• Nuclear Engineering and Technology
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• v.56 no.2
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• pp.426-436
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• 2024

Nuclear security has been emphasized to ensure the safety of the environment and humans, as well as to protect nuclear materials and facilities from malicious attacks. With increasing utilization of nuclear energy and emerging potential threats, there has been a renewed focus on nuclear security. Korea has made efforts to enhance the regulatory oversight processes, both for general and specific legislative systems. While Korea has demonstrated effective nuclear security activities, continuous efforts are necessary to maintain a high level of security and to improve regulatory efficiency in alignment with international standards. In this study, the comprehensive regulatory oversight framework for the security of Korean nuclear facilities has been investigated. For reference, the U.S. regulatory oversight frameworks for nuclear facilities, with a focus on nuclear security, and the motivations of changes in regulatory oversight framework have been identified. By comparing these regulatory programs and frameworks, insights and considerations for enhancing nuclear security regulations have been identified. A comprehensive security inspection program tailored for the Korean regulatory oversight framework has been proposed, and has been preliminarily applied to hypothetical conditions for further discussion.

• Proceedings of the Korea Information Processing Society Conference
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• 2024.05a
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• pp.711-714
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• 2024

In the dynamic landscape of modern machine learning, Federated Learning (FL) has emerged as a compelling paradigm designed to enhance privacy by enabling participants to collaboratively train models without sharing their private data. Specifically, Distillation-based Federated Learning, like Federated Learning with Model Distillation (FedMD), Federated Gradient Encryption and Model Sharing (FedGEMS), and Differentially Secure Federated Learning (DS-FL), has arisen as a novel approach aimed at addressing Non-IID data challenges by leveraging Federated Learning. These methods refine the standard FL framework by distilling insights from public dataset predictions, securing data transmissions through gradient encryption, and applying differential privacy to mask individual contributions. Despite these innovations, our survey identifies persistent vulnerabilities, particularly concerning the susceptibility to logit inversion attacks where malicious actors could reconstruct private data from shared public predictions. This exploration reveals that even advanced Distillation-based Federated Learning systems harbor significant privacy risks, challenging the prevailing assumptions about their security and underscoring the need for continued advancements in secure Federated Learning methodologies.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.7
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• pp.143-148
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• 2020

Recently, web services have been expanded to various areas with the evolution of cloud environment. Whenever a user accesses a web service, the user's log information is stored in the web server. This log information is used as data to analyze the user's web service tendencies and is also used as important data to track the user's system access when a security problem in the system occurs. Currently, most web servers manage user log information in a centralized manner. When user log information is managed in a centralized manner, it is simple in the side of operation, but has a disadvantage of being very vulnerable to external malicious attacks. In the case of centralized management, user log information stored in the web server can be arbitrarily manipulated by external attacks, and in severe cases, the manipulated information can be leaked. In this case, it not only decreases the trust of the web service, but also makes it difficult to trace the source and cause of the attack on the web server. In order to solve these problems, this paper proposes a new method of managing user log information in a cloud environment by applying blockchain technology as an alternative to the existing centralized log management method. The proposed method can manage log information safely from external attacks because user log information is distributed and stored in blockchain on a private network with cloud environment.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.1
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• pp.32-39
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• 2017

Recently, usage of mobile cloud services has been increasing. In particular, beyond the constraints of a single cloud computing service, studies on the multi-cloud have been actively pursued. A user must authenticate multiple cloud service providers to use additional cloud services in a multi-cloud. In previous studies, an authentication method using single sign-on (SSO) was not available in all cloud services. Cloud services will not be available when the SSO server is not available due to malicious attacks, because all authentication is done via the SSO server. Additionally, using a broker, there is a vulnerability that can expose authentication information for the service provider to a user who did not sign up. In this paper, we propose a secure user authentication protocol using biometric authentication that does not expose user information when using additional cloud services. The proposed protocol can use a single biometric authentication for multi-cloud services without storing authentication information in each cloud service. In terms of key stability (to ensure stability through the key agreement process and the key area), by disabling various attack methods, such as man-in-the-middle attacks and replay attacks, we provide secure mobile cloud services.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.44 no.3 s.357
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• pp.33-40
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• 2007

In this paper, low-rate TCP attack as one of shrew attacks is considered and the scaling based dynamic time warping (S-DTW) algorithm is introduced. The low-rate TCP attack can not be detected by the detection method for the previous flooding DoS/DDoS (Denial of Service/Distirbuted Denial of Service) attacks due to its low average traffic rate. It, however, is a periodic short burst that exploits the homogeneity of the minimum retransmission timeout (RTO) of TCP flows and then some pattern matching mechanisms have been proposed to detect it among legitimate input flows. A DTW mechanism as one of detection approaches has proposed to detect attack input stream consisting of many legitimate or attack flows, and shown a depending method as well. This approach, however, has a problem that legitimate input stream may be caught as an attack one. In addition, it is difficult to decide a threshold for separation between the legitimate and the malicious. Thus, the causes of this problem are analyzed through simulation and the scaling by maximum auto-correlation value is executed before computing the DTW. We also discuss the results on applying various scaling approaches and using standard deviation of input streams monitored.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.1
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• pp.123-133
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• 2018

Cyber-Physical System (CPS) is a system in which a physical system and a cyber system are strongly integrated. In order to operate the target physical system stably, the CPS constantly monitors the physical system through the sensor and performs control using the actuator according to the current state. If a malicious attacker performs a forgery attack on the measured values of the sensors in order to conceal their attacks, the cyber system operated based on the collected data can not recognize the current operation status of the physical system. This causes the delay of the response of the automation system and the operator, and then more damage will occur. To protect the CPS from increasingly sophisticated and targeted attacks, countermeasures must be developed that can detect stealthy deception attacks. However, in the CPS environment composed of various heterogeneous devices, the process of analyzing and demonstrating the vulnerability to actual field devices requires a lot of time. Therefore, in this study, we propose a method of constructing the experiment environment of the PLCitM (PLC in the middle) which can verify the performance of the techniques to detect the CPS stealthy deception attack and present the experimental results.

