• Convergence Security Journal
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• v.12 no.5
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• pp.71-77
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• 2012

Expenses in the form of personnel expenses in the past, in modern times, machine guards to gradually transition has been. This is because the machine guard is more efficient than personnel expenses. But due to false alarms, despite the high expectations of the effects of electronic security in the operation of the electronic security system due to factors that hinder the development of machine guards growth slows. Defect removal aspects of this paper, using IPA (Importance Performance Analysis) techniques to study the operation of electronic security systems and its importance in the development of machine guards, look at how high the technical aspects of electronic security systems composite type of malfunction to minimize crime sensor are presented.

• Convergence Security Journal
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• v.16 no.5
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• pp.81-86
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• 2016

Recently, increasing security threats are not only interfering with business continuity of companies but they are al so causing serious problems on social and national levels. As violation of intellectual property rights increases due to growing competition between different companies and countries, companies are now required to follow various IT compliance regulations, under relevant legal obligations. This study proposed a model of convergence security compliance management by using machine learning, in order to help companies actively utilize IT compliance.

• KEPCO Journal on Electric Power and Energy
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• v.6 no.4
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• pp.399-404
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• 2020

There are many attempts to apply AI technology to diagnose facilities or improve the work efficiency of the power industry. The emergence of new machine learning technologies, such as deep learning, is accelerating the digital transformation of the power sector. The problem is that traditional power systems face security risks when adopting state-of-the-art AI systems. This adoption has convergence characteristics and reveals new cybersecurity threats and vulnerabilities to the power system. This paper deals with the security measures and implementations of the power system using machine learning. Through building a commercial facility operations forecasting system using machine learning technology utilizing power big data, this paper identifies and addresses security vulnerabilities that must compensated to protect customer information and power system safety. Furthermore, it provides security guidelines by generalizing security measures to be considered when applying AI.

• Convergence Security Journal
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• v.14 no.6_2
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• pp.45-52
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• 2014

Up to now, the security system in Korea has been developed from the human security system to more efficient mechanical security system. The mechanical security system has its advantages over the human security system in terms of economy and operation. However, as there are weaknesses in the use of mechanical security equipment such as erroneous alarm sounds, the studies have been done to improve them. This paper tries to suggest how to improve the mechanical security system based on the results of the study on the weakness of the mechanical security obtained through AHP technique.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.2
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• pp.498-514
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• 2012

Machine to machine (M2M) communications is the hottest issue in the standardization and industry area, it is also defined as machine-type communication (MTC) in release 10 of the 3rd Generation Partnership Project (3GPP). Recently, most research have focused on congestion control, sensing, computing, and controlling technologies and resource management etc., but there are few studies on security aspects. In this paper, we first introduce the threats that exist in M2M system and corresponding solutions according to 3GPP. In addition, we present several new security issues including group access authentication, multiparty authentication and data authentication, and propose corresponding solutions through modifying existing authentication protocols and cryptographic algorithms, such as group authentication and key agreement protocol used to solve group access authentication of M2M, proxy signature for M2M system to tackle authentication issue among multiple entities and aggregate signature used to resolve security of small data transmission in M2M communications.

• Journal of Korea Multimedia Society
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• v.23 no.11
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• pp.1396-1405
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• 2020

Cyber threats such as forced personal information collection and distribution of malicious codes using malicious URLs continue to occur. In order to cope with such cyber threats, a security technologies that quickly detects malicious URLs and prevents damage are required. In a web environment, malicious URLs have various forms and are created and deleted from time to time, so there is a limit to the response as a method of detecting or filtering by signature matching. Recently, researches on detecting and predicting malicious URLs using machine learning techniques have been actively conducted. Existing studies have proposed various features and machine learning algorithms for predicting malicious URLs, but most of them are only suggesting specialized algorithms by supplementing features and preprocessing, so it is difficult to sufficiently reflect the strengths of various machine learning algorithms. In this paper, a system for predicting malicious URLs using multiple machine learning algorithms was proposed, and an experiment was performed to combine the prediction results of multiple machine learning models to increase the accuracy of predicting malicious URLs. Through experiments, it was proved that the combination of multiple models is useful in improving the prediction performance compared to a single model.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.1
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• pp.17-21
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• 2016

This paper investigates the importance of the computational overhead when machine learning methods, such as SVM, LASSO, AdaBoosting and AdaBagging, are used for automatic security classification.

• Journal of the Korea Convergence Society
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• v.1 no.1
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• pp.49-55
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• 2010

As the developing of the information communication technology, more and more devices are with the capacity of communication and networking. The convergence businesses which communicate with the devices have been developing rapidly. The IT convergence communication is viewed as one of the next frontiers in wireless communications. In this paper, we analyze detailed security threats against M2M(Machine to Machine), intelligent vehicle, smart grid and u-Healthcare in IT convergence architecture. We proposed a direction of the IT convergence security that imbedded system security, forensic security, user authentication and key management scheme.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.342-347
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• 2022

On Cloud, the important data of the user that is protected on remote servers can be accessed via internet. Due to rapid shift in technology nowadays, there is a swift increase in the confidential and pivotal data. This comes up with the requirement of data security of the user's data. Data is of different type and each need discrete degree of conservation. The idea of data security data science permits building the computing procedure more applicable and bright as compared to conventional ones in the estate of data security. Our focus with this paper is to enhance the safety of data on the cloud and also to obliterate the problems associated with the data security. In our suggested plan, some basic solutions of security like cryptographic techniques and authentication are allotted in cloud computing world. This paper put your heads together about how machine learning techniques is used in data security in both offensive and defensive ventures, including analysis on cyber-attacks focused at machine learning techniques. The machine learning technique is based on the Supervised, UnSupervised, Semi-Supervised and Reinforcement Learning. Although numerous research has been done on this topic but in reference with the future scope a lot more investigation is required to be carried out in this field to determine how the data can be secured more firmly on cloud in respect with the Machine Learning Techniques and cryptographic methods.

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