports, such as strengthening the resilience of port safety, improve safety awareness to broaden the public's view of port safety, and conduct research to develop the port industry environment into a safe and specialized mature port. As a result, this study identified the main factors to be improved and provided basic data to develop into a mature port with a port safety culture.

Soil Moisture Estimation Using KOMPSAT-3 and KOMPSAT-5 SAR Images and Its Validation: A Case Study of Western Area in Jeju Island (KOMPSAT-3와 KOMPSAT-5 SAR 영상을 이용한 토양수분 산정과 결과 검증: 제주 서부지역 사례 연구)

  • Jihyun Lee;Hayoung Lee;Kwangseob Kim;Kiwon Lee
    • Korean Journal of Remote Sensing
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    • v.39 no.6_1
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    • pp.1185-1193
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    • 2023
  • The increasing interest in soil moisture data from satellite imagery for applications in hydrology, meteorology, and agriculture has led to the development of methods to produce variable-resolution soil moisture maps. Research on accurate soil moisture estimation using satellite imagery is essential for remote sensing applications. The purpose of this study is to generate a soil moisture estimation map for a test area using KOMPSAT-3/3A and KOMPSAT-5 SAR imagery and to quantitatively compare the results with soil moisture data from the Soil Moisture Active Passive (SMAP) mission provided by NASA, with a focus on accuracy validation. In addition, the Korean Environmental Geographic Information Service (EGIS) land cover map was used to determine soil moisture, especially in agricultural and forested regions. The selected test area for this study is the western part of Jeju, South Korea, where input data were available for the soil moisture estimation algorithm based on the Water Cloud Model (WCM). Synthetic Aperture Radar (SAR) imagery from KOMPSAT-5 HV and Sentinel-1 VV were used for soil moisture estimation, while vegetation indices were calculated from the surface reflectance of KOMPSAT-3 imagery. Comparison of the derived soil moisture results with SMAP (L-3) and SMAP (L-4) data by differencing showed a mean difference of 4.13±3.60 p% and 14.24±2.10 p%, respectively, indicating a level of agreement. This research suggests the potential for producing highly accurate and precise soil moisture maps using future South Korean satellite imagery and publicly available data sources, as demonstrated in this study.

A Study on Efficient AI Model Drift Detection Methods for MLOps (MLOps를 위한 효율적인 AI 모델 드리프트 탐지방안 연구)

  • Ye-eun Lee;Tae-jin Lee
    • Journal of Internet Computing and Services
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    • v.24 no.5
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    • pp.17-27
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    • 2023
  • Today, as AI (Artificial Intelligence) technology develops and its practicality increases, it is widely used in various application fields in real life. At this time, the AI model is basically learned based on various statistical properties of the learning data and then distributed to the system, but unexpected changes in the data in a rapidly changing data situation cause a decrease in the model's performance. In particular, as it becomes important to find drift signals of deployed models in order to respond to new and unknown attacks that are constantly created in the security field, the need for lifecycle management of the entire model is gradually emerging. In general, it can be detected through performance changes in the model's accuracy and error rate (loss), but there are limitations in the usage environment in that an actual label for the model prediction result is required, and the detection of the point where the actual drift occurs is uncertain. there is. This is because the model's error rate is greatly influenced by various external environmental factors, model selection and parameter settings, and new input data, so it is necessary to precisely determine when actual drift in the data occurs based only on the corresponding value. There are limits to this. Therefore, this paper proposes a method to detect when actual drift occurs through an Anomaly analysis technique based on XAI (eXplainable Artificial Intelligence). As a result of testing a classification model that detects DGA (Domain Generation Algorithm), anomaly scores were extracted through the SHAP(Shapley Additive exPlanations) Value of the data after distribution, and as a result, it was confirmed that efficient drift point detection was possible.