• Journal of KIISE:Information Networking
• /
• v.36 no.5
• /
• pp.437-455
• /
• 2009

In the near future, various multicast based services will be provided over clustered wireless networks. To provide multicast services in secure manner, multicast messages are encrypted by using group key which is shared by group members. Therefore, various group key management schemes have been introduced until now. Among them, tree based approach is one of the most representative paradigms in group key management. Traditional tree based approaches effectively reduce rekeying message transmissions of the key distribution center. However, they do not consider the network bandwidth used for transmitting the rekeying messages. In this paper, we firstly present formulas that describe bandwidth consumption of tree based group key management scheme. Based on our formulations, we propose a bandwidth efficient key tree management scheme for clustered wireless networks where membership changes occur frequently. Simulation results show that our scheme effectively reduces the bandwidth consumption used for rekeying compared to existing key tree schemes.

• KIPS Transactions on Computer and Communication Systems
• /
• v.11 no.4
• /
• pp.127-132
• /
• 2022

An anonymous encrypted communication networks that make it difficult to identify the trace of a user's access by passing through several virtual computers and/or networks, such as Tor, provides user and data privacy in the process of Internet communications. However, when it comes to abuse for inappropriate purposes, such as sharing of illegal contents, arms trade, etc. through such anonymous encrypted communication networks, it is difficult to detect and take appropriate countermeasures. In this paper, by extending the website fingerprinting technique that can identify access to a specific site even in anonymous encrypted communication, a method for specifying and classifying service types of websites for not only well-known sites but also unknown sites is proposed. This approach can be used to identify hidden sites that can be used for malicious purposes.

• Journal of the Korea Society of Computer and Information
• /
• v.14 no.11
• /
• pp.169-178
• /
• 2009

There are various approaches that retrieve information from video. However, previous approaches have considered just object information and relationship between objects without story information to retrieve contents. To retrieve exact information at video, we need analyzing approach based on characters and community since these are body of story proceeding. Therefore, this paper describes video information retrieval methodology based on character information. Characters progress story to form relationship through conversations. We can analyze the relationship between characters in a story with the methods that classifies role grades and clusters communities of characters. In this paper, for these, we propose the Character-net and describe how to classify role grades and cluster communities at Character-net. And we show this method to be efficient.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2022.05a
• /
• pp.329-329
• /
• 2022

Accurate characterization of terrestrial precipitation variation from high spatial resolution satellite sensors is beneficial for urban hydrology and microscale agriculture modeling, as well as natural disasters (e.g., urban flooding) early warning. However, the widely-used top-down approach for precipitation retrieval from microwave satellites is limited in several hydrological and agricultural applications due to their coarse spatial resolution. In this research, we aim to apply a novel bottom-up method, the parameterized SM2RAIN, where precipitation can be estimated from soil moisture signals based on an inversion of water balance model, to generate high spatial resolution terrestrial precipitation estimates at 0.01º grid (roughly 1-km) from the C-band SAR Sentinel-1. This product was then tested against a common reanalysis-based precipitation data and a domestic rain gauge network from the Korean Meteorological Administration (KMA) over central South Korea, since a clear difference between climatic types (coasts and mainlands) and land covers (croplands and mixed forests) was reported in this area. The results showed that seasonal precipitation variability strongly affected the SM2RAIN performances, and the product derived from separated parameters (rainy and non-rainy seasons) outperformed that estimated considering the entire year. In addition, the product retrieved over the mainland mixed forest region showed slightly superior performance compared to that over the coastal cropland region, suggesting that the 6-day time resolution of S1 data is suitable for capturing the stable precipitation pattern in mainland mixed forests rather than the highly variable precipitation pattern in coastal croplands. Future studies suggest comparing this product to the traditional top-down products, as well as evaluating their integration for enhancing high spatial resolution precipitation over entire South Korea.

• Convergence Security Journal
• /
• v.23 no.4
• /
• pp.61-70
• /
• 2023

Clustering is an unsupervised learning method that involves grouping data based on features such as distance metrics, using data without known labels or ground truth values. This method has the advantage of being applicable to various types of data, including images, text, and audio, without the need for labeling. Traditional clustering techniques involve applying dimensionality reduction methods or extracting specific features to perform clustering. However, with the advancement of deep learning models, research on deep clustering techniques using techniques such as autoencoders and generative adversarial networks, which represent input data as latent vectors, has emerged. In this study, we propose a deep clustering technique based on deep learning. In this approach, we use an autoencoder to transform the input data into latent vectors, and then construct a vector space according to the cluster structure and perform k-means clustering. We conducted experiments using the MNIST and Fashion-MNIST datasets in the PyTorch machine learning library as the experimental environment. The model used is a convolutional neural network-based autoencoder model. The experimental results show an accuracy of 89.42% for MNIST and 56.64% for Fashion-MNIST when k is set to 10.

