• Convergence Security Journal
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• v.21 no.1
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• pp.93-100
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• 2021

Damage caused by intelligent cyber attacks not only disrupts system operations and leaks information, but also entails massive economic damage. Recently, cyber attacks have a distinct goal and use advanced attack tools and techniques to accurately infiltrate the target. In order to minimize the damage caused by such an intelligent cyber attack, it is necessary to block the cyber attack at the beginning or during the attack to prevent it from invading the target's core system. Recently, technologies for predicting cyber attack paths and analyzing risk level of cyber attack using big data or artificial intelligence technologies are being studied. In this paper, a cyber attack path analysis method using attack tree and RFI is proposed as a basic algorithm for the development of an automated cyber attack path prediction system. The attack path is visualized using the attack tree, and the priority of the path that can move to the next step is determined using the RFI technique in each attack step. Based on the proposed mechanism, it can contribute to the development of an automated cyber attack path prediction system using big data and deep learning technology.

• ETRI Journal
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• v.44 no.2
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• pp.208-219
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• 2022

Crime amount prediction is crucial for optimizing the police patrols' arrangement in each region of a city. First, we analyzed spatiotemporal correlations of the crime data and the relationships between crime and related auxiliary data, including points-of-interest (POI), public service complaints, and demographics. Then, we proposed a crime amount prediction model based on 2D convolution and long short-term memory neural network (2DCONV-LSTM). The proposed model captures the spatiotemporal correlations in the crime data, and the crime-related auxiliary data are used to enhance the regional spatial features. Extensive experiments on real-world datasets are conducted. Results demonstrated that capturing both temporal and spatial correlations in crime data and using auxiliary data to extract regional spatial features improve the prediction performance. In the best case scenario, the proposed model reduces the prediction error by at least 17.8% and 8.2% compared with support vector regression (SVR) and LSTM, respectively. Moreover, excessive auxiliary data reduce model performance because of the presence of redundant information.

• Journal of Computing Science and Engineering
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• v.7 no.3
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• pp.187-197
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• 2013

Security has long been a challenging problem in wireless networks, mainly due to its broadcast nature of communication. This opens up simple yet effective measures to thwart useful communications between legitimate radios. Spread spectrum technologies, such as direct sequence spread spectrum (DSSS), have been developed as effective countermeasures against, for example, jamming attacks. This paper surveys previous research on securing a DSSS channel even further, using physical layer attributes-keyless DSSS mechanisms, and watermarked DSSS (WDSSS) schemes. The former has been motivated by the fact that it is still an open question to establish and share the secret spread sequence between the transmitter and the receiver without being noticed by adversaries. The basic idea of the latter is to exploit the redundancy inherent in DSSS's spreading process to embed watermark information. It can be considered a counter measure (authentication) for an intelligent attacker who obtains the spread sequence to generate fake messages. This paper also presents and evaluates an adaptive DSSS scheme that takes both jam resistance and communication efficiency into account.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.12
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• pp.4308-4325
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• 2021

The importance and necessity of artificial intelligence, particularly machine learning, has recently been emphasized. In fact, artificial intelligence, such as intelligent surveillance cameras and other security systems, is used to solve various problems or provide convenience, providing solutions to problems that humans traditionally had to manually deal with one at a time. Among them, information security is one of the domains where the use of artificial intelligence is especially needed because the frequency of occurrence and processing capacity of dangerous codes exceeds the capabilities of humans. Therefore, this study intends to examine the definition of artificial intelligence and machine learning, its execution method, process, learning algorithm, and cases of utilization in various domains, particularly the cases and contents of artificial intelligence technology used in the field of information security. Based on this, this study proposes a method to apply machine learning technology to the method of classifying and detecting malware that has rapidly increased in recent years. The proposed methodology converts software programs containing malicious codes into images and creates training data suitable for machine learning by preparing data and augmenting the dataset. The model trained using the images created in this manner is expected to be effective in classifying and detecting malware.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.3
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• pp.779-800
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• 2024

Unmanned Aerial Vehicle (UAV) networks, also known as drone networks, have gained significant attention for their potential in various applications, including communication. UAV networks for communication involve using a fleet of drones to establish wireless connectivity and provide communication services in areas where traditional infrastructure is lacking or disrupted. UAV communication networks need to be highly secured to ensure the technology's security and the users' safety. The proposed survey provides a comprehensive overview of the current state-of-the-art UAV network security solutions. In this paper, we analyze the existing literature on UAV security and identify the various types of attacks and the underlying vulnerabilities they exploit. Detailed mitigation techniques and countermeasures for the protection of UAVs are described in this paper. The survey focuses on the implementation of novel technologies like Q-learning, blockchain, IRS, and mmWave. This paper discusses network simulation tools that range in complexity, features, and programming capabilities. Finally, future research directions and challenges are highlighted.

