• Title/Summary/Keyword: 점증적 특징추출

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A Study on Realtime Intrusion Detection System (실시간 침입탐지 시스템에 관한 연구)

  • Kim, Byoung-Joo
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.9 no.1
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    • pp.40-44
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    • 2005
  • Applying artificial intelligence, machine learning and data mining techniques to intrusion detection system are increasing. But most of researches are focused on improving the performance of classifier. These classifiers are performed by batch way and it is not proper method for realtime intrusion detection system. We propose an incremental feature extraction and classification technique for realtime intrusion detection system. Applying proposed system to KDD CUP 99 data, experimental result shows that it has similar capability compared to batch way intrusion detection system.

Detection of Epileptic Seizure Based on Peak Using Sequential Increment Method (점증적 증가를 이용한 첨점 기반의 간질 검출)

  • Lee, Sang-Hong
    • Journal of Digital Convergence
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    • v.13 no.10
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    • pp.287-293
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    • 2015
  • This study proposed signal processing techniques and neural network with weighted fuzzy membership functions(NEWFM) to detect epileptic seizure from EEG signals. This study used wavelet transform(WT), sequential increment method, and phase space reconstruction(PSR) as signal processing techniques. In the first step of signal processing techniques, wavelet coefficients were extracted from EEG signals using the WT. In the second step, sequential increment method was used to extract peaks from the wavelet coefficients. In the third step, 3D diagram was produced from the extracted peaks using the PSR. The Euclidean distances and statistical methods were used to extract 16 features used as inputs for NEWFM. The proposed methodology shows that accuracy, specificity, and sensitivity are 97.5%, 100%, 95% with 16 features, respectively.

A Study on Developing Intrusion Detection System Using APEX : A Collaborative Research Project with Jade Solution Company (APEX 기반 침입 탐지 시스템 개발에 관한 연구 : (주)제이드 솔류션과 공동 연구)

  • Kim, Byung-Joo
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.10 no.1
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    • pp.38-45
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    • 2017
  • Attacking of computer and network is increasing as information processing technology heavily depends on computer and network. To prevent the attack of system and network, host and network based intrusion detection system has developed. But previous rule based system has a lot of difficulties. For this reason demand for developing a intrusion detection system which detects and cope with the attack of system and network resource in real time. In this paper we develop a real time intrusion detection system which is combination of APEX and LS-SVM classifier. Proposed system is for nonlinear data and guarantees convergence. While real time processing system has its advantages, such as memory efficiency and allowing a new training data, it also has its disadvantages of inaccuracy compared to batch way. Therefore proposed real time intrusion detection system shows similar performance in accuracy compared to batch way intrusion detection system, it can be deployed on a commercial scale.