• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.1-7
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• 2023

In the current Internet system, there are many problems using anonymity of the network communication such as personal information leaks and crimes using the Internet system. This is why TCP/IP protocol used in Internet system does not have the user identification information on the communication data, and it is difficult to supervise the user performing the above acts immediately. As a study for solving the above problem, there is the study of Policy Based Network Management (PBNM). This is the scheme for managing a whole Local Area Network (LAN) through communication control for every user. In this PBNM, two types of schemes exist. As one scheme, we have studied theoretically about the Destination Addressing Control System (DACS) Scheme with affinity with existing internet. By applying this DACS Scheme to Internet system management, we will realize the policy-based Internet system management. In this paper, basic system design for PBNM scheme for multi-domain management utilizing data science and AI is proposed.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.151-153
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• 2023

Federated learning (FL)-based network intrusion detection techniques have enormous potential for securing the Industrial Internet of Things (IIoT) cybersecurity. The openness and connection of systems in smart industrial facilities can be targeted and manipulated by malicious actors, which emphasizes the significance of cybersecurity. The conventional centralized technique's drawbacks, including excessive latency, a congested network, and privacy leaks, are all addressed by the FL method. In addition, the rich data enables the training of models while combining private data from numerous participants. This research aims to create an FL-based architecture to improve cybersecurity and intrusion detection in IoT networks. In order to assess the effectiveness of the suggested approach, we have utilized well-known cybersecurity datasets along with centralized and federated machine learning models.

• Journal of the Korean Society for Nondestructive Testing
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• v.23 no.3
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• pp.227-236
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• 2003

Leaks in underground pipelines can cause social, environmental and economical problems. One of relevant countermeasures against leaks is to find and repair of leak points of the pipes. Leak noise is a good source to identify the location of leak points of the pipelines. Although there have been several methods to detect the leak location with leak noise, such as listening rods, hydrophones or ground microphones, they have not been so efficient tools. In this paper, acoustic emission (AE) sensors and accelermeters are used to detect leak locations which could provide all easier and move efficient method. Filtering, signal processing and algorithm of raw input data from sensors for the detection of leak location are described. A 120m-long pipeline system for experiment is installed and the results with the system show that the algorithm with the AE sensors and accelerometers offers accurate pinpointing of leaks. Theoretical analysis of sound wave propagation speed of water in underground pipes, which is critically important in leak locating, is also described.

• International journal of advanced smart convergence
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• v.12 no.2
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• pp.56-66
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• 2023

The goal of deep learning is to extract complex features from multidimensional data use the features to create models that connect input and output. Deep learning is a process of learning nonlinear features and functions from complex data, and the user data that is employed to train deep learning models has become the focus of privacy concerns. Companies that collect user's sensitive personal information, such as users' images and voices, own this data for indefinite period of times. Users cannot delete their personal information, and they cannot limit the purposes for which the data is used. The study has designed a deep learning method that employs privacy protection technology that uses distributed collaborative learning so that multiple participants can use neural network models collaboratively without sharing the input datasets. To prevent direct leaks of personal information, participants are not shown the training datasets during the model training process, unlike traditional deep learning so that the personal information in the data can be protected. The study used a method that can selectively share subsets via an optimization algorithm that is based on modified distributed stochastic gradient descent, and the result showed that it was possible to learn with improved learning accuracy while protecting personal information.

• Convergence Security Journal
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• v.16 no.7
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• pp.167-172
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• 2016

The purpose of this study is to provide implications for preventing insider information leakages in public agencies for national security. First, the study examined the definitions and current usage of information security systems of public agencies were examined. Second, web-service base information leaks and malware-base information leaks were discussed and three major credit card companies' personal information leakage cases were analyzed. Based on the analysis, four solutions were provided. First, information leakages can be protected by using web filtering solutions based on the user, which make possible to limit frequencies of malware exposures. Second, vaccine programs and vaccine management system should be implemented to prevent information leakages by malware. Third, limit the use of portable devices within local networks to prevent information leakages and vaccines programs for malware should be regularly used. Forth, to prevent information leakages by smartphone malwares, data encryption application should be used to encrypt important information.

• Journal of Internet Computing and Services
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• v.20 no.6
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• pp.137-142
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• 2019

This paper Various services exist to detect and monitor abnormal event. However, most services focus on fires and gas leaks. so It is impossible to prevent and respond to emergency situations for the elderly and severely disabled people living alone. In this study, AI model is designed and compared to detect abnormal event of heart rate signal which is considered to be the most important among various bio signals. Specifically, electrocardiogram (ECG) data is collected using Physionet's MIT-BIH Arrhythmia Database, an open medical data. The collected data is transformed in different ways. We then compare the trained AI model with the modified and ECG data.

• Journal of Convergence Society for SMB
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• v.6 no.1
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• pp.17-23
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• 2016

Recently, Internet services via various devices including smartphone have become available. Because of the development of ICT, numerous hacking incidents have occurred and most of those attacks turned out to be APT attacks. APT attack means an attack method by which a hacker continues to collect information to achieve his goal, and analyzes the weakness of the target and infects it with malicious code, and being hidden, leaks the data in time. In this paper, we examine the information collection method the APT attackers use to invade the target system in a short time using big data, and we suggest and evaluate the countermeasure to protect against the attack method using big data.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.217-219
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• 2021

With the development of ICT, the amount of data increases, and the technology of storing and processing becomes important. In this study, we study real-time file copy leakage prevention system to prevent leakage of important data in enterprises, public places, etc. As a research method, we propose a system that detects events in real time to prevent data leakage after analyzing data leakage cases and problems. The file leakage prevention system compares and analyzes with the existing EDLP system, and the proposed system reduces load and detects events. Future research requires research on the prevention of leaks through networks and various channels.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.238-241
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• 2022

North Korea launched an ICBM and declared Moratorium for the September 19 military talks. The Armed Forces must protect military security for national defense and security. The Ministry of National Defense, which received a hacking attack from North Korea, must protect its military security even more. Recently, the leakage of military data through smartphones is occurring through smartphones. Officers and non-commissioned officers can use smartphones while working. Therefore, smartphone forensics is required to check information leakage of military data from smartphones. In this study, forensic leaks of military data from the Galaxy S20 model of S company. Research integrity verification for securing smartphone forensic evidence, securing metadata, and adopting evidence. This study will contribute to the development of military security and forensic technology.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.5
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• pp.1001-1009
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• 2015

In Smart Grid, smart meter sends our electric signal data to the main server of power supply in real-time. However, the more efficient the management of power loads become, the more likely the user's pattern of usage leaks. This paper points out the threat of privacy and the need of security measures in smart device environment by showing that it's possible to identify the appliances and the specific usage patterns of users from the smart meter's data. Learning algorithm PCA is used to reduce the dimension of the feature space and k-NN Classifier to infer appliances and states of them. Accuracy is validated with 10-fold Cross Validation.