• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.358-366
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• 2022

Wireless sensor networks (WSN) are becoming widely used in collecting and sensing information in different fields such as in the medical area, smart phone industry and military environment. The main concern here is reducing the power consumption because it effects in the lifetime of wireless sensor during commutation because it may be work in some environment like sensor in the battlefields where is not easy to change the battery for a node and that may decrease the efficiency of that node and that may affect the network traffic may be interrupted because one or more nodes stop working. In this paper we implement, simulate, and investigate S-MAC protocol with mobility support and show the sequence of events the sender and receiver go through. We tested some parameters and their impacts of on the performance including System throughput, number of packets successfully delivered per second, packet delay, average packet delay before successful transmission.

• International Journal of Computer Science & Network Security
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• v.24 no.8
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• pp.61-71
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• 2024

The dynamic nature of Online Social Networks (OSNs), especially on platforms like Twitter, presents challenges in identifying and responding to community attacks, particularly within Arabic content. The proposed integrated system addresses these challenges by achieving 91% accuracy in detecting real-time community event attacks while efficiently managing computational costs. This is accomplished through the use of specialized integrated approach clustering to detect both major and minor attacks. Additionally, the system leverages clustering algorithms, temporal modules, and social network graphs to identify events, map communities, and analyze online dynamics. An extensive parameter sensitivity analysis was conducted to fine-tune the algorithm, and the system's effectiveness was validated using a benchmark dataset, demonstrating substantial improvements in event detection.

• Korean Security Journal
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• no.58
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• pp.195-213
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• 2019

「Security Services Industry Act」made in 1976 for the purpose of contributing to the good operation of services industry by establishing the requirements for promotion, development and systematic management of services industry has been since 26 times of overall revisions. Period division of private security through overall revision of 「Security Services Industry Act」 can be roughly divided into three periods. The first is a settlement period. It is a period when the present five business areas were completed rom 1976 to 2001. Beginning with facility and escort security in 1996, five types of jobs have been prepared. so far by adding personal protection in 1996, and machine and special security in 2001. The second is a quantitative growth period. It is a period when quantitative development is made on the institutional basis. As various culture, sports and arts events are increasing based on the demand of security services due to an increase in the people's sense of security, quantitative development was made, which additionally influenced the downward trend of capital and the possibility of having two jobs through revisions. The third is a qualitative growth period. From 2013 to the present is a period when quantitative growth has slowed and qualitative growth has been made. After the period of quantitative growth, the growth slowed down. However, by raising the criteria for permission of services industry through creation of a rule for the group civil petition, by raising capital, by institutionally compensating for diverse punishment regulations, by realizing the training hours for new security guards, by permitting private education, etc, the reliability and professionality of a security guard job has been to be recovered.

• Journal of Information Processing Systems
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• v.10 no.1
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• pp.1-22
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• 2014

Recent technological advances provide the opportunity to use large amounts of multimedia data from a multitude of sensors with different modalities (e.g., video, text) for the detection and characterization of criminal activity. Their integration can compensate for sensor and modality deficiencies by using data from other available sensors and modalities. However, building such an integrated system at the scale of neighborhood and cities is challenging due to the large amount of data to be considered and the need to ensure a short response time to potential criminal activity. In this paper, we present a system that enables multi-modal data collection at scale and automates the detection of events of interest for the surveillance and reconnaissance of criminal activity. The proposed system showcases novel analytical tools that fuse multimedia data streams to automatically detect and identify specific criminal events and activities. More specifically, the system detects and analyzes series of incidents (an incident is an occurrence or artifact relevant to a criminal activity extracted from a single media stream) in the spatiotemporal domain to extract events (actual instances of criminal events) while cross-referencing multimodal media streams and incidents in time and space to provide a comprehensive view to a human operator while avoiding information overload. We present several case studies that demonstrate how the proposed system can provide law enforcement personnel with forensic and real time tools to identify and track potential criminal activity.

• Wind and Structures
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• v.39 no.1
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• pp.1-14
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• 2024

