• 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%.

• Convergence Security Journal
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• v.24 no.1
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• pp.43-49
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• 2024

The convergence of education and technology has been emphasized, leading to the application of educational technology (EdTech) in the field of education. EdTech provides learner-centered, customized learning environments through various media and learning situations. In this paper, we designed hardware for EdTech-based educational tools for IoT security education in the field of cybersecurity education. The hardware is based on a dual microcontroller unit (MCU) within a single board, allowing for both attack and defense to be performed. To leverage various sensors in the Internet of Things (IoT), the hardware is modularly designed. From an educational perspective, utilizing EdTech in cybersecurity education enhances engagement by incorporating tangible physical teaching aids. The proposed research suggests that the design of IoT security education hardware can serve as a reference for simplifying the creation of a security education environment for embedded hardware, software, sensor networks, and other areas that are challenging to address in traditional education..

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.10
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• pp.1254-1260
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• 2019

Recently, 5G cellular systems have been attracted great attention as a key enabler for Industry 4.0. In this paper, we propose a novel random access based on pilot-assisted opportunistic transmission to support internet-of-things (IoT) scenario in cellular networks. A key feature of our proposed scheme is to enable each of IoT devices to attempt opportunistic transmission of its data packet in Step 3 with randomly selected uplink pilot signal. Both the opportunistic transmission and the pilot randomization in Step 3 are effective to significantly mitigate the occurrence of packet collisions. We mathematically analyze our proposed scheme in terms of packet collision probability and uplink resource efficiency. Through simulations, we verify the validity of our analysis and evaluate the performance of our proposed scheme. Numerical results show that our proposed scheme outperforms other competitive schemes.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.607-608
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• 2017

이동 Ad-hoc 무선망은 고정 라우터나 호스트, 무선 기지국에 관계없이 순수하게 무선기반의 인프라 구조로 구성된 망이다. 기존의 무선망 인프라는 기반 구축 비용이 많이 들고 기반구조가 파괴되었을 때 서비스 제공이 불가능한 단점이 있다. Ad-hoc 무선망은 이러한 단점을 보안하기 위해 라우팅 알고리즘을 구현하여 기존의 통신망보다 Ad-hoc 무선망을 이용하면 지진으로 인한 유 무선망의 통화단절 등을 막을 수 있고, 이동전화 품질서비스, 저렴한 가격, 떨어져 있는 동일 사업장내에서 무료 통화가 가능하여 이동통신 사업자에게 통신료를 지불할 필요가 없다. 본고에서는 이를 실현화 시킬 수 있는 알고리즘 개발과 시험을 통한 결과를 제시하고자 한다.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.2
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• pp.31-36
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• 2020

The creation of the Defense Integrated Data Center(DIDC) has resulted in a reduction in manpower, operating costs, efficient and effective management of resources. However, it is difficult to effectively collect and manage the data of a large number of battlefields coming from equipments such as drones, robots, and IoT added to the fourth industrial revolution and the future battlefield. Therefore, we will propose the design of DIDC network system using NFV and SDN, which are emerging as the core technologies of 5G, a mobile communication technology. After analyzing the data sheet of each equipment, it is considered that by integrating the redundant functions, energy efficiency, resource utilization and effective network management will be possible.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.8 no.5
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• pp.429-437
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• 2015

DDoS attacks have been used for paralyzing popular Internet services. Especially, amplification attacks have grown dramatically in recent years. Defending against amplification attacks is challenging since the attacks usually generate extremely hugh amount of traffic and attack traffic is coming from legitimate servers, which is hard to differentiate from normal traffic. Moreover, some of protocols used by amplification attacks are widely adopted in IoT devices so that the number of servers susceptible to amplification attacks will continue to increase. This paper studies on the analysis of amplification attack mechanisms in detail and proposes defense methodologies for scenarios where attackers, abused servers or victims are in a monitoring network.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.2
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• pp.175-188
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• 2017

Radio-frequency identification (RFID) is being used in various fields as a technology for identifying objects (people, things etc.) using radio frequencies. In the past, there was an attempt to apply RFID into national defense, but failed to spread RFID in the defense field because of some limitations of RFID in a specific situation (e.g., low recognition rate). Therefore, in this paper, we propose how to overcome the limitation of RFID by adopting the Internet of Things (IoT) technology which is considered as an important technology of the future. Furthermore, we propose four scenarios (i.e., healcare band and RFID, identification and anormal state detection, access control, and confidential document management) that can be used for enhancing national defense security. In addition, we analyze the basic characteristics and security requirements of RFID and IoT in order to effectively apply each technology and improve security level.

• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.57-65
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• 2021

A hyper-connected intelligence information society is emerging that creates new value by converging IoT, AI, and Bigdata, which are new technologies of the fourth industrial revolution, in all industrial fields. Everything is connected to the network and data is exploding, and artificial intelligence can learn on its own and even intellectual judgment functions are possible. In particular, the Internet of Things provides a new communication environment that can be connected to anything, anytime, anywhere, enabling super-connections where everything is connected. Artificial intelligence technology is implemented so that computers can execute human perceptions, learning, reasoning, and natural language processing. Artificial intelligence is developing advanced technologies such as machine learning, deep learning, natural language processing, voice recognition, and visual recognition, and includes software, machine learning, and cloud technologies specialized in various applications such as safety, medical, defense, finance, and welfare. Through this, it is utilized in various fields throughout the industry to provide human convenience and new values. However, on the contrary, it is time to respond as intelligent and sophisticated cyber threats are increasing and accompanied by potential adverse functions such as securing the technical safety of new technologies. In this paper, we propose a new data modeling method to enable IoT integrated security control by utilizing artificial intelligence technology as a way to solve these adverse functions.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.7
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• pp.613-621
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• 2019

Recently, with the development of the Internet of Things (IoT) and cloud computing technologies, security threats have increased as malicious codes infect IoT devices, and new malware spreads ransomware to cloud servers. In this study, we propose a threat-detection technique that checks obfuscated script patterns to compensate for the shortcomings of conventional signature-based and behavior-based detection methods. Proposed is a malicious code-detection technique that is based on malicious script-pattern analysis that can detect zero-day attacks while maintaining the existing detection rate by registering and checking derived distribution patterns after analyzing the types of malicious scripts distributed through websites. To verify the performance of the proposed technique, a prototype system was developed to collect a total of 390 malicious websites and experiment with 10 major malicious script-distribution patterns derived from analysis. The technique showed an average detection rate of about 86% of all items, while maintaining the existing detection speed based on the detection rule and also detecting zero-day attacks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5425-5448
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• 2018

Wireless sensor network has been widely used in Internet of Things (IoT) applications to support large and dense networks. As sensor nodes are usually tiny and provided with limited hardware resources, the existing multiple access methods, which involve high computational complexity to preserve the protocol performance, is not available under such a scenario. In this paper, we propose an intelligent Medium Access Control (MAC) protocol selection scheme based on machine learning in wireless sensor networks. We jointly consider the impact of inherent behavior and external environments to deal with the application limitation problem of the single type MAC protocol. This scheme can benefit from the combination of the competitive protocols and non-competitive protocols, and help the network nodes to select the MAC protocol that best suits the current network condition. Extensive simulation results validate our work, and it also proven that the accuracy of the proposed MAC protocol selection strategy is higher than the existing work.