• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.5
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• pp.361-367
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• 2016

The intruder detection system using digital PIR sensor has the problem that it can't recognize human correctly. In this paper, we suggest a new intruder detection system based on analog PIR sensor to get around the drawbacks of the digital PIR sensor. The analog type PIR sensor emits the voltage output at various levels whereas the output of the digitial PIR sensor is binary. The signal captured using analog PIR sensor is sampled, and its frequency feature is extracted using FFT or MFCC. The extracted features are used for the input of neural networks. After neural network is trained using various human and pet's intrusion data, it is used for classifying human and pet in the intrusion situation.

• Convergence Security Journal
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• v.4 no.2
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• pp.17-25
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• 2004

As Internet and Internet users are rapidly increasing and getting popularized in the world the existing firewall has limitations to detect attacks which exploit vulnerability of web server. And these attacks are increasing. Most of all, intrusions using web application's programming error are occupying for the most part. In this paper, we introduced real-time web-server agent which analyze web-server based log and detect web-based attacks after the analysis of the web-application's vulnerability. We propose the method using real-time agent which remove Process ID(pid) and block out attacker's If if it detects the intrusion through the decision stage after judging attack types and patterns.

• Convergence Security Journal
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• v.2 no.2
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• pp.99-108
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• 2002

In this paper, I propose a reference image updating algorithm for Intruder Detection System using a difference image method that can reliably separate moving objects from noisy background in the image sequence received from a camera at the fixed position. The proposed algorithm consists of four process determines threshold value and quantization, segmentation of a moving object area, generation of adaptive temporary image that removes a moving object area, and updates reference image using median filtering. The test results show that the proposed algorithm can generate reference image very effectively in the noisy environment.

• The Journal of the Korea Contents Association
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• v.9 no.4
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• pp.131-141
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• 2009

An increasing variety of malware, such as worms, spyware and adware, threatens both personal and business computing. Remotely controlled bot networks of compromised systems are growing quickly. This paper proposes an intrusion detection system based outflow traffic analysis. Many research efforts and commercial products have focused on preventing intrusion by filtering known exploits or unknown ones exploiting known vulnerabilities. Complementary to these solutions, the proposed IDS can detect intrusion of unknown new mal ware before their signatures are widely distributed. The proposed IDS is consists of a outflow detector, user monitor, process monitor and network monitor. To infer user intent, the proposed IDS correlates outbound connections with user-driven input at the process level under the assumption that user intent is implied by user-driven input. As a complement to existing prevention system, proposed IDS decreases the danger of information leak and protects computers and networks from more severe damage.

• Convergence Security Journal
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• v.9 no.4
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• pp.21-27
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• 2009

In the environment of development of network bandwidth and intrusion technology there is limit to the pattern analysis of all massed packets through the existing pattern matching method by the intrusion detection system. To detect the packets efficiently when they are received fragmented, it has been presented the matching method only the pattern of packets consisting with the operation system such as Esnort. Pattern matching performance is improved through the use of NMAP, the basic mechanism od Esnort, by scanning the operation system of the same network system and appling pattern match selectively scanned information and the same operation system as the received packets. However, it can be appeared the case of disregarding the receivied packets depending on the diversity of the kind of operation systems and recognition mistake of operation system of nmap. In this paper, we present and verify the improved intrusion detection system shortening the pattern matching time by the creation of hashy table through the pattern hash of intrusion detection system independently with the users system environment .in the state of flux.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.13 no.6
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• pp.129-140
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• 2003

Recently viruses and various hacking tools that threat hosts on a network becomes more intelligent and cleverer, and so the various security mechanisms against them have ken developed during last decades. To detect these network attacks, many NIPSs(Network-based Intrusion Prevention Systems) that are more functional than traditional NIDSs are developed by several companies and organizations. But, many previous NIPSS are hewn to have some weakness in protecting important hosts from network attacks because of its incorrectness and post-management aspects. The aspect of incorrectness means that many NIPSs incorrectly discriminate between normal and attack network traffic in real time. The aspect of post-management means that they generally respond to attacks after the intrusions are already performed to a large extent. Therefore, to detect network attacks in realtime and to increase the capability of analyzing packets, faster and more active responding capabilities are required for NIPS frameworks. In this paper, we propose a framework for real-time intrusion prevention. This framework consists of packet filtering component that works on netfilter in Linux kernel and traffic control component that have a capability of step-by-step control over abnormal network traffic with the CBQ mechanism.

