• 한국멀티미디어학회논문지
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• 제10권12호
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• pp.1726-1732
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• 2007

The rapid proliferation of wireless networks and mobile computing applications has changed the landscape of network security. Anomaly detection is a pattern recognition task whose goal is to report the occurrence of abnormal or unknown behavior in a given system being monitored. This paper presents an efficient rough set based anomaly detection method that can effectively identify a group of especially harmful internal attackers - masqueraders in cellular mobile networks. Our scheme uses the trace data of wireless application layer by a user as feature value. Based on this, the used pattern of a mobile's user can be captured by rough sets, and the abnormal behavior of the mobile can be also detected effectively by applying a roughness membership function with the age of the user profile. The performance of the proposed scheme is evaluated by using a simulation. Simulation results demonstrate that the anomalies are well detected by the proposed scheme that considers the age of user profiles.

• Journal of the Korean Data and Information Science Society
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• 제18권2호
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• pp.315-326
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• 2007

The rapid proliferation of wireless networks and mobile computing applications has changed the landscape of network security. Anomaly detection is a pattern recognition task whose goal is to report the occurrence of abnormal or unknown behavior in a given system being monitored. This paper presents a dynamic anomaly detection scheme that can effectively identify a group of especially harmful internal masqueraders in cellular mobile networks. Our scheme uses the trace data of wireless application layer by a user as feature value. Based on the feature values, the use pattern of a mobile's user can be captured by rough sets, and the abnormal behavior of the mobile can be also detected effectively by applying a roughness membership function with both the age of the user profile and weighted feature values. The performance of our scheme is evaluated by a simulation. Simulation results demonstrate that the anomalies are well detected by the proposed dynamic scheme that considers the age of user profiles.

• 디지털산업정보학회논문지
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• 제16권1호
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• pp.67-78
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• 2020

Typical security solutions such as intrusion detection system are not suitable for detecting advanced persistent attack(APT), because they cannot draw the big picture from trivial events of security solutions. Researches on techniques for detecting multiple stage attacks by analyzing the correlations between security events or alerts are being actively conducted in academic field. However, these studies still use events from existing security system, and there is insufficient research on the structure of the entire security system suitable for advanced persistent attacks. In this paper, we propose an attack path and intention recognition system suitable for multiple stage attacks like advanced persistent attack detection. The proposed system defines the trace format and overall structure of the system that detects APT attacks based on the correlation and behavior analysis, and is designed with a structure of detection system using deep learning and big data technology, etc.

• International Journal of Computer Science & Network Security
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• 제21권4호
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• pp.131-139
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• 2021

In current time, anomaly detection is the primary concern of the administrative authorities. Suspicious activity identification is shifting from a human operator to a machine-assisted monitoring in order to assist the human operator and react to an unexpected incident quickly. These automatic surveillance systems face many challenges due to the intrinsic complex characteristics of video sequences and foreground human motion patterns. In this paper, we propose a novel approach to detect anomalous human activity using a hybrid approach of statistical model and Genetic Programming. The feature-set of local motion patterns is generated by a statistical model from the video data in an unsupervised way. This features set is inserted to an enhanced Genetic Programming based classifier to classify normal and abnormal patterns. The experiments are performed using publicly available benchmark datasets under different real-life scenarios. Results show that the proposed methodology is capable to detect and locate the anomalous activity in the real time. The accuracy of the proposed scheme exceeds those of the existing state of the art in term of anomalous activity detection.

• 전자공학회논문지SC
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• 제48권5호
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• pp.25-30
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• 2011

본 논문은 교통 감시를 수행하는 고정 카메라에서, 움직이는 물체들의 궤적을 사용자가 입력한 사용자 지정 경로를 바탕으로 그 정상/비정상성을 판별하는 방법을 제안한다. 제안된 방법은 입력된 경로 정보를 미리 정해진 규칙에 따라 각각의 이동 물체에 대한 비정상성(abnormality)을 계산하고 이를 임계값(Threshold)과 비교하여 비정상 행위를 판별해낸다. 사용자의 경로 정보 입력 기능을 이용하기 때문에 기존의 방법들에서 사용한, 계산량과 시간 소모가 크며 학습 데이터에 의해 그 성능이 크게 영향을 받는 정상 행위 (normal behavior) 모델링 단계를 배제하여 보다 빠르고 정확한 판별 결과를 제공한다. 뿐만 아니라 단순히 지정된 규칙만을 이용하지 않고 주어진 환경에 따라 규칙을 변형 적용하여 보다 강인한 판별 결과를 제공한다. 실험 결과는 본 논문에서 제안한 방법이 각종 교통 상황에서 발생하는 불법 및 비정상 교통 행위를 강인하게 판별해 냄을 보여준다.

• IEIE Transactions on Smart Processing and Computing
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• 제5권2호
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• pp.94-99
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• 2016

Application layer attacks have for years posed an ever-serious threat to network security, since they always come after a technically legitimate connection has been established. In recent years, cyber criminals have turned to fully exploiting the web as a medium of communication to launch a variety of forbidden or illicit activities by spreading malicious automated software (auto-ware) such as adware, spyware, or bots. When this malicious auto-ware infects a network, it will act like a robot, mimic normal behavior of web access, and bypass the network firewall or intrusion detection system. Besides that, in a private and large network, with huge Hypertext Transfer Protocol (HTTP) traffic generated each day, communication behavior identification and classification of auto-ware is a challenge. In this paper, based on a previous study, analysis of auto-ware communication behavior, and with the addition of new features, a method for classification of HTTP auto-ware communication is proposed. For that, a Not Only Structured Query Language (NoSQL) database is applied to handle large volumes of unstructured HTTP requests captured every day. The method is tested with real HTTP traffic data collected through a proxy server of a private network, providing good results in the classification and detection of suspicious auto-ware web access.

