• Convergence Security Journal
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• v.23 no.1
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• pp.55-61
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• 2023

QR Code is a two-dimensional code in the form of a matrix that contains data in a square-shaped black-and-white grid pattern, and has recently been used in various fields. In particular, in order to prevent the spread of COVID-19, the usage increased rapidly by identifying the movement path in the form of a QR code that anyone can easily and conveniently use. As such, Qshing attacks and damages using QR codes are increasing in proportion to the usage of QR codes. Therefore, in this paper, a system was implemented to block movement to harmful sites and installation of malicious codes when scanning QR codes.

• International Journal of Computer Science & Network Security
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• v.23 no.6
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• pp.169-175
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• 2023

Ethical hackers are using different tools and techniques to encounter malicious cyber-attacks generated by bad hackers. During the software development process, development teams typically bypass or ignore the security parameters of the software. Whereas, with the advent of online web-based software, security is an essential part of the software development process for implementing secure software. Security features cannot be added as additional at the end of the software deployment process, but they need to be paid attention throughout the SDLC. In that view, this paper presents a new, Ethical Hacking - Software Development Life Cycle (EH-SDLC) introducing ethical hacking processes and phases to be followed during the SDLC. Adopting these techniques in SDLC ensures that consumers find the end-product safe, secure and stable. Having a team of penetration testers as part of the SDLC process will help you avoid incurring unnecessary costs that come up after the data breach. This research work aims to discuss different operating systems and tools in order to facilitate the secure execution of the penetration tests during SDLC. Thus, it helps to improve the confidentiality, integrity, and availability of the software products.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1916-1934
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• 2023

The phishing attack is a malicious emerging threat on the internet where the hackers try to access the user credentials such as login information or Internet banking details through pirated websites. Using that information, they get into the original website and try to modify or steal the information. The problem with traditional defense systems like firewalls is that they can only stop certain types of attacks because they rely on a fixed set of principles to do so. As a result, the model needs a client-side defense mechanism that can learn potential attack vectors to detect and prevent not only the known but also unknown types of assault. Feature selection plays a key role in machine learning by selecting only the required features by eliminating the irrelevant ones from the real-time dataset. The proposed model uses Hyperparameter Optimized Artificial Neural Networks (H-ANN) combined with a Hybrid Firefly and Grey Wolf Optimization algorithm (H-FFGWO) to detect and block phishing websites in Internet of Things(IoT) Applications. In this paper, the H-FFGWO is used for the feature selection from phishing datasets ISCX-URL, Open Phish, UCI machine-learning repository, Mendeley website dataset and Phish tank. The results showed that the proposed model had an accuracy of 98.07%, a recall of 98.04%, a precision of 98.43%, and an F1-Score of 98.24%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.147-169
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• 2024

In pace with the development of network technology at lightning speed, social networks have been extensively applied in our lives. However, as social networks retain a large number of users' sensitive information, the openness of this information makes social networks vulnerable to attacks by malicious attackers. To preserve the link privacy of individuals in social networks, an uncertain graph method based on node random response is devised, which satisfies differential privacy while maintaining expected data utility. In this method, to achieve privacy preserving, the random response is applied on nodes to achieve edge modification on an original graph and node differential privacy is introduced to inject uncertainty on the edges. Simultaneously, to keep data utility, a divide and conquer strategy is adopted to decompose the original graph into many sub-graphs and each sub-graph is dealt with separately. In particular, only some larger sub-graphs selected by the exponent mechanism are modified, which further reduces the perturbation to the original graph. The presented method is proven to satisfy differential privacy. The performances of experiments demonstrate that this uncertain graph method can effectively provide a strict privacy guarantee and maintain data utility.

• 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.

• Convergence Security Journal
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• v.24 no.3
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• pp.13-21
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• 2024

Recently, with the rapid expansion of networks driven by the advancements of the Fourth Industrial Revolution, cybersecurity threats are becoming increasingly severe. Traditional signature-based Network Intrusion Detection Systems (NIDS) are effective in detecting known attacks but show limitations when faced with new threats such as Advanced Persistent Threats (APT). Additionally, deep learning models based on supervised learning can lead to biased decision boundaries due to the imbalanced nature of network traffic data, where normal traffic vastly outnumbers malicious traffic. To address these challenges, this paper proposes a network intrusion detection method based on one-class models that learn only from normal data to identify abnormal traffic. The effectiveness of this approach is validated through experiments using the Deep SVDD and MemAE models on the NSL-KDD dataset. Comparative analysis with supervised learning models demonstrates that the proposed method offers superior adaptability and performance in real-world scenarios.

• Journal of Internet Technology
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• v.22 no.4
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• pp.903-911
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• 2021

As cyber-attacks on Cyber-Physical System (CPS) become more diverse and sophisticated, it is important to quickly detect malicious behaviors occurring in CPS. Since CPS can collect sensor data in near real time throughout the process, there have been many attempts to detect anomaly behavior through normal behavior learning from the perspective of data-driven security. However, since the CPS datasets are big data and most of the data are normal data, it has always been a great challenge to analyze the data and implement the anomaly detection model. In this paper, we propose and evaluate the Clustered Deep One-Class Classification (CD-OCC) model that combines the clustering algorithm and deep learning (DL) model using only a normal dataset for anomaly detection. We use auto-encoder to reduce the dimensions of the dataset and the K-means clustering algorithm to classify the normal data into the optimal cluster size. The DL model trains to predict clusters of normal data, and we can obtain logit values as outputs. The derived logit values are datasets that can better represent normal data in terms of knowledge distillation and are used as inputs to the OCC model. As a result of the experiment, the F1 score of the proposed model shows 0.93 and 0.83 in the SWaT and HAI dataset, respectively, and shows a significant performance improvement over other recent detectors such as Com-AE and SVM-RBF.

• 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.