• Journal of IKEEE
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• v.23 no.2
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• pp.413-418
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• 2019

Attacks on existing sensor networks include sniffing, flooding, and spoofing attacks. The basic countermeasures include encryption and authentication methods and switching methods. Wormhole attack, HELLO flood attack, Sybil attack, sinkhole attack, and selective delivery attack are the attacks on the network layer in wireless sensor network (WSN). These attacks may not be defended by the basic countmeasures mentioned above. In this paper, new countermeasures against these attacks include periodic key changes and regular network monitoring. Moreover, we present various threats (attacks) in the network layer of wireless sensor networks and new countermeasures accordingly.

• Journal of Korea Society of Digital Industry and Information Management
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• v.15 no.2
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• pp.43-52
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• 2019

The recent technological developments of smart devices, multiple services are provided to enhance the users' quality of life including smart city, smart energy, smart car, smart healthcare, smart home, and so on. Academia and industries try to provide the users with convenient services upon seamless technological research and developments. Also, whenever and wherever a variety of services can be used without any limitation on the place and time upon connecting with different types of devices. However, security weaknesses due to integrations of multiple technological elements have been detected resulting in the leakage of user information, account hacking, and privacy leakage, threats to people's lives by device operation have been raised. In this paper, safer communication framework is suggested by device control and user authentication in the mobile network environment. After implementations of registration and authentication processes by users and devices, safe communication protocol is designed based on this. Also, renewal process is designed according to the safe control of the device. In the performance evaluation, safety was analyzed on the attack of protocol change weakness occurred in the existing system, service halt, data leakage, illegal operation control of message, and so on, which confirmed the enhanced speed approximately by 8% and 23% in the communication and verification parts, respectively, compared to the existing system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1871-1890
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• 2021

The transition toward a contactless society has been rapidly progressing owing to the recent COVID-19 pandemic. As a result, the IT environment of organizations and enterprises is changing rapidly; in particular, data security is expanding to the private sector. To adapt to these changes, organizations and companies have started to securely transfer confidential data to residential PCs and personally owned devices of employees working from home or from other locations. Therefore, organizations and companies are introducing streaming data services, such as the virtual desktop infrastructure (VDI) or cloud services, to securely connect internal and external networks. These methods have the advantage of providing data without the need to download to a third terminal; however, while the data are being streamed, attacks such as screen shooting or capturing are performed. Therefore, there is an increasing interest in prevention techniques against screen capture threats that may occur in a contactless environment. In this study, we analyze possible screen capture methods in a PC and a mobile phone environment and present techniques that can protect the screens against specific attack methods. The detection and defense for screen capture of PC applications on Windows OS and Mac OS could be solved with a single agent using our proposed techniques. Screen capture of mobile devices can be prevented by applying our proposed techniques on Android and iOS.

• Archives of Plastic Surgery
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• v.48 no.3
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• pp.295-304
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• 2021

Clinical photography is an essential component of patient care in plastic surgery. The use of unsecured smartphone cameras, digital cameras, social media, instant messaging, and commercially available cloud-based storage devices threatens patients' data safety. This paper Identifies potential risks of clinical photography and heightens awareness of safe clinical photography. Specifically, we evaluated existing risk-mitigation strategies globally, comparing them to industry standards in similar settings, and formulated a framework for developing a risk-mitigation plan for avoiding data breaches by identifying the safest methods of picture taking, transfer to storage, retrieval, and use, both within and outside the organization. Since threats evolve constantly, the framework must evolve too. Based on a literature search of both PubMed and the web (via Google) with key phrases and child terms (for PubMed), the risks and consequences of data breaches in individual processes in clinical photography are identified. Current clinical-photography practices are described. Lastly, we evaluate current risk mitigation strategies for clinical photography by examining guidelines from professional organizations, governmental agencies, and non-healthcare industries. Combining lessons learned from the steps above into a comprehensive framework that could contribute to national/international guidelines on safe clinical photography, we provide recommendations for best practice guidelines. It is imperative that best practice guidelines for the simple, safe, and secure capture, transfer, storage, and retrieval of clinical photographs be co-developed through cooperative efforts between providers, hospital administrators, clinical informaticians, IT governance structures, and national professional organizations. This would significantly safeguard patient data security and provide the privacy that patients deserve and expect.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.4
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• pp.522-528
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• 2020

Rapidly spreading malware, such as ransomware, trojans and Internet worms, have become one of the new major threats of the Internet recently. In order to resist against their malicious behaviors, it is essential to comprehend how malware propagate and how main factors affect spreads of them. In this paper, we aim to develop a spread prediction tool based on the modeling of malware epidemics. So we surveyed the related studies, and described the system design and implementation. In addition, we experimented on the spread of malware with major factors of malware using the developed spread prediction tool. If you make good use of the proposed prediction tool, it is possible to predict the malware spread at major factors and explore under various responses from a macro perspective with only basic knowledge of the recently wormable malware.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.2
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• pp.39-46
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• 2021

