• Title/Summary/Keyword: Security Behavior

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SIP DDoS Detection Scheme based-on Behavior (행위기반 SIP DDoS 트래픽 탐지 기법)

  • Lee, Changyong;Kim, Hwankuk;Ko, Kyunghee;Kim, Jeongwook;Jeong, Hyuncheol
    • Proceedings of the Korea Information Processing Society Conference
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    • 2010.11a
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    • pp.1285-1288
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    • 2010
  • SIP 프로토콜은 멀티미디어 통신 세션을 생성, 삭제, 변경할 수 있는 프로토콜로 높은 간결성, 확장성 등 장점을 가지고 있다. 최근 인터넷전화의 대부분이 SIP 프로토콜을 사용하는 등 SIP 프로토콜의 사용이 많이 보편화 되었으나 그만큼 보안에 대한 위협 또한 중요한 문제가 되고있다. SIP는 응용계층 프로토콜로, 기존의 IP기반 보안 기술로는 공격 탐지/차단에 한계가 있을 수 있어 SIP 전용의 보안 기술 및 장비의 개발이 필요하다. 본 논문에서는 SIP 트래픽의 응용계층 정보 통계를 통하여 DDoS 공격트래픽 행위 특성을 분석하고 이를 정상 트래픽과 구분, 탐지하는 탐지 기법을 제안한다. 제안된 기법은 자체 테스트 망 구축과 SIP DDoS 공격 에뮬레이션을 통해 검증한다.

Abnormal Behavior Detection for Zero Trust Security Model Using Deep Learning (제로트러스트 모델을 위한 딥러닝 기반의 비정상 행위 탐지)

  • Kim, Seo-Young;Jeong, Kyung-Hwa;Hwang, Yuna;Nyang, Dae-Hun
    • Proceedings of the Korea Information Processing Society Conference
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    • 2021.05a
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    • pp.132-135
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    • 2021
  • 최근 네트워크의 확장으로 인한 공격 벡터의 증가로 외부자뿐 아니라 내부자를 경계해야 할 필요성이 증가함에 따라, 이를 다룬 보안 모델인 제로트러스트 모델이 주목받고 있다. 이 논문에서는 reverse proxy 와 사용자 패턴 인식 AI 를 이용한 제로트러스트 아키텍처를 제시하며 제로트러스트의 구현 가능성을 보이고, 새롭고 효율적인 전처리 과정을 통해 효과적으로 사용자를 인증할 수 있음을 제시한다. 이를 위해 사용자별로 마우스 사용 패턴, 리소스 사용 패턴을 인식하는 딥러닝 모델을 설계하였다. 끝으로 제로트러스트 모델에서 사용자 패턴 인식의 활용 가능성과 확장성을 보인다.

Enhancing cloud computing security: A hybrid machine learning approach for detecting malicious nano-structures behavior

  • Xu Guo;T.T. Murmy
    • Advances in nano research
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    • v.15 no.6
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    • pp.513-520
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    • 2023
  • The exponential proliferation of cutting-edge computing technologies has spurred organizations to outsource their data and computational needs. In the realm of cloud-based computing environments, ensuring robust security, encompassing principles such as confidentiality, availability, and integrity, stands as an overarching imperative. Elevating security measures beyond conventional strategies hinges on a profound comprehension of malware's multifaceted behavioral landscape. This paper presents an innovative paradigm aimed at empowering cloud service providers to adeptly model user behaviors. Our approach harnesses the power of a Particle Swarm Optimization-based Probabilistic Neural Network (PSO-PNN) for detection and recognition processes. Within the initial recognition module, user behaviors are translated into a comprehensible format, and the identification of malicious nano-structures behaviors is orchestrated through a multi-layer neural network. Leveraging the UNSW-NB15 dataset, we meticulously validate our approach, effectively characterizing diverse manifestations of malicious nano-structures behaviors exhibited by users. The experimental results unequivocally underscore the promise of our method in fortifying security monitoring and the discernment of malicious nano-structures behaviors.

