• Journal of Korea Multimedia Society
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• v.14 no.11
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• pp.1438-1449
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• 2011

UWSNs(Underwater Wireless Sensor Networks) need effective modeling fitted to the customized type of application and its covering area. In particular it requires an energy efficient data aggregation method for such customized application. In this paper, we envisage the application oriented model for monitoring the pollution or intrusion detection over a given underwater area. The suggested model is based on the honeycomb array of hexagonal prisms. In this model, the purpose of data aggregation is that the head node of each layer(cluster) receives just one event data arrived firstly and transfer this and its position data to the base station effectively in the manner of energy efficiency and simplicity without duplication. Here if we apply the existent data aggregation methods to this kind of application, the result is far from energy efficiency due to the complexity of the data aggregation process based on the shortest path or multicast tree. In this paper we propose three energy efficient and simple data aggregation methods in the domain of cluster and three in the domain of inter-cluster respectively. Based on the comparative performance analysis of the possible combination pairs in the two domains, we derive the best energy efficient data aggregation method for the suggested application.

• Journal of Internet Computing and Services
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• v.17 no.6
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• pp.1-8
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• 2016

Current indoor intrusion detection and location tracking methods have the weakness in seamless operations in tracking the objective because the object must possess a communicating device and the limitation of the single cell size (approximate $100cm{\times}100cm$) exits. Also, the utilization of CCTV technologies show the shortcomings in tracking when the object disappear the area where the CCTV is not installed or illumination is not enough for capturing the scene (e.g. where the context-awarded system is not installed or low illumination presents). Therefore, in this paper we present an improved in-door tracking system based on sensor networks. Such system is built on a simulated scenario and enables us to detect and extend the area of surveillance as well as actively responding the emergency situation. Through simulated studies, we have demonstrated that the proposed system is capable of supplementing the shortcomings of signal cutting, and of estimating the location of the moving object. We expect the study will improve the better analysis of the intruder behavior, the more effective prevention and flexible response to various emergency situations.

• 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 Society of Computer and Information
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• v.17 no.10
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• pp.79-88
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• 2012

In this paper, we design a mutual authentication protocol which divided sessions from an authenticated session are updated periodically. And in order to minimize the traffic overhead for session authentication, we also introduce dynamic session management according to sampling rate of medical sensor type. And randomize the divided session time. This model has the effect of blocking the integrity and confidentiality intrusion of rogue gateway. Moreover, efficiency is provided through medical data to be transmitted have different sampling rate. In order to evaluate this model, it was embodied and experimented in TinyOS 2.1 environment. The result, we got an overall validity from three types of experiment.

• Smart Media Journal
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• v.12 no.11
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• pp.36-47
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• 2023

Industrial Control System(ICS), which controls facilities at major industrial sites, is increasingly connected to other systems through networks. With this integration and the development of intelligent attacks that can lead to a single external intrusion as a whole system paralysis, the risk and impact of security on industrial control systems are increasing. As a result, research on how to protect and detect cyber attacks is actively underway, and deep learning models in the form of unsupervised learning have achieved a lot, and many abnormal detection technologies based on deep learning are being introduced. In this study, we emphasize the application of preprocessing methodologies to enhance the anomaly detection performance of deep learning models on time series data. The results demonstrate the effectiveness of a Wavelet Transform (WT)-based noise reduction methodology as a preprocessing technique for deep learning-based anomaly detection. Particularly, by incorporating sensor characteristics through clustering, the differential application of the Dual-Tree Complex Wavelet Transform proves to be the most effective approach in improving the detection performance of cyber attacks.