Damages by malware have recently been increasing. Conventional signature-based antivirus solutions are helplessly vulnerable to unprecedented new threats such as Zero-day attack and ransomware. Despite that, many enterprises have retained signature-based antivirus solutions as part of the multiple endpoints security strategy. They do recognize the problem. This paper proposes a solution using the blockchain and deep learning technologies as the next-generation antivirus solution. It uses the antivirus software that updates through an existing DB server to supplement the detection unit and organizes the blockchain instead of the DB for deep learning using various samples and forms to increase the detection rate of new malware and falsified malware.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.169-176
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• 2021

Maritime monitoring requirements have been beyond human operators capabilities due to the broadness of the coverage area and the variety of monitoring activities, e.g. illegal migration, or security threats by foreign warships. Abnormal vessel movement can be defined as an unreasonable movement deviation from the usual trajectory, speed, or other traffic parameters. Detection of the abnormal vessel movement requires the operators not only to pay short-term attention but also to have long-term trajectory trace ability. Recent advances in deep learning have shown the potential of deep learning techniques to discover hidden and more complex relations that often lie in low dimensional latent spaces. In this paper, we propose a deep autoencoder-based clustering model for automatic detection of vessel movement anomaly to assist monitoring operators to take actions on the vessel for more investigation. We first generate gridded trajectory images by mapping the raw vessel trajectories into two dimensional matrix. Based on the gridded image input, we test the proposed model along with the other deep autoencoder-based models for the abnormal trajectory data generated through rotation and speed variation from normal trajectories. We show that the proposed model improves detection accuracy for the generated abnormal trajectories compared to the other models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.4224-4243
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• 2021

Cognitive radio (CR) is a feasible intelligent technology and can be used as an effective solution to spectrum scarcity and underutilization. As the key function of CR, cooperative spectrum sensing (CSS) is able to effectively prevent the harmful interference with primary users (PUs) and identify the available spectrum resources by exploiting the spatial diversity of multiple secondary users (SUs). However, the open nature of the cognitive radio networks (CRNs) framework makes CSS face many security threats, such as, the malicious user (MU) launches Byzantine attack to undermine CRNs. For this aim, we make an in-depth analysis of the motive and purpose from the MU's perspective in the interweave CR system, aiming to provide the future guideline for defense strategies. First, we formulate a dynamic Byzantine attack model by analyzing Byzantine behaviors in the process of CSS. On the basis of this, we further make an investigation on the condition of making the fusion center (FC) blind when the fusion rule is unknown for the MU. Moreover, the throughput and interference to the primary network are taken into consideration to evaluate the impact of Byzantine attack on the interweave CR system, and then analyze the optimal strategy of Byzantine attack when the fusion rule is known. Finally, theoretical proofs and simulation results verify the correctness and effectiveness of analyses about the impact of Byzantine attack strategy on the throughput and interference.

• Journal of Biomedical Engineering Research
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• v.44 no.2
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• pp.99-108
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• 2023

According to the COVID-19, development of various medical software based on IoT(Internet of Things) was accelerated. Especially, interest in a central software system that can remotely monitor and control ventilators is increasing to solve problems related to the continuous increase in severe COVID-19 patients. Since medical device software is closely related to human life, this study aims to develop central monitoring system that can remotely monitor and control multiple ventilators in compliance with medical device software development standards and to verify performance of system. In addition, to ensure the safety and reliability of this central monitoring system, this study also specifies risk management requirements that can identify hazardous situations and evaluate potential hazards and confirms the implementation of cybersecurity to protect against potential cyber threats, which can have serious consequences for patient safety. As a result, we obtained medical device software manufacturing certificates from MFDS(Ministry of Food and Drug Safety) through technical documents about performance verification, risk management and cybersecurity application.

• Journal of the Korea Institute of Military Science and Technology
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• v.27 no.6
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• pp.684-692
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• 2024

Effective border security is crucial in managing and mitigating firearm-related threats. While prior research has focused on firearm detection, it lacks contextual analysis. This paper advances firearm-related incident assessment by integrating pose estimation to improve gun violence detection. Our novel approach extracts body and firearm pose graphs and employs Graph Attention Networks(GAT) for graph analysis to accurately identify gun violence incidents. By recognizing associated actions, our system provides greater situational awareness beyond mere firearm detection. Utilizing Graph-LSTM, we capture spatial and temporal information. As a result, our proposed algorithm is lighter and more accurate than the CNN-LSTM model used as a baseline, achieving test F1-scores of 82.04 % on our collected data.