• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.18 no.3
• /
• pp.685-703
• /
• 2024

Cloud computing provides each consumer with a large-scale computing tool. Different Cyber Attacks can potentially target cloud computing systems, as most cloud computing systems offer services to many people who are not known to be trustworthy. Therefore, to protect that Virtual Machine from threats, a cloud computing system must incorporate some security monitoring framework. There is a tradeoff between the security level of the security system and the performance of the system in this scenario. If strong security is needed, then the service of stronger security using more rules or patterns is provided, since it needs much more computing resources. A new way of security system is introduced in this work in cloud environments to the VM on account of resources allocated to customers are ease. The main spike of Fog computing is part of the cloud server's work in the ongoing study tells the step-by-step cloud server to change the tremendous measurement of information because the endeavor apps are relocated to the cloud to keep the framework cost. The cloud server is devouring and changing a huge measure of information step by step to reduce complications. The Medical Data Health-Care (MDHC) records are stored in Cloud datacenters and Fog layer based on the guard intensity and the key is provoked for ingress the file. The monitoring center sustains the Activity Log, Risk Table, and Health Records. Cloud computing and Fog computing were combined in this paper to review data movement and safe information about MDHC.

• Journal of Digital Convergence
• /
• v.12 no.9
• /
• pp.219-223
• /
• 2014

In line with the advanced wireless communication technology, M2M (Machine-to-Machine) communication has drawn attention in industry. M2M communication features are installed and operated in the fields where human accessibility is highly limited such as disaster, safety, construction, health and welfare, climate, environment, logistics, culture, defense, medical care, agriculture and stockbreeding. In M2M communication, machine replaces people for automatic communication and countermeasures as part of unmanned information management and machine operation. Wireless M2M inter-device communication is likely to be exposed to intruders' attacks, causing security issues, which warrants proper security measures including cross-authentication of whether devices are legitimate. Therefore, research on multiple security protocols has been conducted. The present study applied SessionKey, HashFunction and Nonce to address security issues in M2M communication and proposed a safe protocol with reinforced security properties. Notably, unlike most previous studies arguing for the security of certain protocols based on mathematical theorem proving, the present study used the formal verification with Casper/FDR to prove the safety of the proposed protocol. In short, the proposed protocol was found to be safe and secure.

• Korean Security Journal
• /
• no.17
• /
• pp.361-381
• /
• 2008

Quality rating of machine security systems is difficult because both tangible and intangible services are included. However, still, the research template applied the SERVQUAL model with the intention of confirming machine security systems' service quality formation and experimentally inspecting the relationship between service quality and customer satisfaction. Therefore, the following highlights the experimental research outcomes and their implications for small-scale businesses utilizing machine security systems in the Daegu region. First, after observing whether the determining factors constitute service quality, four components were found to have significant influence on customer satisfaction. Additionally, in observing any differences in their influences, the following in order were observed as having influence on customer satisfaction: empathy, assurance reliability, responsiveness, and tangibility. Moreover, though companies‘ newest facilities and equipment are important, it can be concluded that a company employees’ prudent consideration, individual interest, reliability, and assurance for the customer carry greater importance. Secondly, though we intended to survey machine security systems by employing the SERVQUAL model, determinant factor analysis results found applying SERVQUAL model in its original state a challenge. According to results from determinant factor analysis, the basis for forming service quality is determined by assurance reliability, empathy, tangibility, and responsiveness. Furthermore, in future research, while more accurately distinguishing between assurance and reliability, a more appropriate model must also be considered for modification in domestic machine security system industry‘s service quality evaluation.

• Advances in environmental research
• /
• v.12 no.2
• /
• pp.113-122
• /
• 2023

Biometric authentication has become an essential part of modern-day security systems, especially in financial institutions like banks. A face recognition-based ATM is a biometric authentication system, that uses facial recognition technology to verify the identity of bank account holders during ATM transactions. This technology offers a secure and convenient alternative to traditional ATM transactions that rely on PIN numbers for verification. The proposed system captures users' pictures and compares it with the stored image in the bank's database to authenticate the transaction. The technology also offers additional benefits such as reducing the risk of fraud and theft, as well as speeding up the transaction process. However, privacy and data security concerns remain, and it is important for the banking sector to instrument solid security actions to protect customers' personal information. The proposed system consists of two stages: the first stage captures the user's facial image using a camera and performs pre-processing, including face detection and alignment. In the second stage, machine learning algorithms compare the pre-processed image with the stored image in the database. The results demonstrate the feasibility and effectiveness of using face recognition for ATM authentication, which can enhance the security of ATMs and reduce the risk of fraud.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• v.9 no.2
• /
• pp.791-794
• /
• 2005

In this paper, we describe security for a virtual heterogeneous machine. Our security architecure is based on separation of services into for distinct system support for domains, where available. We have chosen to use emergong public key technology as an interim solution to provide domain seperation. Th proposed architecture has been analysed in numerically.

