• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.6
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• pp.110-116
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• 2014

Pattern matching algorithms are widely used in computer security systems such as computer networks, ubiquitous networks, sensor networks, and so on. However, the advances in information technology causes grow on the amount of data and increase on the computation complexity of pattern matching processes. Therefore, there is a strong demand for a novel high performance pattern matching algorithms. In light of this fact, this paper newly proposes a suffix tree based pattern matching algorithm. The suffix tree is constructed based on the suffix values of all patterns. Then, the shift nodes which informs how many characters can be skipped without matching operations are added to the suffix tree in order to boost matching performance. The proposed algorithm reduces memory usage on the suffix tree and the amount of matching operations by the shift nodes. From the performance evaluation, our algorithm achieved 24% performance gain compared with the traditional algorithm named as Wu-Manber.

• Journal of the Korea Institute of Military Science and Technology
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• v.11 no.5
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• pp.58-70
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• 2008

Earlier researches on Sensor Networks preferred symmetric key-based authentication schemes in consideration of limitations in network resources. However, recent advancements in cryptographic algorithms and sensor-node manufacturing techniques have opened suggestion to public key-based solutions such as Merkle tree-based schemes. These previous schemes, however, must perform the authentication process one-by-one in hierarchical manner and thus are not fit to be used as primary authentication methods in sensor networks which require mass of multiple authentications at any given time. This paper proposes a new concept of public key-based authentication that can be effectively applied to sensor networks. This scheme is based on exponential distributed data concept, a derivative from Shamir's (t, n) threshold scheme, in which the authentication of neighbouring nodes are done simultaneously while minimising resources of sensor nodes and providing network scalability. The performance advantages of this scheme on memory usage, communication overload and scalability compared to Merkle tree-based authentication are clearly demonstrated using performance analysis.

• Journal of Internet Computing and Services
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• v.16 no.6
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• pp.23-30
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• 2015

As BigData is issued, many applications that operate based on the results of data analysis have been developed, typically applications are products recommend service of e-commerce application service system, search service on the search engine service and friend list recommend system of social network service. In this paper, we suggests a decision frequent pattern tree that is combined the origin frequent pattern tree that is mining similar pattern to appear in the data set of the existing data mining techniques and decision tree based on the theory of computer science. The decision frequent pattern tree algorithm improves about problem of frequent pattern tree that have to make some a lot's pattern so it is to hard to analyze about data. We also proposes to model for a Mapredue framework that is a programming model to help to operate in distributed environment.

• KIPS Transactions on Computer and Communication Systems
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• v.3 no.5
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• pp.155-162
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• 2014

We proposed a 2 level tree based cluster based routing protocol for mobile ad hoc networks. it is our crucial goal to establish improved clustering's structure in order to extend average node life-time and elevate the average packet delivery ratio. Because of insufficient wireless resources and energy, the method to form and manage clusters is useful for increasing network stability. but cluster-head fulfills roles as a host and a router in clustering protocol of Ad hoc networks environment. Therefore energy exhaustion of cluster-head causes communication interruption phenomenon. Effective management of cluster-head is key-point which determines the entire network performance. The scheme focuses on improving the performance the life time of the network and throughput through the management of cluster-heads and its neighbor nodes. In simulation, we demonstrated that it would obtain averagely better 17% performance than LS2RP.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.4
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• pp.9-15
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• 2013

Most of recent studies on data center networks are based on the assumption of symmetrical multi-rooted tree topologies such as a Fat-Tree. While packet scatter schemes are very effective for such a network topology, it is known that various failures can result in an asymmetric topology which degrades TCP performance. In this paper, we reexamine the effects of link failures on packet scatter schemes in Fat-Trees. Our simulation results show that in case of a single link failure in a large-scale Fat-Tree, packet reordering does not occur enough to degrade TCP performance. This implies that we do not necessarily need a complex scheme to make packet schemes robust to link failures.