• Convergence Security Journal
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• v.22 no.3
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• pp.25-32
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• 2022

Recently, the image processing industry has been activated as deep learning-based technology is introduced in the image recognition and detection field. With the development of deep learning technology, learning model vulnerabilities for adversarial attacks continue to be reported. However, studies on countermeasures against poisoning attacks that inject malicious data during learning are insufficient. The conventional countermeasure against poisoning attacks has a limitation in that it is necessary to perform a separate detection and removal operation by examining the training data each time. Therefore, in this paper, we propose a technique for reducing the attack success rate by applying modifications to the training data and inference data without a separate detection and removal process for the poison data. The One-shot kill poison attack, a clean label poison attack proposed in previous studies, was used as an attack model. The attack performance was confirmed by dividing it into a general attacker and an intelligent attacker according to the attacker's attack strategy. According to the experimental results, when the proposed defense mechanism is applied, the attack success rate can be reduced by up to 65% compared to the conventional method.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.125-141
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• 2012

These days, the malicious attacks and hacks on the networked systems are dramatically increasing, and the patterns of them are changing rapidly. Consequently, it becomes more important to appropriately handle these malicious attacks and hacks, and there exist sufficient interests and demand in effective network security systems just like intrusion detection systems. Intrusion detection systems are the network security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. Conventional intrusion detection systems have generally been designed using the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. However, they cannot handle new or unknown patterns of the network attacks, although they perform very well under the normal situation. As a result, recent studies on intrusion detection systems use artificial intelligence techniques, which can proactively respond to the unknown threats. For a long time, researchers have adopted and tested various kinds of artificial intelligence techniques such as artificial neural networks, decision trees, and support vector machines to detect intrusions on the network. However, most of them have just applied these techniques singularly, even though combining the techniques may lead to better detection. With this reason, we propose a new integrated model for intrusion detection. Our model is designed to combine prediction results of four different binary classification models-logistic regression (LOGIT), decision trees (DT), artificial neural networks (ANN), and support vector machines (SVM), which may be complementary to each other. As a tool for finding optimal combining weights, genetic algorithms (GA) are used. Our proposed model is designed to be built in two steps. At the first step, the optimal integration model whose prediction error (i.e. erroneous classification rate) is the least is generated. After that, in the second step, it explores the optimal classification threshold for determining intrusions, which minimizes the total misclassification cost. To calculate the total misclassification cost of intrusion detection system, we need to understand its asymmetric error cost scheme. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, total misclassification cost is more affected by FNE rather than FPE. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 10,000 samples from them by using random sampling method. Also, we compared the results from our model with the results from single techniques to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell R4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on GA outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that the proposed model outperformed all the other comparative models in the total misclassification cost perspective. Consequently, it is expected that our study may contribute to build cost-effective intelligent intrusion detection systems.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.6
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• pp.159-166
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• 2013

Recently, because the interest and needs in privacy protection are growing, smartcard-based remote user authentication schemes have been actively studied to provide the user anonymity. In 2008, Kim et al. first proposed an authentication scheme in order to ensure the user anonymity against both external attackers and the remote server and track malicious users with the help of a trusted trace sever. However, in 2010, Lee et al. showed that Kim et al.'s scheme cannot provide the user anonymity against remote server, which is because the server can trace users without any help of the trace server, and then proposed a improved scheme. On the other hand, in 2010, Horng et al. proposed an authentication scheme with non-tamper resistant smart cards, in which the non-tamper resistant smart card means that an attacker may find out secret information stored in the smart card through special data analysis techniques such as monitoring power consumption, to be secure against a variety of attacks and to provide the user anonymity against external attackers. In this paper, we will propose a remote user authentication scheme with non-tamper resistant smart cards not only to ensure the user anonymity against both external attackers and the remote server but also to track malicious users with only the help of a trusted trace sever.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.2
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• pp.267-280
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• 2023

With development of computing and communications technologies, IoT environments based on high-speed networks have been extending rapidly. Especially, from home to an office or a factory, applications of IoT devices with sensing environment and performing computations are increasing. Unfortunately, IoT devices which have limited hardware resources can be vulnerable to cyber attacks. Hence, there is a concern that an IoT botnet can give rise to information leakage as a national cyber security crisis arising from abuse as a malicious waypoint or propagation through connected networks. In order to response in advance from unknown cyber threats in IoT networks, in this paper, We firstly define four types of We firstly define four types of characteristics by analyzing darknet traffic accessed from an IoT botnet. Using the characteristic, a suspicious IP address is filtered quickly. Secondly, the filtered address is identified by Cyber Threat Intelligence (CTI) or Open Source INTelligence (OSINT) in terms of an unknown suspicious host. The identified IP address is finally fingerprinted to determine whether the IP is a malicious host or not. To verify a validation of the proposed method, we apply to a Darknet on real-world SOC. As a result, about 1,000 hosts who are detected and blocked preemptively by the proposed method are confirmed as real IoT botnets.