Widespread damages from extreme winds have attracted lots of attentions of the resilience assessment of power distribution systems. With many related environmental parameters as well as numerous power infrastructure components, such as poles and wires, the increased challenge of power asset management before, during and after extreme events have to be addressed to prevent possible cascading failures in the power distribution system. Many extreme winds from weather events, such as hurricanes, generate widespread damages in multiple areas such as the economy, social security, and infrastructure management. The livelihoods of residents in the impaired areas are devastated largely due to the paucity of vital utilities, such as electricity. To address the challenge of power grid asset management, power system clustering is needed to partition a complex power system into several stable clusters to prevent the cascading failure from happening. Traditionally, system clustering uses the Binary Decision Diagram (BDD) to derive the clustering result, which is time-consuming and inefficient. Meanwhile, the previous studies considering the weather hazards did not include any detailed weather-related meteorologic parameters which is not appropriate as the heterogeneity of the parameters could largely affect the system performance. Therefore, a fragility-based network hierarchical spectral clustering method is proposed. In the present paper, the fragility curve and surfaces for a power distribution subsystem are obtained first. The fragility of the subsystem under typical failure mechanisms is calculated as a function of wind speed and pole characteristic dimension (diameter or span length). Secondly, the proposed fragility-based hierarchical spectral clustering method (F-HSC) integrates the physics-based fragility analysis into Hierarchical Spectral Clustering (HSC) technique from graph theory to achieve the clustering result for the power distribution system under extreme weather events. From the results of vulnerability analysis, it could be seen that the system performance after clustering is better than before clustering. With the F-HSC method, the impact of the extreme weather events could be considered with topology to cluster different power distribution systems to prevent the system from experiencing power blackouts.

• Journal of the Korea Society of Computer and Information
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• v.25 no.9
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• pp.143-150
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• 2020

The purpose of this study is to investigate the human error of bodyguards caused in the process of performing access control activities between security missions, focusing on multiple risk cases, and to suggest countermeasures accordingly. To verify this, after arranging the sequence of events in a time series, the VTA technique and Why-Why analysis technique that can easily identify the problem centered on the variable node were used. In addition, environmental factors and personal factors that cause human errors were extracted through M-SHEL Metrix. As a result of analyzing multiple risk cases through such a method, the security environment factors that cause access control accidents include lack of time (impatience), prejudice against visitors, intensive work methods, lack of security management, unattended travel, and familiar atmosphere. (Relaxation), formal work activities, convenience provision, and underestimation were surveyed. In addition, human errors caused by personal security guards were investigated as low alertness, formal work, negligence of inspection, and comfortable coping.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.227-230
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• 2013

Financial transactions using a mobile terminal and the Internet is activated, it is a stock exchange enabled using mobile devices and the Internet. Koscom in charge of IT operations of securities transaction-related in (securities ISAC), to analyze the vulnerability of information security related to securities transactions, which corresponds to running the integrated security control system. Online stock trading is a subject to the Personal Information Protection Act, electronic systems of related, has been designated as the main information and communication infrastructure to, damage financial carelessness of the user, such as by hacking is expected to are. As a result, research on the key vulnerabilities of information security fields related to securities business cancer decoding of the Securities and Exchange packet, through the analysis of security events and integrated security control is needed.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.2
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• pp.267-279
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• 2017

This study emulated unscheduled phishing e-mails over a long period of time by imitating the manner in which external hackers attacked a group of employees in a company. We then measured and analyzed the recipient's ability to identify and respond to phishing e-mails as training progressed. In addition, we analyzed the changes in participants' response behavior when changing the external control condition between the training. As a result of the analysis, it was confirmed that the training duration had a positive (+) relationship with the employees' ability to identify phishing e-mails and the infection rate, and more employees read emails and infected with phishing attacks using social issues and seasonal events. It was also confirmed that reinforcement of internal control policy on infected persons affects positively (+) on the phishing attack response behavior of employees. Based on these results, we would like to suggest the right training method for each organization to enhance the ability of employees to cope with phishing attacks.

• Convergence Security Journal
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• v.20 no.3
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• pp.53-60
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• 2020

At the present stage of the fourth industrial revolution, machine learning and artificial intelligence technologies are rapidly developing, and there is a movement to apply machine learning technology in the security field. Malicious code, including new and transformed, generates an average of 390,000 a day worldwide. Statistics show that security companies ignore or miss 31 percent of alarms. As many malicious codes are generated, it is becoming difficult for humans to detect all malicious codes. As a result, research on the detection of malware and network intrusion events through machine learning is being actively conducted in academia and industry. In international conferences and journals, research on security data analysis using deep learning, a field of machine learning, is presented. have. However, these papers focus on detection accuracy and modify several parameters to improve detection accuracy but do not consider the ratio of dataset. Therefore, this paper aims to reduce the cost and resources of many machine learning research by finding the ratio of dataset that can derive the highest detection accuracy in CNN Mobile net-based malware detection model.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.269-273
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• 2010

7.7 DDoS incident due to recent events and the emerging importance of privacy and Privacy laws are being discussed in the National Assembly. In this study, Legislative Assembly Secretariat support organization focused on using the system, such as the Internet network and the administrative, technical and physical security elements on the status of confidentiality, availability, integrity and security criteria to identify and follow and We are analyzing. In addition, the Internet, including network and use the system primarily for use, Legislative support agency, The National Assembly Secretariat staff awareness about information security and privacy on the survey for compliance with codes of conduct and We are analyzing. Through this analysis of legislative support agencies' security status, and social responsibility as an institution will wish to encourage the role.