• Journal of the Semiconductor & Display Technology
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• v.22 no.3
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• pp.112-118
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• 2023

As technology advances, the need for enhanced preparedness against cyber-attacks becomes an increasingly critical problem. Therefore, it is imperative to consider various circumstances and to prepare for cyber-attack strategic technology. This paper proposes a method to solve network security problems by applying reinforcement learning to cyber-security. In general, traditional static cyber-security methods have difficulty effectively responding to modern dynamic attack patterns. To address this, we implement cyber-attack scenarios such as 'Tiny Alpha' and 'Small Alpha' and evaluate the performance of various reinforcement learning methods using Network Attack Simulator, which is a cyber-attack simulation environment based on the gymnasium (formerly Open AI gym) interface. In addition, we experimented with different RL algorithms such as value-based methods (Q-Learning, Deep-Q-Network, and Double Deep-Q-Network) and policy-based methods (Actor-Critic). As a result, we observed that value-based methods with discrete action spaces consistently outperformed policy-based methods with continuous action spaces, demonstrating a performance difference ranging from a minimum of 20.9% to a maximum of 53.2%. This result shows that the scheme not only suggests opportunities for enhancing cybersecurity strategies, but also indicates potential applications in cyber-security education and system validation across a large number of domains such as military, government, and corporate sectors.

• The Journal of the Korea Contents Association
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• v.19 no.8
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• pp.667-676
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• 2019

In recent years, the occurrence of life and violent crime in the city centered on crime-prone areas has become a big issue, raising the public interest in safety from crime has been higher than ever. As the initial response to crime and the importance of crime prevention are emerging, the government is preparing various measures such as crime prevention service using advanced technology. This project is a demonstration project to select specific target sites and apply that on a trial basis with the main purpose of commercialization of intelligent crime prevention technologies and services. In order to spread the business through the demonstration project, it is expected that the practical economic evaluation as well as the academic and practical value of the project will have a great significance. In this study, the purpose of this research is to derive economic feasibility through technologies and services to be implemented in Indokwon district of Anyang city, which is selected demonstration site of the intelligent security, to promote the spread of business and contribute to the policy activation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.6
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• pp.3254-3272
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• 2017

With the development of wireless access technologies and the popularity of mobile intelligent terminals, cloud computing is expected to expand to mobile environments. Attribute-based encryption, widely applied in cloud computing, incurs massive computational cost during the encryption and decryption phases. The computational cost grows with the complexity of the access policy. This disadvantage becomes more serious for mobile devices because they have limited resources. To address this problem, we present an efficient verifiable outsourced scheme based on the bilinear group of prime order. The scheme is called the verifiable outsourced computation ciphertext-policy attribute-based encryption scheme (VOC-CP-ABE), and it provides a way to outsource intensive computing tasks during encryption and decryption phases to CSP without revealing the private information and leaves only marginal computation to the user. At the same time, the outsourced computation can be verified by two hash functions. Then, the formal security proofs of its (selective) CPA security and verifiability are provided. Finally, we discuss the performance of the proposed scheme with comparisons to several related works.

• Proceedings of the Korean Society of Computer Information Conference
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• 2011.06a
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• pp.203-206
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• 2011

차세대 전력망인 Smart Grid는 에너지 효율성을 높이기 위한 대안이다. 현재 Smart Grid는 빠른 진행 속도에 비해 보안상 취약점을 다수 내포하고 있다. 이에 Smart Grid의 보안 취약점들을 분석하고, 취약점들에 대한 대응책을 마련하기 위한 방법을 연구하며, 그에 따른 보안 정책을 개발하여, 이들을 저장하여 보안 DB를 구축하고, 보안 시스템을 개발하여 지능적 취약점 DB 보안 시스템을 작동 시킬 수 있는 방법을 연구하여 다가올 보안 위협에 대응 할 수 있도록 하여 Smart Grid 시대의 발전에 기여 할 것이다.

• International Journal of Computer Science & Network Security
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• v.21 no.7
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• pp.1-7
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• 2021

An amazing growth in the field of Internet of Things (IoT) and Blockchain based smart cities from both industry and academia has been witnessed in the recent years. There are many smart applications such as intelligent transportation, smart banking, improving the life style of citizen, energy consumption and managing the waste in the city, handling home needs are supporting the Smart city concept. These applications are profoundly supported by the advanced technologies like Blockchain as well as IoT in the recent past. Smart cities can be supported by the Blockchain core concepts such as secure, transparent, decentralized and immutable nature. Still, Blockchain and IoT technologies implementation in smart cities are in their early stages and significant research efforts are desirable to integrate them. This review article explores the roles and responsibilities of Blockchain and IoT in building smart cities.