• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.3
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• pp.252-259
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• 2000

In this paper, we propose a Fuzzy Classifier System(FCS) to find a set of fuzzy rules which can carry out the edge detection of a image. The FCS is based on the fuzzy logic system combined with machine learning. Therefore the antecedent and consequent of a classifier in FCS are the same as those of a fuzzy rule. There are two different approaches, Michigan and Pittsburgh approaches, to acquire appropriate fuzzy rules by evolutionary computation. In this paper, we use the Michigan style in which a single fuzzy if-then rule is coded as an individual. Also the FCS employs the Genetic Algorithms to generate new rules and modify rules when performance of the system needs to be improved. The proposed method is evaluated by applying it to the edge detection of a gray-level image that is a pre-processing step of the computer vision. the differences of average gray-level of the each vertical/horizontal arrays of neighborhood pixels are represented into fuzzy sets, and then the center pixel is decided whether it is edge pixel or not using fuzzy if-then rules. We compare the resulting image with a conventional edge image obtained by the other edge detection method such as Sobel edge detection.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.6
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• pp.2735-2751
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• 2020

The emergence of Software Defined Networking (SDN) overcomes the limitations of traditional networking architectures. There are some advantages in SDN which are centralized global network view, programmability, and separation of the data plane and control plane. Due to the limitation of data plane storage capacity in SDN, it is necessary to process the redundancy rules of switch. In this paper, we propose a method for active detection and processing of redundant rules. We use the result generated by the customized probe package to detect redundant rules. And by checking the forwarding behavior of probe packets in the data plane, the redundancy rules are further processed. Furthermore, in order to quickly check the dynamic networks, we propose an incremental algorithms for rapidly evolve the network strategies. We conduct simulation experiments on Matlab to verify the feasibility of the algorithm. The influence of some parameters on the result are discussed.

• Journal of KIISE:Software and Applications
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• v.34 no.6
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• pp.519-529
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• 2007

In this paper, we investigate the use of fuzzy rules for efficient intrusion detection. We use evolutionary algorithm to optimize the set of fuzzy rules for intrusion detection by constructing fuzzy decision trees. For efficient execution of evolutionary algorithm we use supervised clustering to generate an initial set of membership functions for fuzzy rules. In our method both performance and complexity of fuzzy rules (or fuzzy decision trees) are taken into account in fitness evaluation. We also use evaluation with data partition, membership degree caching and zero-pruning to reduce time for construction and evaluation of fuzzy decision trees. For performance evaluation, we experimented with our method over the intrusion detection data of KDD'99 Cup, and confirmed that our method outperformed the existing methods. Compared with the KDD'99 Cup winner, the accuracy was increased by 1.54% while the cost was reduced by 20.8%.

• Journal of Internet Computing and Services
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• v.20 no.2
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• pp.9-19
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• 2019

Service and users over the Internet are increasing rapidly. Cyber attacks are also increasing. As a result, information leakage and financial damage are occurring. Government, public agencies, and companies are using security systems that use signature-based detection rules to respond to known malicious codes. However, it takes a long time to generate and validate signature-based detection rules. In this paper, we propose and develop signature based detection rule generation and verification systems using the signature extraction scheme developed based on the LDA(latent Dirichlet allocation) algorithm and the traffic analysis technique. Experimental results show that detection rules are generated and verified much more quickly than before.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.1
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• pp.240-259
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• 2020

Analyzing network traffic is the basis of dealing with network security issues. Most of the network security systems depend on the feature selection of network traffic data and the detection ability of malicious traffic in network can be improved by the correct method of feature selection. An FAFS method, which is short for Fuzzy Association Feature Selection method, is proposed in this paper for network malicious traffic detection. Association rules, which can reflect the relationship among different characteristic attributes of network traffic data, are mined by association analysis. The membership value of association rules are obtained by the calculation of fuzzy reasoning. The data features with the highest correlation intensity in network data sets are calculated by comparing the membership values in association rules. The dimension of data features are reduced and the detection ability of malicious traffic detection algorithm in network is improved by FAFS method. To verify the effect of malicious traffic feature selection by FAFS method, FAFS method is used to select data features of different dataset in this paper. Then, K-Nearest Neighbor algorithm, C4.5 Decision Tree algorithm and Naïve Bayes algorithm are used to test on the dataset above. Moreover, FAFS method is also compared with classical feature selection methods. The analysis of experimental results show that the precision and recall rate of malicious traffic detection in the network can be significantly improved by FAFS method, which provides a valuable reference for the establishment of network security system.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.3
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• pp.615-625
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• 2015