• 제어로봇시스템학회논문지
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• 제9권12호
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• pp.1009-1018
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• 2003

In a park or street, we can see many people Jogging or walking with their dogs chasing their masters. In the previous study, an entertainment robot, CARTRI that imitates the dog's behavior was created. The robot's task was chasing a moving goal that was recognized as the master. The physical structure of the CARTRI robot was three-wheel type locomotion system. The sensor system which could detect the position of the master in the outdoor space, was consists of a signal transmitter which was held by the master and five ultrasonic receivers which were mounted on the robot. In the experiment, the robot could chase a human walking in outdoor space like a park. But it could not avoid obstacles and its behavior was only goal-chasing behavior because of the limit of the sensor system. In this study, an improved RF-ultrasonic sensor system which can detect both goal and obstacle is developed in order to enable the CARTRI robot to carry out various behavior. The sensor system has increased angle resolution by using eight ultrasonic receivers instead of five in the previous study. And it can detect obstacle by using reflective type ultrasonic sensors. The sensor system is designed so that detection of goal and obstacle could be conducted in one sampling period. The Performance of the developed sensor system is evaluated through experiments.

• Journal of Information Processing Systems
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• 제12권3호
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• pp.489-501
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• 2016

As interest in the Internet increases, related technologies are also quickly progressing. As smart devices become more widely used, interest is growing in words are missing here like "improving the" or "figuring out how to use the" future Internet to resolve the fundamental issues of transmission quality and security. The future Internet is being studied to improve the limits of existing Internet structures and to reflect new requirements. In particular, research on words are missing here like "finding new forms of" or "applying new forms of" or "studying various types of" or "finding ways to provide more" reliable communication to connect the Internet to various services is in demand. In this paper, we analyze the security threats caused by malicious activities in the future Internet and propose a human behavior analysis-based security service model for malware detection and intrusion prevention to provide more reliable communication. Our proposed service model provides high reliability services by responding to security threats by detecting various malware intrusions and protocol authentications based on human behavior.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2021년도 춘계학술대회
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• pp.25-27
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• 2021

최근 딥러닝 기술의 발전으로 CCTV 카메라를 통해 획득한 영상 정보에서 객체의 이상행동을 분석하기 위한 컴퓨터 비전 기반 AI 기술들이 연구되었다. 위험 지역이나 보안 지역에는 범죄 예방 및 경계 감시를 위해 감시카메라가 설치되어 있는 경우가 다수 존재한다. 이러한 이유로 기업들에서는 감시카메라 환경에서 침입, 배회, 낙상, 폭행 같은 주요한 상황을 판단하기 위한 연구들이 진행되고 있다. 본 논문에서는 객체 검출 및 추적 방법을 사용한 실시간 이상 행위 분석 알고리즘을 제안한다.

• 산업융합연구
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• 제21권12호
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• pp.55-62
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• 2023

최근 등장하는 랜섬웨어들은 다양한 공격 기법과 다양한 경로를 통해 공격을 수행하고 있어 조기 탐지와 방어에 많은 어려움을 겪고 있으며, 그 피해 규모도 날로 증가하고 있다. 따라서 본 논문에서는 효과적인 랜섬웨어 탐지를 위하여 파일 암호화와 암호화 패턴을 머신러닝 기반으로 하는 감지 기법을 제안한다. 파일 암호화는 랜섬웨어가 공격하는데 필수적으로 사용하는 기능으로 암호 행위와 암호화 패턴을 분석함으로써 랜섬웨어를 탐지하고 랜섬웨어의 특정 변종이나 새로운 유형의 랜섬웨어를 탐지할 수 있기 때문에 랜섬웨어 공격을 식별하고 차단하는 데 매우 효과적이다. 제안한 머신러닝 기반의 암호화 행위 감지 기법은 암호화 특성과 암호화 패턴 특성을 추출하여 머신러닝 기반의 분류기를 통해 각각 학습을 시켜 해당 행위에 대한 탐지를 진행하고 최종 결과는 두 분류기의 평가 결과를 기반으로 앙상블 분류기에서 랜섬웨어 유무를 판별하여 좀 더 정확도를 높였다. 또한, 제안한 기법을 numpy와 pandas, 파이썬의 사이킷런 라이브러리를 사용하여 구현하여 평가지표를 사용한 성능를 평가한 결과 평균적으로 94%,의 정확도와 95%의 정밀도, 93%의 재현률과 95%의 F1 스코어가 산출되었다. 성능 평가 결과를 보면 암호화 행위 감지를 통해 랜섬웨어 탐지가 가능하다는 것을 확인할 수 있었고 랜섬웨어의 사전 탐지를 위해 제안한 기법의 성능을 높이기 위한 연구도 계속해서 진행되어야 한다.