• The Transactions of the Korea Information Processing Society
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• v.4 no.3
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• pp.821-830
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• 1997

The directory-based cache coherence scheme is an attractive approach to solve the caceh coherence problem in a large-scale shared-memory multiprocessor.However, the exsting directory-based schemes have some problens such as the enormous storage overhead for a directory, the long invalidation latency, the heavy network condes-tion, and the low scalability.For resolving these problems, we propose a new directroy- based caceh coherence scheme which is suitable for building scalable, shred-memory multiprocessors.In this scheme, each directory en-try ofr a given memory block is a number-balanced binaty tree(NBBT) stucture.The NBBT has several proper-ties to effciently maintain the directory for the cache consistency such that the shape is unique, the maximum depth is [log$_2$n], and the tree has the minimum number of leaf nodes among the binarry tree with n nodes.Therefore, this scheme can reduce the storage overhead, the network traffic, and the inbalidation latency and can ensutr the high- scalability the large-scale shared-memory multiprocessors.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.5A
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• pp.502-510
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• 2011

In this paper, we propose a slot scheduling algorithm for balancing power consumption in tree-based sensor networks. In this type of networks, nodes with lower depths tend to consume more energy than those with higher depths, thereby reducing the life time of the network. The proposed algorithm allocates a series of receiving slots first and then a series of sending slots. This way of slot allocation eases packet aggregation and filtering, and thus reduces traffic load on nodes near a sink. We compare the proposed algorithm and the frame-slot allocation algorithm employed in the TreeMAC by resorting to simulation. The simulation results showed that the proposed approach well achieves the balancing of power consumption.

• Transactions on Electrical and Electronic Materials
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• v.18 no.6
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• pp.350-358
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• 2017

Recently, owing to increased interest in low-carbon energy supplies, renewable energy sources such as photovoltaics and wind turbines in distribution networks have received considerable attention for generating clean and unlimited energy. The presence of energy storage systems (ESSs) in the promising field of active distribution networks (ADNs) would have direct impact on power system problems such as encountered in probabilistic distributed generation (DG) model studies. Hence, the optimal procedure is offered herein, in which the simultaneous placement of an ESS, photovoltaic-based DG, and wind turbine-based DG in an ADN is taken into account. The main goal of this paper is to maximize the net present value of the loss reduction benefit by considering the price of electricity for each load state. The proposed framework consists of a scenario tree method for covering the existing uncertainties in the distribution network's load demand as well as DG. The collected results verify the considerable effect of concurrent installation of probabilistic DG models and an ESS in defining the optimum site of DG and the ESS and they demonstrate that the optimum operation of an ESS in the ADN is consequently related to the highest value of the loss reduction benefit in long-term planning as well. The results obtained are encouraging.

• Journal of Korea Multimedia Society
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• v.16 no.3
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• pp.269-280
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• 2013

In this paper, we suggested the possibility of using an efficient classifier for the dependency analysis of retinal disease. First, we analyzed the classification performance and the prediction accuracy of GBN (General Bayesian Network), GBN with reduced features by Markov Blanket and TAN (Tree-Augmented Naive Bayesian Network) among the various bayesian networks. And then, for the first time, we applied TAN showing high performance to the dependency analysis of the clinical data of retinal disease. As a result of this analysis, it showed applicability in the diagnosis and the prediction of prognosis of retinal disease.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.190-194
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• 2023

By looking the importance of communication, data delivery and access in various sectors including governmental, business and individual for any kind of data, it becomes mandatory to identify faults and flaws during cyber communication. To protect personal, governmental and business data from being misused from numerous advanced attacks, there is the need of cyber security. The information security provides massive protection to both the host machine as well as network. The learning methods are used for analyzing as well as preventing various attacks. Machine learning is one of the branch of Artificial Intelligence that plays a potential learning techniques to detect the cyber-attacks. In the proposed methodology, the Decision Tree (DT) which is also a kind of supervised learning model, is combined with the different cross-validation method to determine the accuracy and the execution time to identify the cyber-attacks from a very recent dataset of different network attack activities of network traffic in the UNSW-NB15 dataset. It is a hybrid method in which different types of attributes including Gini Index and Entropy of DT model has been implemented separately to identify the most accurate procedure to detect intrusion with respect to the execution time. The different DT methodologies including DT using Gini Index, DT using train-split method and DT using information entropy along with their respective subdivision such as using K-Fold validation, using Stratified K-Fold validation are implemented.