• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.199-201
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• 2021

The Internet of Things (IoT) is a technology that can organically connect people and things without time and space constraints by using communication network technology and sensors, and transmit and receive data in real time. The IoT used in all industrial fields has limitations in terms of storage allocation, such as device size, memory capacity, and data transmission performance, so it is important to manage power consumption to effectively utilize the limited battery capacity. In the prior research, there is a problem in that security is deteriorated instead of improving power efficiency by lightening the security algorithm of the encryption module. In this study, we proposes a low-power security architecture that can utilize high-performance security algorithms in the IoT environment. This can provide high security and power efficiency by using relatively complex security modules in low-power environments by executing security modules only when threat detection is required based on inspection results.

• Convergence Security Journal
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• v.22 no.4
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• pp.65-73
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• 2022

Since cyber operations are performed in a virtual cyber battlefield, the measurement indicators that can evaluate and visualize the current state of the cyber environment in a consistent form are required for the commander to effectively support the decision-making of cyber operations. In this paper, we propose a method to define various evaluation indicators that can be collected on the cyber battlefield, normalized them, and evaluate the cyber status in a consistent form. The proposed cyber battlefield status element consists of cyber asset-related indicators, target network-related indicators, and cyber threat-related indicators. Each indicator has 6 sub-indicators and can be used by assigning weights according to the commander's interests. The overall status of the cyber battlefield can be easily recognized because the measured indicators are visualized in time series on a single screen. Therefore, the proposed method can be used for the situational awareness required to effectively conduct cyber warfare.

• Convergence Security Journal
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• v.22 no.1
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• pp.129-134
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• 2022

A smart platform is defined as an evolved platform that realizes physical and virtual space into a hyper-connected environment by combining the existing platform and advanced IT technology. The hyper-connection that is the connection between information and information, infrastructure and infrastructure, infrastructure and information, or space and service, enables the realization and provision of high-quality services that significantly change the quality of life and environment of users. In addition, it is providing everyone with the effect of significantly improving the social safety net and personal health management level by implementing smart government and smart healthcare. A lot of information produced and consumed in these processes can act as a factor threatening the basic rights of the public and individuals by the informations themselves or through big data analysis. In particular, as the smart platform as a core function that forms the ecosystem of a smart city is naturally and continuously expanded, it faces a huge security burden in data processing and network operation. In this paper, platform components as core functions of smart city and appropriate security threats and countermeasures are studied.

• IMMUNE NETWORK
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• v.22 no.3
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• pp.21.1-21.21
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• 2022

As far the current severe coronavirus disease 2019 (COVID-19), respiratory disease is still the biggest threat to human health. In addition, infectious respiratory diseases are particularly prominent. In addition to killing and clearing the infection pathogen directly, regulating the immune responses against the pathogens is also an important therapeutic modality. Sirtuins belong to NAD+-dependent class III histone deacetylases. Among 7 types of sirtuins, silent information regulator type-1 (SIRT1) played a multitasking role in modulating a wide range of physiological processes, including oxidative stress, inflammation, cell apoptosis, autophagy, antibacterial and antiviral functions. It showed a critical effect in regulating immune responses by deacetylation modification, especially through high-mobility group box 1 (HMGB1), a core molecule regulating the immune system. SIRT1 was associated with many respiratory diseases, including COVID-19 infection, bacterial pneumonia, tuberculosis, and so on. Here, we reviewed the latest research progress regarding the effects of SIRT1 on immune system in respiratory diseases. First, the structure and catalytic characteristics of SIRT1 were introduced. Next, the roles of SIRT1, and the mechanisms underlying the immune regulatory effect through HMGB1, as well as the specific activators/inhibitors of SIRT1, were elaborated. Finally, the multitasking roles of SIRT1 in several respiratory diseases were discussed separately. Taken together, this review implied that SIRT1 could serve as a promising specific therapeutic target for the treatment of respiratory diseases.

• Journal of the Korea Society of Computer and Information
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• v.18 no.9
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• pp.21-31
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• 2013

ECU(Electric Control Unit) on the important features of the vehicle is equipped, ECU between sending and receiving messages is connected to one of the internal network(CAN BUS), but this network easily accessible from the outside and not intended to be able to receive attacks from an attacker, In this regard, the development of tools that can be used in order to verify the possibility of attacks on attacks from outside, However, the time costs incurred for developing tools and time to analyze from actual car for CAN messages to be used in the attack to find. In this paper, we want to solve it, propose a method to generate test cases required for the attack is publicly available tool called Sulley and it explains how to find the CAN messages to be used in the attack. Sulley add the CAN messages data generated library files in provided library file and than Sulley execute that make define and execute file conform to the CAN communication preferences and create message rules. Experiments performed by the proposed methodology is applied to the actual car and result, test cases generated by the CAN messages fuzzing through Sulley send in the car and as a result without a separate tool developed was operating the car.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.6
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• pp.25-31
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• 2016

With the development of IOT technology and the expansion of ICT services recently, a variety of home network services have been advanced based on wired and wireless high speed telecommunication. Domestic and global companies have been studying on the innovative technology for the users using IOT based technology and the environment for the smart home services has been gradually developed. The users live their lives with more convenience due to the expansions and developments of smart phones. However, the threatening on the security of the smart home network had occurred by various attacks with the connection to the smart environment telecommunication, lack of applications on low powered and light weight telecommunication, and the problems of security guideline. In addition, the solutions are required for the new and variant attacking cases such as data forgery and alteration of the device for disguising approach with ill will. In this article, the safe communication protocol was designed using certification management technique based on AKI which supplemented the weakness of PKI, the existing certification system in the smart environment. Utilizing the signature technique based on ECDSA, the efficiency on the communication performance was improved, and the security and the safety were analyzed on the security threat under the smart home environment.

