• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.2086-2098
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• 2018

Security plays a vital role and is the key challenge in Mobile Ad-hoc Networks (MANET). Infrastructure-less nature of MANET makes it arduous to envisage the genre of topology. Due to its inexhaustible access, information disseminated by roaming nodes to other nodes is susceptible to many hazardous attacks. Intrusion Detection and Prevention System (IDPS) is undoubtedly a defense structure to address threats in MANET. Many IDPS methods have been developed to ascertain the exceptional behavior in these networks. Key issue in such IDPS is lack of fast self-organized learning engine that facilitates comprehensive situation awareness for optimum decision making. Proposed "Intelligent Behavioral Hybridized Intrusion Detection and Prevention System (IBH_IDPS)" is built with computational intelligence to detect complex multistage attacks making the system robust and reliable. The System comprises of an Intelligent Client Agent and a Smart Server empowered with fuzzy inference rule-based service engine to ensure confidentiality and integrity of network. Distributed Intelligent Client Agents incorporated with centralized Smart Server makes it capable of analyzing and categorizing unethical incidents appropriately through unsupervised learning mechanism. Experimental analysis proves the proposed model is highly attack resistant, reliable and secure on devices and shows promising gains with assured delivery ratio, low end-to-end delay compared to existing approach.

• Proceedings of the KSR Conference
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• 2011.05a
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• pp.1729-1734
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• 2011

Urban transit administers need to introduce the intelligent system to know the situations in the urban transit service area automatically. It is one of the important elements to detect of intrusion in operation room or electric rooms, etc. In this paper, we describe the definition for detection of intrusion in urban transit area, and propose the context-awareness model detect of intrusion. We expect that the definition is helpful to extract the elements that are need to construct the intrusion detecting system. The proposed model that is based on an image analysis model and a rule-based model is also helpful to design intelligent surveillance model.

• Journal of Convergence for Information Technology
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• v.12 no.4
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• pp.126-132
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• 2022

At present, due to the rapid spread of smartphones and activation of IoT, malicious codes are disseminated using SNS, or intelligent intrusions such as intelligent APT and ransomware are in progress. The damage caused by the intelligent intrusion is also becoming more consequential, threatening, and emergent than the previous intrusion. Therefore, in this paper, we propose an intelligent intrusion situation-aware reasoning system to detect transgression behavior made by such intelligent malicious code. The proposed system was used to detect and respond to various intelligent intrusions at an early stage. The anticipated system is composed of an event monitor, event manager, situation manager, response manager, and database, and through close interaction between each component, it identifies the previously recognized intrusive behavior and learns about the new invasive activities. It was detected through the function to improve the performance of the inference device. In addition, it was found that the proposed system detects and responds to intelligent intrusions through the state of detecting ransomware, which is an intelligent intrusion type.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.05a
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• pp.109-113
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• 2002

In this paper, we propose a new intrusion detection algorithm based on clustering: Kernel-ART, which is composed of the on-line clustering algorithm, ART (adaptive resonance theory), combining with mercer-kernel and concept vector. Kernel-ART is not only satisfying all desirable characteristics in the context of clustering-based 105 but also alleviating drawbacks associated with the supervised learning IDS. It is able to detect various types of intrusions in real-time by means of generating clusters incrementally.

• 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.

• Journal of Information Processing Systems
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• v.6 no.4
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• pp.481-500
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• 2010

The Internet explosion and the increase in crucial web applications such as ebanking and e-commerce, make essential the need for network security tools. One of such tools is an Intrusion detection system which can be classified based on detection approachs as being signature-based or anomaly-based. Even though intrusion detection systems are well defined, their cooperation with each other to detect attacks needs to be addressed. Consequently, a new architecture that allows them to cooperate in detecting attacks is proposed. The architecture uses Software Agents to provide scalability and distributability. It works in two modes: learning and detection. During learning mode, it generates a profile for each individual system using a fuzzy data mining algorithm. During detection mode, each system uses the FuzzyJess to match network traffic against its profile. The architecture was tested against a standard data set produced by MIT's Lincoln Laboratory and the primary results show its efficiency and capability to detect attacks. Finally, two new methods, the memory-window and memoryless-window, were developed for extracting useful parameters from raw packets. The parameters are used as detection metrics.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.307-313
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• 1999

