• Journal of the Korea Institute of Information Security & Cryptology
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• v.12 no.4
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• pp.29-39
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• 2002

In general, the intrusion detection method of anomalous behaviors has high false alarm rate which contains false-positive and false-negative. To increase the sensitivity of intrusion detection, we propose a method of aggregate detection to reduce false alarm rate by using correlation between misuse activity detection sensors and anomalous ones. For each normal behavior and anomalous one, we produce the reflection rate between the result from one sensor and another in off-line. Then, we apply this rate to the result of real-time detection to reduce false alarm rate.

• 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 Society of Computer and Information
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• v.7 no.2
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• pp.46-52
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• 2002

Trend of wire internet has been transferred to wireless internet gradually due to the spread of mobile phone which made Possible Mobility and portability which wire internet could not afford. Not only front line of business part can access business information but also people can use government information for their daily life without limit of place. The frequent report of larceny and misuse of information has been issued to social sector that the need for IDS considering wire wireless internet. In this paper to design IDS to protect information first, searched wire internet intrusion type, intrusion detection method, and wireless intrusion type. In this paper, first, separate abnormal access at the point of system landing and detect intrusion attack with disguise through mobile wireless internet. Due to the intruder can access system normally with disguise, Circuit Patrol model has been suggested to monitor from intrusion attack.

• Journal of Korea Society of Digital Industry and Information Management
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• v.6 no.2
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• pp.199-207
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• 2010

Recently, large amount of information in IDS(Intrusion Detection System) can be un manageable and also be mixed with false prediction error. In this paper, we propose a data mining methodology for IDS, which contains uncertainty based on training process and post-processing analysis additionally. Our system is trained to classify the existing attack for misuse detection, to detect the new attack pattern for anomaly detection, and to define border patter between attack and normal pattern. In experimental results show that our approach improve the performance against existing attacks and new attacks, from 0.62 to 0.84 about 35%.

• Journal of KIISE:Information Networking
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• v.30 no.3
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• pp.397-406
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• 2003

As the information technology grows interests in the intrusion detection system (IDS), which detects unauthorized usage, misuse by a local user and modification of important data, has been raised. In the field of anomaly-based IDS several artificial intelligence techniques such as hidden Markov model (HMM), artificial neural network, statistical techniques and expert systems are used to model network rackets, system call audit data, etc. However, there are undetectable intrusion types for each measure and modeling method because each intrusion type makes anomalies at individual measure. To overcome this drawback of single-measure anomaly detector, this paper proposes a multiple-measure intrusion detection method. We measure normal behavior by systems calls, resource usage and file access events and build up profiles for normal behavior with hidden Markov model, statistical method and rule-base method, which are integrated with a rule-based approach. Experimental results with real data clearly demonstrate the effectiveness of the proposed method that has significantly low false-positive error rate against various types of intrusion.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.3
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• pp.282-288
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• 2005

In this paper, we propose a new intrusion detection model, which keeps advantages of existing misuse detection model and anomaly detection model and resolves their problems. This new intrusion detection system, named to MMIDS, was designed to satisfy all the following requirements : 1) Fast detection of new types of attack unknown to the system; 2) Provision of detail information about the detected types of attack; 3) cost-effective maintenance due to fast and efficient learning and update; 4) incrementality and scalability of system. The fast and efficient training and updating faculties of proposed novel multi-class SVM which is a core component of MMIDS provide cost-effective maintenance of intrusion detection system. According to the experimental results, our method can provide superior performance in separating similar patterns and detailed separation capability of MMIDS is relatively good.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10c
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• pp.517-519
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• 2002

침입탐지 시스템은 정밀성자 적응성, 그리고 확장성을 필요로 한다. 이와 같은 조건을 포함하면서 복잡한 Network 환경에서 중요하고 기밀성이 유지되어야 할 리소스를 보호하기 위해, 우리는 더욱 구조적이며 지능적인 IDS(Intrusion Detection Systems) 개발의 필요성이 요구되고 있다. 본 연구는 데이터 마이닝(Data mining)을 통해 입 패턴, 즉 침입 규칙(Rules)을 생성한다. 데이터 마이닝 기법 중 분류(Classification)에 초점을 맞추어 분석과 실험을 하였으며, 사용된 데이터는 KDD데이터이다. 이 데이터를 중심으로 침입 규칙을 생성하였다. 규칙생성에는 유전자알고리즘(Genetic Algorithm : GAs)을 적용하였다. 즉, 오용탐지(Misuse Detection) 기법을 실험하였으며, 생성된 규칙은 침입데이터를 대표하는 규칙으로 비정상 사용자와 정상 사용자를 분류하게 된다. 규칙은 "Time Based Traffic Model", "Host Based Traffic Model", "Content Model" 이 세 가지 모듈에서 각각 상이한 침입 규칙을 생성하게 된다. 본 시스템에서 도출된 침입 규칙은 430M Test data set에서 테스트한 결과 평균 약94.3%의 성능 평가 결과를 얻어 만족할 만한 성과를 보였다.의 성능 평가 결과를 얻어 만족할 만한 성과를 보였다.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.59.1-59
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• 2001

The attackers on Internet-connected systems we are seeing today are more serious and more technically complex than those in the past. Computer security incidents are different from many other types of crimes because detection is unusually difficult. So, network security managers need a IDS and Firewall. IDS (Intrusion Detection System) monitors system activities to identify unauthorized use, misuse or abuse of computer and network system. It accomplishes these by collecting information from a variety of systems and network resources and then analyzing the information for symptoms of security problems. A Firewall is a way to restrict access between the Internet and internal network. Usually, the input ...

• 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 KIISE:Information Networking
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• v.29 no.6
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• pp.674-684
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• 2002

Anomaly detection techniques have teen devised to address the limitations of misuse detection approach for intrusion detection. An HMM is a useful tool to model sequence information whose generation mechanism is not observable and is an optimal modeling technique to minimize false-positive error and to maximize detection rate, However, HMM has the short-coming of login training time. This paper proposes an effective HMM-based IDS that improves the modeling time and performance by only considering the events of privilege flows based on the domain knowledge of attacks. Experimental results show that training with the proposed method is significantly faster than the conventional method trained with all data, as well as no loss of recognition performance.