Behavior and Motor Skill of Children with Intellectual Disabilities Participating Functional Games (지적장애아동의 기능성 게임 참여에 따른 행동변화 및 운동수행능력)

  • Kang, Sunyoung
    • Convergence Security Journal
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    • v.15 no.4
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    • pp.149-154
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    • 2015
  • The aim of this study is to suggest the change of behavior and motor skill of children with intellectual disabilities participating functional games using virtual reality. For this purpose, 5 children with intellectual disabilities completed a 16-week functional game program twice a week. The result was as following; the application of functional games using virtual reality has positive effect on behavior and motor skill -static coordination, hand motion coordination, normal motion coordination, motor speed, simultaneous spontaneous motion, single motion competency. The application and utilization of functional games using virtual reality systematically of children with intellectual disabilities can bring an improvement on their overall development.

Detecting Android Malware Based on Analyzing Abnormal Behaviors of APK File

  • Xuan, Cho Do
    • International Journal of Computer Science & Network Security
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    • v.21 no.6
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    • pp.17-22
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    • 2021
  • The attack trend on end-users via mobile devices is increasing in both the danger level and the number of attacks. Especially, mobile devices using the Android operating system are being recognized as increasingly being exploited and attacked strongly. In addition, one of the recent attack methods on the Android operating system is to take advantage of Android Package Kit (APK) files. Therefore, the problem of early detecting and warning attacks on mobile devices using the Android operating system through the APK file is very necessary today. This paper proposes to use the method of analyzing abnormal behavior of APK files and use it as a basis to conclude about signs of malware attacking the Android operating system. In order to achieve this purpose, we propose 2 main tasks: i) analyzing and extracting abnormal behavior of APK files; ii) detecting malware in APK files based on behavior analysis techniques using machine learning or deep learning algorithms. The difference between our research and other related studies is that instead of focusing on analyzing and extracting typical features of APK files, we will try to analyze and enumerate all the features of the APK file as the basis for classifying malicious APK files and clean APK files.

Understanding Driver Compliance Behaviour at Signalised Intersection for Developing Conceptual Model of Driving Simulation

  • Aznoora Osman;Nadia Abdul Wahab;Haryati Ahmad Fauzi
    • International Journal of Computer Science & Network Security
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    • v.24 no.3
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    • pp.142-150
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    • 2024
  • A conceptual model represents an understanding of a system that is going to be developed, which in this research, a driving simulation software to study driver behavior at signalised intersections. Therefore, video observation was conducted to study driver compliance behaviour within the dilemma zone at signalised intersection, with regards to driver's distance from the stop line during yellow light interval. The video was analysed using Thematic Analysis and the data extracted from it was analysed using Chi-Square Independent Test. The Thematic Analysis revealed two major themes which were traffic situation and driver compliance behaviour. Traffic situation is defined as traffic surrounding the driver, such as no car in front and behind, car in front, and car behind. Meanwhile, the Chi-Square Test result indicates that within the dilemma zone, there was a significant relationship between driver compliance behaviour and driver's distance from the stop line during yellow light interval. The closer the drivers were to the stop line, the more likely they were going to comply. In contrast, drivers showed higher non-compliant behavior when further away from stop line. This finding could help in the development of conceptual model of driving simulation with purpose in studying driver behavior.

The Organizational Citizenship Behavior and Organizational Effectiveness of Hospital Employees (병원근로자의 조직시민행동과 조직효과성 관계 연구)

  • Kim, Sung Ho;Kim, Jang Mook;Seo, Young Joon
    • Health Policy and Management
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    • v.24 no.2
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    • pp.191-202
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    • 2014
  • Background: The organizational citizenship behavior is generally known as the important factor relevant to the organizational effectiveness. This research examined the mediating effect of the organizational citizenship behavior of hospital employees on the organizational effectiveness. Methods: Data were collected from 1,112 employees located in city of Seoul, Kyunggi and Chungnam province through self-administered questionnaires. Collected data were analyzed using IBM SPSS ver. 20.0, frequency analysis, t-test, analysis of variance, regression analysis, and path analysis. The main findings of the study are as follows. Results: First, it was found that many characteristics variables of personality, job, and relationship together affected organizational citizenship behavior of hospital employees. Especially, the following variables of negative affectivity, desire for growth, job value, job significance, and job security were found to have significant effect on the organizational citizenship behavior of hospital employees. Second, the results of path analysis showed that, through the mediating effect of organizational citizenship behavior, personality variables of positive and negative affectivity, and desire for growth, job characteristics variables of job value, job significance, and job security, and relationship variables of organizational support and task interdependence, had significant total effects on the level of job satisfaction of hospital employees. Conclusion: As a result, the organizational citizenship behavior seems to have both direct and indirect effects on the organizational effectiveness of hospital employees. Based on above findings, some theoretical and practical implications were discussed.