• Journal of KIISE:Computing Practices and Letters
• /
• v.16 no.7
• /
• pp.794-798
• /
• 2010

Assuming that a wireless link is mostly used at the network edge and the wireless NIC driver keeps monitoring the error rate of its link, this paper proposes an enhanced TCP congestion control, TCP-L (TCP Link-Aware). TCP-L predicts true congestion losses occurred inside the wired link area by utilizing the wireless link error rate. As a result, it mitigates performance degradation caused from TCP congestion control actions when segments losses occur in a wireless link. Experimental results show that TCP-L provides better performance and fairness in lossy wireless links than existing TCP congestion control schemes. Our approach utilizing the characteristic of the link at TCP could be well adapted to new wireless environments such as Cognitive Radio and ACK-less IEEE 802.11, where a frame may be delivered with a very long delay or lost in the link.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.32 no.9B
• /
• pp.563-568
• /
• 2007

This paper proposes a routing establishment method for improving path stability in mobile ad-hoc networks. In mobile ad-hoc networks, the network topology is highly dynamic due to the node mobility unlike wired networks. Since the existing methods are based on the shortest path algorithm with the minimum hop count regardless of the path stability, it could lead to packet loss and path disconnection in mobile ad-hoc networks. In particular, if control packets and critical data are transmitted on the unstable path, it causes serious problems. Therefore, this paper proposes one approach in order to minimize packet loss and path disconnection by considering the node mobility. After the destination node receives multiple RREQ messages, it selects the stable path through the proposed MinMax algorithm according to the node speed.

• The Journal of the Korea Contents Association
• /
• v.11 no.10
• /
• pp.59-68
• /
• 2011

As the role of soccer game analyst who analyzes soccer games and creates soccer wining strategies is emphasized, it is required to have high-level analysis beyond the procedural ones such as main event detection in the context of IT based broadcasting soccer game research community. In this paper, we propose a novel approach to generate the high-level in-depth analysis results via real-time text based soccer Webcast and text mining. Proposed method creates a metadata such as attribute, action and event, build index, and then generate available knowledges via text mining techniques such as association rule mining, event growth index, and pathfinder network analysis using Webcast and domain knowledges. We carried out a feasibility experiment on the proposed technique with the Webcast text about Spain team's 2010 World Cup games.

• The KIPS Transactions:PartC
• /
• v.12C no.6 s.102
• /
• pp.865-874
• /
• 2005

The information stored in the computer system needs to be protected from unauthorized access, malicious destruction or alteration and accidental inconsistency. In this paper, we propose an intrusion detection system based on sensor concept for defecting and preventing malicious attacks We use software sensor objects which consist of sensor file for each important directory and sensor data for each secret file. Every sensor object is a sort of trap against the attack and it's touch tan be considered as an intrusion. The proposed system is a new challenge of setting up traps against most interception threats that try to copy or read illicitly programs or data. We have implemented the proposed system on the Linux operating system using loadable kernel module technique. The proposed system combines host~based detection approach and network-based one to achieve reasonably complete coverage, which makes it possible to detect unknown interception threats.

• Journal of KIISE:Information Networking
• /
• v.33 no.2
• /
• pp.165-174
• /
• 2006

Data dissemination using either flooding or legacy ad-hoc routing protocol is not realistic approach in the wireless sensor networks, which are composed of sensor nodes with very weak computing power, small memory and limited battery. In this paper, we propose the ELF(Energy-efficient Localized Flooding) protocol. The ELF is energy-efficient data dissemination protocol for wireless sensor networks. In the ELF protocol, there are two data delivery phases between fixed source and mobile sink node. The first phase, before the tracking zone, sensing data are forwarded by unicasting. After that, within the tracking zone, sensing data are delivered by localized flooding. Namely, the ELF Properly combines advantages from both unicasting and flooding. According to evaluation results by simulation, the proposed ELF protocol maintains very high data delivery ratio with using a little energy. Also, the property of average delay is better than others. From our research results, the ELF is very effective data dissemination protocol for wireless sensor networks.