This paper reviews and assesses the analogy between the human immune system and network intrusion detection systems. The promising results from a growing number of proposed computer immune models for intrusion detection motivate this work. The paper begins by briefly introducing existing intrusion detection systems (IDS's). A set of general requirements for network-based IDS's and the design goals to satisfy these requirements are identified by a careful examination of the literature. An overview of the human immune system is presented and its salient features that can contribute to the design of competent network-based IDS's are analysed. The analysis shows that the coordinated actions of several sophisticated mechanisms of the human immune system satisfy all the identified design goals. Consequently, the paper concludes that the design of a network-based IDS based on the human immune system is promising for future network-based IDS's

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.4
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• pp.491-498
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• 2003

The advanced computer network technology enables connectivity of computers through an open network environment. There has been growing numbers of security threat to the networks. Therefore, it requires intrusion detection and prevention technologies. In this paper, we propose a network based intrusion detection model using FCM(Fuzzy Cognitive Maps) that can detect intrusion by the DoS attack detection method adopting the packet analyses. A DoS attack appears in the form of the Probe and Syn Flooding attack which is a typical example. The SPuF(Syn flooding Preventer using Fussy cognitive maps) model captures and analyzes the packet informations to detect Syn flooding attack. Using the result of analysis of decision module, which utilized FCM, the decision module measures the degree of danger of the DoS and trains the response module to deal with attacks. For the performance comparison, the "KDD′99 Competition Data Set" made by MIT Lincoln Labs was used. The result of simulating the "KDD′99 Competition Data Set" in the SPuF model shows that the probe detection rates were over 97 percentages.

• Journal of Korea Society of Digital Industry and Information Management
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• v.14 no.1
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• pp.35-43
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• 2018

MANET composed of only wireless nodes is increasingly utilized in various fields. However, it is exposed to many security vulnerabilities because it doesn't have any infrastructure and transmits data by using multi-hop method. Therefore, MANET should be applied the intrusion detection technique that can detect efficiently malicious nodes and decrease impacts of various attacks. In this paper, we propose a distributed intrusion detection technique that can detect the various attacks while improving the efficiency of attack detection and reducing the false positive rate. The proposed technique uses the cluster structure to manage the information in the center and monitor the traffic of their neighbor nodes directly in all nodes. We use three parameters for attack detection. We also applied an efficient authentication technique using only key exchange without the help of CA in order to provide integrity when exchanging information between cluster heads. This makes it possible to free the forgery of information about trust information of the nodes and attack nodes. The superiority of the proposed technique can be confirmed through comparative experiments with existing intrusion detection techniques.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2007.11a
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• pp.514-519
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• 2007

컴퓨터 네트워크 모니터링에 의한 보안장비는 많은 트래픽 자료를 분석하여, 이상유무를 판단하고, 대응해야 한다. 기존의 보안장비들은 이미 알려진 패턴에 대한 규칙을 이용하는 오용탐지방법(misuse detection)과 의미를 파악하기 어려운 많은 자료들을 제시하고 있는데 머물고 있다. 보다 나은 보안을 위해서는 정상적인 동작에서 벗어나는 이상징후를 탐지하여 침입을 탐지하는 이상탐지방법(anomaly detection)의 채용이 필요하고, 보안장비에서 제시되는 많은 트래픽 자료들은 보안전문가의 전문적인 분석이 필요하다. 본 연구에서는 데이터마이닝 기법을 이용한 이상탐지방법과 보안전문가의 전문적인 보안지식에 의한 분석, 대응, 관리를 위한 지식처리 기법을 사용할 수 있는 지능형 IPS(intrusion Detection System) 프레임워크를 제안한다.