• Journal of the Korea Society of Computer and Information
• /
• v.9 no.2
• /
• pp.81-87
• /
• 2004

Very various intrusion by development of systems that is based on network is spread. To detect and respond this intrusion, security solutions such as firewall or IDS are bringing and management of security system that load these becomes more harder. Moreover, because environment of systems that require security is various, hard to manage establishing suitable security policy Therefore, need model about enterprise management of various security system and intrusion detection of each systems and response. In this paper, improve PBNM structure that manage wide network resources and presented suitable model in intrusion detection and response of security system. Also, designed policy-based enterprise security management system for effective intrusion detection and response by applying presented model to enterprise security management system.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.2
• /
• pp.702-723
• /
• 2020

The application of machine learning (ML) in intrusion detection has attracted much attention with the rapid growth of information security threat. As an efficient multi-label classifier, kernel extreme learning machine (KELM) has been gradually used in intrusion detection system. However, the performance of KELM heavily relies on the kernel selection. In this paper, a novel multiple kernel extreme learning machine (MKELM) model combining the ReliefF with nature-inspired methods is proposed for intrusion detection. The MKELM is designed to estimate whether the attack is carried out and the ReliefF is used as a preprocessor of MKELM to select appropriate features. In addition, the nature-inspired methods whose fitness functions are defined based on the kernel alignment are employed to build the optimal composite kernel in the MKELM. The KDD99, NSL and Kyoto datasets are used to evaluate the performance of the model. The experimental results indicate that the optimal composite kernel function can be determined by using any heuristic optimization method, including PSO, GA, GWO, BA and DE. Since the filter-based feature selection method is combined with the multiple kernel learning approach independent of the classifier, the proposed model can have a good performance while saving a lot of training time.

• Convergence Security Journal
• /
• v.5 no.2
• /
• pp.9-17
• /
• 2005

Recently It's difficult to deal with about variety of attack. And Simple Security management have a problem. It is that they don't develop system measuring their system envoirment and have efficient attack detector, countermeasure organization about large network. Therefore, need model about enterprise management of various security system and intrusion detection of each systems and response. In this paper, improve PBNM structure that manage wide network resources and presented suitable model in intrusion detection and response of security system. Also, designed policy-based enterprise security management system for effective intrusion detection and response by applying presented model to enterprise security management system.

• Journal of information and communication convergence engineering
• /
• v.7 no.1
• /
• pp.7-12
• /
• 2009

The advanced computer network and Internet technology enables connectivity of computers through an open network environment. Despite the growing numbers of security threats to networks, most intrusion detection identifies security attacks mainly by detecting misuse using a set of rules based on past hacking patterns. This pattern matching has a high rate of false positives and can not detect new hacking patterns, making it vulnerable to previously unidentified attack patterns and variations in attack and increasing false negatives. Intrusion detection and prevention technologies are thus required. We proposed a network based hybrid Probe Intrusion Detection model using Fuzzy cognitive maps (PIDuF) that detects intrusion by DoS (DDoS and PDoS) attack detection using packet analysis. A DoS attack typically appears as a probe and SYN flooding attack. SYN flooding using FCM model captures and analyzes packet information to detect SYN flooding attacks. Using the result of decision module analysis, which used FCM, the decision module measures the degree of danger of the DoS and trains the response module to deal with attacks. For the performance evaluation, the "IDS Evaluation Data Set" created by MIT was used. From the simulation we obtained the max-average true positive rate of 97.064% and the max-average false negative rate of 2.936%. The true positive error rate of the PIDuF is similar to that of Bernhard's true positive error rate.

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

• Journal of information and communication convergence engineering
• /
• v.2 no.2
• /
• pp.84-88
• /
• 2004

Firewall is not perfectly prevent hacker, Intrusion Detection System(IDS) is considered a next generation security solution for more trusted network i and system security. We propose a agent IDS model in the different platforms that can detect intrusions in the expanded distributed host environment, since that is a drawback of existing IDS. Then we implement a prototype and verify validity. We use a pattern extraction agent so that we extract audit files needed in intrusion detection automatically even in other platforms.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.5
• /
• pp.2732-2753
• /
• 2019

With the application of wireless sensor networks in the fields of ecological observation, defense military, architecture and urban management etc., the security problem is becoming more and more serious. Characteristics and constraint conditions of wireless sensor networks such as computing power, storage space and battery have brought huge challenges to protection research. Inspired by the danger theory in biological immune system, this paper proposes an intrusion detection model for wireless sensor networks. The model abstracts expressions of antigens and antibodies in wireless sensor networks, defines meanings and functions of danger signals and danger areas, and expounds the process of intrusion detection based on the danger theory. The model realizes the distributed deployment, and there is no need to arrange an instance at each sensor node. In addition, sensor nodes trigger danger signals according to their own environmental information, and do not need to communicate with other nodes, which saves resources. When danger is perceived, the model acquires the global knowledge through node cooperation, and can perform more accurate real-time intrusion detection. In this paper, the performance of the model is analyzed including complexity and efficiency, and experimental results show that the model has good detection performance and reduces energy consumption.

• Proceedings of the KSR Conference
• /
• 2011.05a
• /
• pp.1729-1734
• /
• 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 Information Processing Systems
• /
• v.20 no.4
• /
• pp.458-476
• /
• 2024

To address the issue of class imbalance in network traffic data, which affects the network intrusion detection performance, a combined framework using transformers is proposed. First, Tomek Links, SMOTE, and WGAN are used to preprocess the data to solve the class-imbalance problem. Second, the transformer is used to encode traffic data to extract the correlation between network traffic. Finally, a hybrid deep learning network model combining a bidirectional gated current unit and deep neural network is proposed, which is used to extract long-dependence features. A DNN is used to extract deep level features, and softmax is used to complete classification. Experiments were conducted on the NSLKDD, UNSWNB15, and CICIDS2017 datasets, and the detection accuracy rates of the proposed model were 99.72%, 84.86%, and 99.89% on three datasets, respectively. Compared with other relatively new deep-learning network models, it effectively improved the intrusion detection performance, thereby improving the communication security of network data.

• Journal of the Korea Society of Computer and Information
• /
• v.9 no.1
• /
• pp.37-43
• /
• 2004

Expasion of computer network and rapid growth of Internet have made computer security very important. As one of the ways to deal with security risk, much research has been made on Intrusion Detection System(IDS). The paper, also, addresses the issue of intrusion detection, but especially with Neuro-Fuzzy model. By applying the fuzzy logic which is known to deal with uncertainty to Anomaly Intrusion, it not only overcomes the difficulty of Misuse Intrusion, but also ultimately aims to detect the intrusions yet to be known.