• International Journal of Computer Science & Network Security
• /
• v.21 no.9
• /
• pp.297-302
• /
• 2021

The development of various phishing websites enables hackers to access confidential personal or financial data, thus, decreasing the trust in e-business. This paper compared the detection techniques utilizing URL-based features. To analyze and compare the performance of supervised machine learning classifiers, the machine learning classifiers were trained by using more than 11,005 phishing and legitimate URLs. 30 features were extracted from the URLs to detect a phishing or legitimate URL. Logistic Regression, Random Forest, and CatBoost classifiers were then analyzed and their performances were evaluated. The results yielded that CatBoost was much better classifier than Random Forest and Logistic Regression with up to 96% of detection accuracy.

• Journal of Electrical Engineering and Technology
• /
• v.11 no.1
• /
• pp.192-199
• /
• 2016

With changes in insulated defects, the environment, and so on, new partial discharge (PD) data are highly different from the original samples. It leads to a decrease in on-line recognition rate. The UHF signal and pulse current signal of four kinds of typical artificial defect models in gas insulated switchgear (GIS) are obtained simultaneously by experiment. The relationship map of ultra-high frequency (UHF) cumulative energy and its corresponding apparent discharge of four kinds of typical artificial defect models are plotted. UHF cumulative energy and its corresponding apparent discharge are used as inputs. The support vector machine (SVM) incremental method is constructed. Examples show that the PD SVM incremental method based on simulated annealing (SA) effectively speeds up the data update rate and improves the adaptability of the classifier compared with the original method, in that the total sample is constituted by the old and new data. The PD SVM incremental method is a better pattern recognition technology for PD on-line monitoring.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.18 no.1
• /
• pp.49-57
• /
• 2008

In this paper, we propose a novel hierarchical distributed intrusion detection system, named HNHDIDS(Home Network Hierarchical Distributed Intrusion Detection System), which is not only based on the structure of distributed intrusion detection system, but also fully consider the environment of secure home networks service. The proposed system is hierarchically composed of the one-class support vector machine(support vector data description) and local agents, in which it is designed for optimizing for the environment of secure home networks service. We support our findings with computer experiments and analysis.

• Journal of Digital Convergence
• /
• v.13 no.4
• /
• pp.205-210
• /
• 2015

The auto industry has significantly evolved to the extent that much attention is paid to M2M (Machine-to-Machine) communication. In M2M communication which was first used in meteorology, environment, logistics, national defense, agriculture and stockbreeding, devices automatically communicate and operate in accordance with varying situations. M2M system is applied to vehicles, specifically to device-to-device communication inside cars, vehicle-to-vehicle communication, communication between vehicles and traffic facilities and that between vehicles and surroundings. However, communication systems are characterized by potential intruders' attacks in transmission sections, which may cause serious safety problems if vehicles' operating system, control system and engine control parts are attacked. Thus, device-to-device secure communication has been actively researched. With a view to secure communication between vehicular devices, the present study drew on hash functions and complex mathematical formulae to design a protocol, which was then tested with Casper/FDR, a tool for formal verification of protocols. In brief, the proposed protocol proved to operate safely against a range of attacks and be effective in practical application.

• International journal of advanced smart convergence
• /
• v.13 no.2
• /
• pp.48-60
• /
• 2024

With the exponential growth of satellite data utilization, machine learning has become pivotal in enhancing innovation and cybersecurity in satellite systems. This paper investigates the role of machine learning techniques in identifying and mitigating vulnerabilities and code smells within satellite software. We explore satellite system architecture and survey applications like vulnerability analysis, source code refactoring, and security flaw detection, emphasizing feature extraction methodologies such as Abstract Syntax Trees (AST) and Control Flow Graphs (CFG). We present practical examples of feature extraction and training models using machine learning techniques like Random Forests, Support Vector Machines, and Gradient Boosting. Additionally, we review open-access satellite datasets and address prevalent code smells through systematic refactoring solutions. By integrating continuous code review and refactoring into satellite software development, this research aims to improve maintainability, scalability, and cybersecurity, providing novel insights for the advancement of satellite software development and security. The value of this paper lies in its focus on addressing the identification of vulnerabilities and resolution of code smells in satellite software. In terms of the authors' contributions, we detail methods for applying machine learning to identify potential vulnerabilities and code smells in satellite software. Furthermore, the study presents techniques for feature extraction and model training, utilizing Abstract Syntax Trees (AST) and Control Flow Graphs (CFG) to extract relevant features for machine learning training. Regarding the results, we discuss the analysis of vulnerabilities, the identification of code smells, maintenance, and security enhancement through practical examples. This underscores the significant improvement in the maintainability and scalability of satellite software through continuous code review and refactoring.