• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.10a
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• pp.542-547
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• 2003

A network based intrusion detection system(N-IDS), can detect intruders coming in through packets in real time environment. The ability of capture of packet is the most important factor when we evaluate the performance of the system. The time delay between the time handling one packet capture and next one is variant become of packet handling mechanism. So for N-IDS can not settle this problem because most systems use a single processor. In this thesis, we solve the problem of irregular tine delay with a file socket and multi-thread processing. We designed and implement, the Crasto system. By an accurate observation, the performance testing shows that the Crasto reduces the capture delay time to 1/5 comparing to the existing single process N-IDS, and maintain delay time regularly.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.9 no.1
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• pp.71-84
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• 1999

The most of intrusion detection methods do not detect intrusion when it happens. To solve the problem, we are studying a real-time intrusion detection. Because a previous intrusion detection system(IDS) is running on the host level, it difficult to port and to extend to other system on the network level that distributed environment. Also IDS provides the confidentiality of messages when it sends each other. This paper proposes a model of real-time intrusion detection using agents. It applies to distributed environment using an extensibility and communication mechanism among agents, supports a portability, an extensibility and a confidentiality of IDS.

• The Transactions of the Korea Information Processing Society
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• v.6 no.11
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• pp.3001-3010
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• 1999

The most of intrusion detection methods do not detect intrusion on real-time because it takes a long time to analyze an auditing data for intrusions. To solve the problem, we are studying a real-time intrusion detection. Therefore, this paper proposes an agent model using multi warning level for real-time intrusion detection. It applies to distributed environment using an extensibility and communication mechanism among agents, supports a portability, an extensibility and a confidentiality of IDS.

• Journal of Software Assessment and Valuation
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• v.15 no.2
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• pp.121-128
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• 2019

Computing systems have various vulnerabilities to cyber attacks. In particular, various cyber attacks that are intelligent in the information society have caused serious social problems and economic losses. Traditional security systems are based on misuse-based technology, which requires the continuous updating of new attack patterns and the real-time analysis of vast amounts of data generated by numerous security devices in order to accurately detect. However, traditional security systems are unable to respond through detection and analysis in real time, which can delay the recognition of intrusions and cause a lot of damage. Therefore, there is a need for a new security system that can quickly detect, analyze, and predict the ever-increasing cyber security threats based on machine learning and big data analysis models. In this paper, we present a IDS model that combines machine learning and big data technology.

• The Transactions of the Korea Information Processing Society
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• v.6 no.12
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• pp.3606-3612
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• 1999

System(IDS) hasn't Protection capability for various security attacks perfectly. Because, It is probably affected by IDS's workload caused by treating all kind of the characteristics and attack patterns of system and can't probe all of the attack types being intelligently different with attack patterns. In this paper, we propose a new taxonomy criteria about DoS(denial of service attacks) to make more efficient and new real time probing system. It's started with an idea that most of the goal oriented systems make the state of system operation more unambiguous than general purpose system. A new event caused the state of the system operation to change and classifying a category of the new events may contribute to design the IDS.

• Convergence Security Journal
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• v.17 no.2
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• pp.69-80
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• 2017

Due to the advanced IT environment, the role of Security Monitoring & Control becomes more important as the cyber-crime is becoming intelligent, diversified, and advanced. In contrast to the way it relied solely on security devices such as Firewall and IDS in the past, Security Monitoring & Control tasks responding to cyber attacks through real-time monitoring have become wide spread and their role is also important. In response to current cyber threats, since security equipment alone can not be guaranteed a stable defense, the task of Security Monitoring & Control became essential to operate and monitor security equipment and to respond in real time. In this study, we will discuss how to configure network security system effectively and how to improve the real-time Security Monitor & Control.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.34-34
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• 2000

One of the basic goals, when considering networks for communication in industrial control applications, is the reduction of complexity of related wiring harnesses. In addition, the networking offers the advantages for industrial control applications, such as ease of cabling, ease of changes in the cabling, ease of adding controller modules, etc. CAN (Controller Area Network) is generally applied in car networking in order to reduce the complexity of the related wiring harnesses. These traditional CAN application techniques are modified to achieve the real time communication for the industrial control applications. In this paper, we propose the method of CAN Identifier assignment for Real-Time network system. This method is can be used to scheduling messages on CAN for Real-Time network system. And also, the real-time network system is developed and the proposed moth(Ids are verified experimentally.

• KIPS Transactions on Computer and Communication Systems
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• v.5 no.12
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• pp.471-480
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• 2016

Recently, the damage of cyber attack toward infra-system, national defence and security system is gradually increasing. In this situation, military recognizes the importance of cyber warfare, and they establish a cyber system in preparation, regardless of the existence of threaten. Thus, the study of Intrusion Detection System(IDS) that plays an important role in network defence system is required. IDS is divided into misuse and anomaly detection methods. Recent studies attempt to combine those two methods to maximize advantagesand to minimize disadvantages both of misuse and anomaly. The combination is called Hybrid IDS. Previous studies would not be inappropriate for near real-time network environments because they have computational complexity problems. It leads to the need of the study considering the structure of IDS that have high detection rate and low computational cost. In this paper, we proposed a Hybrid IDS which combines C4.5 decision tree(misuse detection method) and Weighted K-means algorithm (anomaly detection method) hierarchically. It can detect malicious network packets effectively with low complexity by applying mutual information and genetic algorithm based efficient feature selection technique. Also we construct upgraded the the hierarchical structure of IDS reusing feature weights in anomaly detection section. It is validated that proposed Hybrid IDS ensures high detection accuracy (98.68%) and performance at experiment section.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.2
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• pp.385-395
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• 2018

With the development of Internet, cyber attack has become a major threat. To detect cyber attacks, intrusion detection system(IDS) has been widely deployed. But IDS has a critical weakness which is that it generates a large number of false alarms. One of the promising techniques that reduce the false alarms in real time is machine learning. However, there are problems that must be solved to use machine learning. So, many machine learning approaches have been applied to this field. But so far, researchers have not focused on features. Despite the features of IDS alerts are important for performance of model, the approach to feature is ignored. In this paper, we propose new feature set which can improve the performance of model and can be extracted from a single alarm. New features are motivated from security analyst's know-how. We trained and tested the proposed model applied new feature set with real IDS alerts. Experimental results indicate the proposed model can achieve better accuracy and false positive rate than SVM model with ordinary features.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.7
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• pp.633-640
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• 2001

In this paper we examine the machine learning issues raised by the domain of the Intrusion Detection Systems(IDS), which have difficulty successfully classifying intruders. There systems also require a significant amount of computational overhead making it difficult to create robust real-time IDS. Machine learning techniques can reduce the human effort required to build these systems and can improve their performance. Genetic algorithms are used to improve the performance of search problems, while data mining has been used for data analysis. Data Mining is the exploration and analysis of large quantities of data to discover meaningful patterns and rules. Among the tasks for data mining, we concentrate the classification task. Since classification is the basic element of human way of thinking, it is a well-studied problem in a wide variety of application. In this paper, we propose a classifier system based on genetic algorithm, and the proposed system is evaluated by applying it to IDS problem related to classification task in data mining. We report our experiments in using these method on KDD audit data.