This paper proposes new methods and examples for improving fraud detection rules based on banking customer's transaction behaviors focused on anomaly detection method. This study investigates real example that FDS(Fraud Detection System) regards fraudulent transaction as legitimate transaction and figures out fraudulent types and transaction patterns. To understanding the cases that FDS regard legitimate transaction as fraudulent transaction, it investigates all transactions that requied additional authentications or outbound call. We infered additional facts to refine detection rules in progress of outbound calling and applied to existing detection rules to improve. The main results of this study is the following: (a) Type I error is decreased (b) Type II errors are also decreased. The major contribution of this paper is the improvement of effectiveness in detecting fraudulent transaction using transaction behaviors and providing a continuous method that elevate fraud detection rules.

• International Journal of Safety
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• v.11 no.1
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• pp.6-10
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• 2012

This paper presents a study on the analysis for reducing the number of false alarms in fire detection system. In order to intelligent algorithm fuzzy logic is adopted in developing fire detection system to reduce false alarm. The intelligent fire detection algorithm compared and analyzed the fire and non-fire signatures measured in circuits simulating flame fire and smoldering fire. The algorithm has input variables obtained by fire experiment with K-type thermocouple and optical smoke sensor. Also triangular membership function is used for inference rules. And the antecedent part of inference rules consists of temperature and smoke density, and the consequent part consists of fire probability. A fire-experiment is conducted with paper, plastic, and n-heptane to simulate actual fire situation. The results show that the intelligent fire detection algorithm suggested in this study can more effectively discriminate signatures between fire and similar fire.

• The KIPS Transactions:PartC
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• v.10C no.6
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• pp.717-726
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• 2003

In this paper, we analyze the associated rule based deductive algorithm which creates the rules automatically for intrusion detection from the vast packet data. Based on the result, we also suggest the deductive algorithm which creates the rules of intrusion pattern fast in order to apply the intrusion detection systems. The deductive algorithm proposed is designed suitable to the concept of clustering which classifies and deletes the large data. This algorithm has direct relation with the method of pattern generation and analyzing module of the intrusion detection system. This can also extend the appication range and increase the detection speed of exiting intrusion detection system as the rule database is constructed for the pattern management of the intrusion detection system. The proposed pattern generation technique of the deductive algorithm is used to the algorithm is used to the algorithm which can be changed by the supporting rate of the data created from the intrusion detection system. Fanally, we analyze the possibility of the speed improvement of the rule generation with the algorithm simulation.

• Journal of KIISE:Software and Applications
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• v.29 no.11
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• pp.837-850
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• 2002

In this paper, we propose an EEG-based response time prediction method during a yes/no cognitive decision task. In the experimental task, a subject goes through responding of visual stimulus, understanding the given problem, controlling hand motions, and hitting a key. Considering the subject's varying brain activities, we model subjects' mental states with defining CT (cut time), ST (selection time), and RP (repeated period). Based on the assumption between ST and RT in the mental model, we predict subjects' response time by detection of selection time. To recognize the subjects' selection time ST, we extract 3 types of feature from the filtered brain waves at frequency bands of $\alpha$, $\beta$, ${\gamma}$ waves in 4 electrode pairs combined by spatial relationships. From the extracted features, we construct specific rules for each subject and meta rules including common factors in all subjects. Applying the ST detection rules to 8 subjects gives 83% success rates and also shows that the subjects will hit a key in 0.73 seconds after ST detected. To validate the detection rules and parameters, we test the rules for 2 subjects among 8 and discuss about the experimental results. We expect that the proposed detection method can be a basic technology for brain-computer-interface by combining with left/right hand movement or yes/no discrimination methods.

• Journal of Korea Society of Digital Industry and Information Management
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• v.6 no.4
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• pp.153-160
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• 2010

MANET composed mobile nodes without central concentration control like base station communicate through multi-hop route among nodes. Accordingly, it is hard to maintain stability of network because topology of network change at any time owing to movement of mobile nodes. MANET has security problems because of node mobility and needs intrusion detection system that can detect attack of malicious nodes. Therefore, system is protected from malicious attack of intruder in this environment and it has to correspond to attack immediately. In this paper, we propose intrusion detection system based on rules in order to more accurate intrusion detection. Cluster head perform role of monitor node to raise monitor efficiency of packet. In order to evaluate performance of proposed method, we used jamming attack, selective forwarding attack, repetition attack.