• Proceedings of the Korea Information Processing Society Conference
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• 2002.04b
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• pp.923-926
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• 2002

인공면역시스템을 이용한 침입탐지시스템 개발을 위해 적용한 동적클론선택(Dynamic Clonal Selection) 알고리즘과 그의 문제점을 소개하고 개선된 동적클론선택 알고리즘을 제안한다. 개선된 동적클론선택 알고리즘은 정상행위를 비정상행위로 판단하는 기억 탐지 자들을 제거함으로써 기존에 동적클론선택 알고리즘이 안고 있던 오류를 감소시키는 방안을 제시한다.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10c
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• pp.646-648
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• 2002

인터넷과 네트워킹 기술의 비약적인 발전으로 인해 수많은 프로토콜들과 관련 기술, 그리고 서비스들이 새롭게 등장하였다. 하지만 설계상에서 보안에 대해 고려되지 않았던 많은 기술들은 이제 새로운 보안 위협을 발생시키는 등의 문제점을 드러내고 있다. 네트워크를 통한 크래킹 역시 이러한 문제점으로 지적되고 있는데, 이러한 위협으로부터 시스템을 보호하기 위해 방화벽, 침입탐지 시스템과 같은 정보보호 시스템들이 연구, 개발되었다. 본 논문에서 제안하는 네트워크 모니터링 도구는 스니핑이라는 해킹 기법으로 이용되기도 하는 다소 위험한 기술을 이용하여 네트워크상의 패킷을 실시간으로 수집, 분석함으로써 네트워크 관련 오류의 점검, 크래킹의 실시간 감시등에 이용할 수 있도록 해준다.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.157-173
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• 2011

As the Internet use explodes recently, the malicious attacks and hacking for a system connected to network occur frequently. This means the fatal damage can be caused by these intrusions in the government agency, public office, and company operating various systems. For such reasons, there are growing interests and demand about the intrusion detection systems (IDS)-the security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. The intrusion detection models that have been applied in conventional IDS are generally designed by modeling the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. These kinds of intrusion detection models perform well under the normal situations. However, they show poor performance when they meet a new or unknown pattern of the network attacks. For this reason, several recent studies try to adopt various artificial intelligence techniques, which can proactively respond to the unknown threats. Especially, artificial neural networks (ANNs) have popularly been applied in the prior studies because of its superior prediction accuracy. However, ANNs have some intrinsic limitations such as the risk of overfitting, the requirement of the large sample size, and the lack of understanding the prediction process (i.e. black box theory). As a result, the most recent studies on IDS have started to adopt support vector machine (SVM), the classification technique that is more stable and powerful compared to ANNs. SVM is known as a relatively high predictive power and generalization capability. Under this background, this study proposes a novel intelligent intrusion detection model that uses SVM as the classification model in order to improve the predictive ability of IDS. Also, our model is designed to consider the asymmetric error cost by optimizing the classification threshold. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, when considering total cost of misclassification in IDS, it is more reasonable to assign heavier weights on FNE rather than FPE. Therefore, we designed our proposed intrusion detection model to optimize the classification threshold in order to minimize the total misclassification cost. In this case, conventional SVM cannot be applied because it is designed to generate discrete output (i.e. a class). To resolve this problem, we used the revised SVM technique proposed by Platt(2000), which is able to generate the probability estimate. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 1,000 samples from them by using random sampling method. In addition, the SVM model was compared with the logistic regression (LOGIT), decision trees (DT), and ANN to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell 4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on SVM outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that our model reduced the total misclassification cost compared to the ANN-based intrusion detection model. As a result, it is expected that the intrusion detection model proposed in this paper would not only enhance the performance of IDS, but also lead to better management of FNE.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.125-141
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• 2012

These days, the malicious attacks and hacks on the networked systems are dramatically increasing, and the patterns of them are changing rapidly. Consequently, it becomes more important to appropriately handle these malicious attacks and hacks, and there exist sufficient interests and demand in effective network security systems just like intrusion detection systems. Intrusion detection systems are the network security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. Conventional intrusion detection systems have generally been designed using the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. However, they cannot handle new or unknown patterns of the network attacks, although they perform very well under the normal situation. As a result, recent studies on intrusion detection systems use artificial intelligence techniques, which can proactively respond to the unknown threats. For a long time, researchers have adopted and tested various kinds of artificial intelligence techniques such as artificial neural networks, decision trees, and support vector machines to detect intrusions on the network. However, most of them have just applied these techniques singularly, even though combining the techniques may lead to better detection. With this reason, we propose a new integrated model for intrusion detection. Our model is designed to combine prediction results of four different binary classification models-logistic regression (LOGIT), decision trees (DT), artificial neural networks (ANN), and support vector machines (SVM), which may be complementary to each other. As a tool for finding optimal combining weights, genetic algorithms (GA) are used. Our proposed model is designed to be built in two steps. At the first step, the optimal integration model whose prediction error (i.e. erroneous classification rate) is the least is generated. After that, in the second step, it explores the optimal classification threshold for determining intrusions, which minimizes the total misclassification cost. To calculate the total misclassification cost of intrusion detection system, we need to understand its asymmetric error cost scheme. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, total misclassification cost is more affected by FNE rather than FPE. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 10,000 samples from them by using random sampling method. Also, we compared the results from our model with the results from single techniques to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell R4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on GA outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that the proposed model outperformed all the other comparative models in the total misclassification cost perspective. Consequently, it is expected that our study may contribute to build cost-effective intelligent intrusion detection systems.