• Proceedings of the Korea Water Resources Association Conference
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• 2002.05a
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• pp.43-50
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• 2002

Accurate quantitative forecasting of rainfall for basins with a short response time is essential to predict streamflow and flash floods. Previously, neural networks were used to develop a Quantitative Precipitation Forecasting (QPF) model that highly improved forecasting skill at specific locations in Pennsylvania, using both Numerical Weather Prediction (NWP) output and rainfall and radiosonde data. The objective of this study was to improve an existing artificial neural network model and incorporate the evolving structure and frequency of intense weather systems in the mid-Atlantic region of the United States for improved flood forecasting. Besides using radiosonde and rainfall data, the model also used the satellite-derived characteristics of storm systems such as tropical cyclones, mesoscale convective complex systems and convective cloud clusters as input. The convective classification and tracking system (CCATS) was used to identify and quantify storm properties such as life time, area, eccentricity, and track. As in standard expert prediction systems, the fundamental structure of the neural network model was learned from the hydroclimatology of the relationships between weather system, rainfall production and streamflow response in the study area. The new Quantitative Flood Forecasting (QFF) model was applied to predict streamflow peaks with lead-times of 18 and 24 hours over a five year period in 4 watersheds on the leeward side of the Appalachian mountains in the mid-Atlantic region. Threat scores consistently above .6 and close to 0.8 ∼ 0.9 were obtained fur 18 hour lead-time forecasts, and skill scores of at least 4％ and up to 6％ were attained for the 24 hour lead-time forecasts. This work demonstrates that multisensor data cast into an expert information system such as neural networks, if built upon scientific understanding of regional hydrometeorology, can lead to significant gains in the forecast skill of extreme rainfall and associated floods. In particular, this study validates our hypothesis that accurate and extended flood forecast lead-times can be attained by taking into consideration the synoptic evolution of atmospheric conditions extracted from the analysis of large-area remotely sensed imagery While physically-based numerical weather prediction and river routing models cannot accurately depict complex natural non-linear processes, and thus have difficulty in simulating extreme events such as heavy rainfall and floods, data-driven approaches should be viewed as a strong alternative in operational hydrology. This is especially more pertinent at a time when the diversity of sensors in satellites and ground-based operational weather monitoring systems provide large volumes of data on a real-time basis.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.3
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• pp.374-380
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• 2017

The Internet of Things (IoT) refers to intelligent technologies and services that connect all things to the internet so they can interactively communicate with people, other things, and other systems. The development of the IoT environment accompanies advances in network protocols applicable to more lightweight and intelligent sensors, and lightweight and diverse environments. The development of those elemental technologies is promoting the rapid progress in smart car environments that provide safety features and user convenience. These developments in smart car services will bring a positive effect, but can also lead to a catastrophe for a person's life if security issues with the services are not resolved. Although smart cars have various features with different types of communications functions to control the vehicles under the existing platforms, insecure features and functions may bring various security threats, such as bypassing authentication, malfunctions through illegitimate control of the vehicle via data forgery, and leaking of private information. In this paper, we look at types of smart car services in the IoT, deriving the security threats from smart car services based on various scenarios, suggesting countermeasures against them, and we finally propose a safe smart car application plan.

• Journal of Internet Computing and Services
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• v.19 no.5
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• pp.67-75
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• 2018

According to Symantec's Internet Security Threat Report(2018), Internet security threats such as Cryptojackings, Ransomwares, and Mobile malwares are rapidly increasing and diversifying. It means that detection of malwares requires not only the detection accuracy but also versatility. In the past, malware detection technology focused on qualitative performance due to the problems such as encryption and obfuscation. However, nowadays, considering the diversity of malware, versatility is required in detecting various malwares. Additionally the optimization is required in terms of computing power for detecting malware. In this paper, we present Stream Order(SO)-CNN and Incremental Coordinate(IC)-CNN, which are malware detection schemes using CNN(Convolutional Neural Network) that effectively detect intelligent and diversified malwares. The proposed methods visualize each malware binary file onto a fixed sized image. The visualized malware binaries are learned through GoogLeNet to form a deep learning model. Our model detects and classifies malwares. The proposed method reveals better performance than the conventional method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.8
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• pp.1551-1559
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• 2015

Recently, the performance of smart devices is almost similar to that of the existing PCs, thus the users of smart devices can perform similar works such as messengers, SNSs(Social Network Services), smart banking, etc. originally performed in PC environment using smart devices. Although the development of smart devices has led to positive impacts, it has caused negative changes such as an increase in security threat aimed at mobile environment. Specifically, the threats of mobile devices, such as leaking private information, generating unfair billing and performing DDoS(Distributed Denial of Service) attacks has continuously increased. Over 80% of the mobile devices use android platform, thus, the number of damage caused by mobile malware in android platform is also increasing. In this paper, we propose android based malware detection mechanism using time-series analysis, which is one of statistical-based detection methods.We use auto-regressive moving-average model which is extracting accurate predictive values based on existing data among time-series model. We also use fast and exact malware detection method by extracting possible malware data through Z-Score. We validate the proposed methods through the experiment results.