• Journal of the Korea Institute of Information Security & Cryptology
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• v.13 no.2
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• pp.99-106
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• 2003

Intrusions pose a serious security risk in a network environment. For detecting the intrusion effectively, many researches have developed data mining framework for constructing intrusion detection modules. Traditional anomaly detection techniques focus on detecting anomalies in new data after training on normal data. To detect anomalous behavior, Precise normal Pattern is necessary. This training data is typically expensive to produce. For this, the understanding of the characteristics of data on network is inevitable. In this paper, we propose to use clustering and association rules as the basis for guiding anomaly detection. For applying entropy to filter noisy data, we present a technique for detecting anomalies without training on normal data. We present dynamic transaction for generating more effectively detection patterns.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.16 no.3
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• pp.65-76
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• 2006

This paper deals with an effective algerian aimed at improving the port scan detection in an intrusion detection system (IDS). In particular, a detection correlation algerian is proposed to maximize the detection capability in the network-based IDS whereby the 'misuse' is flagged for analysis to establish intrusion profile in relation to the overall port scan detection process. In addition, we establish an appropriate system maintenance policy for port scan detection as preprocessor for improved port scan in IDS, thereby achieving minimum false positive in the misuse detection engine while enhancing the system performance.

• Journal of Aerospace System Engineering
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• v.15 no.1
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• pp.95-101
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• 2021

The detection sensitivity of a Walk Through Metal Detector (WTMD) currently being developed and operated in Korea differs from one manufacturer to another, making it difficult for them to be used based on Aviation Security level. In addition, the FAA 3-GUN Test approved by the domestic aviation authority for aviation security supervision is a single test object. There is no Operational Test Piece (OTP) consisting of multiple test objects for the operation of aviation security for a WTMD. This paper, the detection sensitivity of a WTMD applied by a commercial OTP was measured and detection sensitivity standards for a WTMD were developed based on results of measurement. Furthermore, institutional plans to maintain the same detection sensitivity for domestic aviation security were made through suggestions for Korean standards OTP development methods, taking characteristics of the aviation field into consideration.

• The Journal of Society for e-Business Studies
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• v.27 no.1
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• pp.1-13
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• 2022

With the development of network technologies, the security to protect organizational resources from internal and external intrusions and threats becomes more important. Therefore in recent years, the anomaly detection algorithm that detects and prevents security threats with respect to various security log events has been actively studied. Security anomaly detection algorithms that have been developed based on rule-based or statistical learning in the past are gradually evolving into modeling based on machine learning and deep learning. In this study, we propose a deep-autoencoder model that transforms LSTM-autoencoder as an optimal algorithm to detect insider threats in advance using various machine learning analysis methodologies. This study has academic significance in that it improved the possibility of adaptive security through the development of an anomaly detection algorithm based on unsupervised learning, and reduced the false positive rate compared to the existing algorithm through supervised true positive labeling.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.04a
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• pp.343-346
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• 2000

In this paper, a novel approach to intruder detection is introduced. The approach, based on the genetic algorithms, improved detection rate of the host systems which has traditionally relied on known intruder patterns and host addresses. Rather than making judgments on whether the access is instrusion or not, the systems can continuously monitor systems with categorized security level. With the categorization, when the intruder attempts repeatedly to access the systems, the security level is incrementally escalated. In the simulation of a simple intrusion, it was shown that the current approach improves robustness of the security systems by enhancing detection and flexibility. The evolutionary approach to intruder detection enhances adaptability of the system.

• Convergence Security Journal
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• v.6 no.3
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• pp.87-95
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• 2006

An intrusion can be defined as any set of actors that attempt to compromise the integrity, confidentiality and availability of computer resource and destroy the security policy of computer system. The Intrusion Detection System that detects the intrusion consists of data collection, data reduction, analysis and detection, and report and response. It is important for feature selection to detect the intrusion efficiently after collecting the large set of data of Intrusion Detection System. In this paper, the feature selection method using Genetic Algorithm and Decision Tree is proposed. Also the method is verified by the simulation with KDD data.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.1
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• pp.93-98
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• 2012

MANET composed of only moving nodes is concerned to core technology to construct ubiquitous computing environment. Also, it is a lack of security because of no middle infrastructure. So, it is necessary to intrusion detection system which can track malicious attack. In this study, cluster was used to stable intrusion detection, and rule about various attacks was defined to detect accurately attack that seems like network problem. Proposed method through experience was confirmed that stable detection rate was showed although number of nodes increase.

• International Journal of Computer Science & Network Security
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• v.22 no.12
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• pp.1-12
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• 2022

Besides unexpected growth perceived by IoT's, the variety and volume of threats have increased tremendously, making it a necessity to introduce intrusion detections systems for prevention and detection of such threats. But Intrusion Detection and Prevention System (IDPS) inside the IoT network yet introduces some unique challenges due to their unique characteristics, such as privacy inference, performance, and detection rate and their frequency in the dynamic networks. Our research is focused on the privacy inferences of existing intrusion prevention and detection system approaches. We also tackle the problem of providing unified a solution to implement the open-source IDPS in the IoT architecture for assessing the performance of IDS by calculating; usage consumption and detection rate. The proposed scheme is considered to help implement the human health monitoring system in IoT networks

• Convergence Security Journal
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• v.8 no.3
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• pp.25-31
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• 2008

SELinux, security operating system, is the security system which implements mandatory access control using linux security module on the traditional linux kernel supporting discretionary access control. But intrusion detection and logging are lacked when system intrusions are happened. This study proposes a SELinux security kernel which performs detection of access violation and privilege restriction using dynamic access control. It detects the intrusion using security check when the abnormal access of system is happened, and dynamically changes the system privilege for the intruder through privilege restriction. Finally we prevent reintrusion and explain the result of experiment.

• Journal of information and communication convergence engineering
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• v.18 no.1
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• pp.16-21
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• 2020

Owing to the convergence of the communication network with the control system and public network, security threats, such as information leakage and falsification, have become possible through various routes. If we examine closely at the security type of the current control system, the operation of the security system focuses on the threats made from outside to inside, so the study on the detection system of the security threats conducted by insiders is inadequate. Thus, this study, based on "Spotting the Adversary with Windows Event Log Monitoring," published by the National Security Agency, found that event logs can be utilized for the detection and maneuver of threats conducted by insiders, by analyzing the validity of detecting insider threats to the control system with the list of important event logs.