Analyzing Gifted Students' Social Behavior on Social Media at COVID-19 Quarantine

  • Khayyat, Mashael;Sulaimani, Mona;Bukhri, Hanan;Alamiri, Faisal
    • International Journal of Computer Science & Network Security
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    • v.22 no.9
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    • pp.7-14
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    • 2022
  • COVID-19 has caused a global disturbance, increased anxiety, and panic, eliciting diverse reactions. While its cure has not been discovered, new infection cases and fatalities are being recorded daily. The focus of the present study was to analyze the reaction of gifted undergraduate students on social media during the quarantine period of the COVID-19. A special group of gifted students, who joined the program of attracting and nurturing talents at the University of Jeddah, University students as were the target sample of this study. To analyze online reactions during the pandemic; the choice of university students was arrived at as they are perceived to be gifted academically. Hence, the analysis of the impacts on their behavior on social media use is imperative. This study presented accurate and consistent data on the effects of social media using Twitter platforms on gifted students during the quarantine occasioned by the COVID-19 pandemic. The behavior of learners due to during the use of social media was extensively explored and results analyzed. The study was carried out between April and May 2020 (quarantine period in Saudi Arabia) to establish whether the online behavior of gifted students reflects positive or negative feelings. The methods used in conducting this study the research were online interviews and scraping participants' Twitter accounts (where most of the online activities and studies take place). The study employed the Activity theory to analyze the behavior of gifted students on social media. The sample size used was 60 students, and the analysis of their behavior was based on Activity theory Overall, the results showed proactive, positive behavior for coping with a challenging situation, educating society, and entertaining. Finally, this study recommends investing in gifted students due to their valuable problem-solving skills that can help handle global pandemics efficiently.

Real-time Abnormal Behavior Detection System based on Fast Data (패스트 데이터 기반 실시간 비정상 행위 탐지 시스템)

  • Lee, Myungcheol;Moon, Daesung;Kim, Ikkyun
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.25 no.5
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    • pp.1027-1041
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    • 2015
  • Recently, there are rapidly increasing cases of APT (Advanced Persistent Threat) attacks such as Verizon(2010), Nonghyup(2011), SK Communications(2011), and 3.20 Cyber Terror(2013), which cause leak of confidential information and tremendous damage to valuable assets without being noticed. Several anomaly detection technologies were studied to defend the APT attacks, mostly focusing on detection of obvious anomalies based on known malicious codes' signature. However, they are limited in detecting APT attacks and suffering from high false-negative detection accuracy because APT attacks consistently use zero-day vulnerabilities and have long latent period. Detecting APT attacks requires long-term analysis of data from a diverse set of sources collected over the long time, real-time analysis of the ingested data, and correlation analysis of individual attacks. However, traditional security systems lack sophisticated analytic capabilities, compute power, and agility. In this paper, we propose a Fast Data based real-time abnormal behavior detection system to overcome the traditional systems' real-time processing and analysis limitation.

The Value, Knowledge, and Sustainable Consumption Behavior of Fashion Consumers (패션 소비자의 가치, 지식 및 지속가능한 소비행동에 관한 연구)

  • Suk, HyoJung;Lee, Eun-Jin
    • Journal of the Korean Society of Clothing and Textiles
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    • v.37 no.3
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    • pp.424-438
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    • 2013
  • This study examines the value, knowledge and sustainable consumption behavior of fashion consumers. The study shows that universalism/harmony, security/benevolence, power, and tradition/faith have positive effects on buying/usage behavior; however, hedonism/wealth has a negative effect. Stimulation/self-direction and universalism/harmony positively influence boycott behavior; however, power has a negative influence. Universalism/harmony and politeness have positive impacts on care/disposing behavior. Consumer knowledge about fashion related environmental problems, labor practices, ethical issues and sustainable environment knowledge influence sustainable buying/usage behavior; in addition, knowledge about ethical issues and sustainable environmental problems positively influence boycott and care/disposing behavior. Moreover, there were significant differences in values, knowledge, and consumption behavior by age.