• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.6
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• pp.1165-1170
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• 2022

In WSNs, a clustering algorithm groups sensor nodes on a unit called cluster and periodically selects a cluster head (CH) that acts as a communication relay on behalf of nodes in each cluster for the purpose of energy conservation and relay efficiency. Meanwhile, attack techniques also have emerged to intervene in the CH election process through compromised nodes (inside attackers) and have a fatal impact on network operation. However, existing countermeasures such as encryption key-based methods against outside attackers have a limitation to defend against such inside attackers. Therefore, we propose a statistical detection method that detects abnormal CH election behaviors occurs in a WSN cluster. We design two attack methods (Selfish and Greedy attacks) and our proposed defense method in WSNs with two clustering algorithms and conduct experiments to validate our proposed defense method works well against those attacks.

• Journal of the Society of Disaster Information
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• v.18 no.4
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• pp.922-929
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• 2022

Purpose: The purpose of this paper is to develop a deep learning model to detect fire extinguishers in images taken from CCTVs in underground utility tunnels. Method: Various fire extinguisher images were collected for detection of fire extinguishers in the running-based underground utility tunnel, and a model applying the One-stage Detector method was developed based on the CNN algorithm. Result: The detection rate of fire extinguishers photographed within 10m through CCTV video in the underground common area is over 96%, showing excellent detection rate. However, it was confirmed that the fire extinguisher object detection rate drops sharply at a distance of 10m or more, in a state where it is difficult to see with the naked eye. Conclusion: This paper develops a model for detecting fire extinguisher objects in underground common areas, and the model shows high performance, and it is judged that it can be used for underground common area digital twin model synchronizing.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.6
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• pp.1565-1585
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• 2012

The characteristics of ad hoc networks, such as the absence of infrastructure, a dynamic topology, a shared wireless medium and a resource-constrained environment pose various security challenges. Most previous studies focused on the detection of misbehavior after it had occurred. However, in this paper we propose a new way of thinking to evade the occurrence of misbehavior. In our scheme, we firstly present a clustering algorithm that employs a powerful analytic hierarchy process methodology to elect a clusterhead for each cluster. The clusterhead in each cluster is then allowed to assign the backoff values to its members, i.e., originators, rather than permitting the originators to choose the backoff values by themselves. Through this media access control layer misbehavior detection mechanism, the misuse of the backoff in the media access control layer in the 802.11 distributed coordination function can be detected.

• The Bulletin of The Korean Astronomical Society
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• v.39 no.1
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• pp.80-80
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• 2014

Dynamics of a globular cluster (GC) is dominated by behaviors of high-mass components such as neutron stars or black holes (BHs). Massive components in a cluster are segregated into the cluster core and some of them are ejected by dynamical interactions. In this study, we perform N-body simulations of GCs adapting two BH mass components, $10M_{\odot}$ and $20M_{\odot}$. Previous studies which mostly assume single-mass BHs suggested a rapid collapsing and escaping of BHs. A cluster with a two-component BH mass spectrum, however, retains a large fraction of $10M_{\odot}$ BHs longer. In addition to their roles in cluster dynamics, massive components in binaries are one of important sources of gravitational waves (GWs). We investigate properties of BH binaries escaped from the cluster and discuss their implications for GW detection.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.6
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• pp.1199-1203
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• 2012

This paper proposes a Scene Change Detection method using the local decision tree and clustering. The local decision tree detects cluster boundaries wherein local scenes occur, in such a way as to compare time similarity distributions among the difference values between detected scenes and their adjacent frames, and group an unbroken sequence of frames with similarities in difference value into a cluster unit.

• Proceedings of the Korean Institute of Surface Engineering Conference
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• 2015.11a
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• pp.242-243
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• 2015

Fault detection using optical emission spectra with modified K-means cluster analysis and principal component anal ysis are demonstrated for inductive coupl ed pl asma cl eaning processes. The optical emission spectra from optical emission spectroscopy (OES) are used for measurement. Furthermore, Principal component analysis and K-means cluster analysis algorithm is modified and applied to real-time detection and sensitivity enhancement for fluorocarbon cleaning processes. The proposed techniques show clear improvement of sensitivity and significant noise reduction when they are compared with single wavelength signals measured by OES. These techniques are expected to be applied to various plasma monitoring applications including fault detections as well as chamber cleaning endpoint detection.

• Asia pacific journal of information systems
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• v.28 no.4
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• pp.308-319
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• 2018

This paper aims at providing valuable insights on Financial Fraud Detection on a mobile money transactional activity. We have predicted and classified the transaction as normal or fraud with a small sample and massive data set using Azure and Spark ML, which are traditional systems and Big Data respectively. Experimenting with sample dataset in Azure, we found that the Decision Forest model is the most accurate to proceed in terms of the recall value. For the massive data set using Spark ML, it is found that the Random Forest classifier algorithm of the classification model proves to be the best algorithm. It is presented that the Spark cluster gets much faster to build and evaluate models as adding more servers to the cluster with the same accuracy, which proves that the large scale data set can be predictable using Big Data platform. Finally, we reached a recall score with 0.73, which implies a satisfying prediction quality in predicting fraudulent transactions.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.39.1-39.1
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• 2019

Not all stars in the Universe are gravitationally bounded to galaxies. Since first discovered in 1951, observations have revealed that a significant fraction of stars fills the space between galaxies in local (low-redshift) galaxy clusters, observed as diffuse intracluster light (ICL). Theoretical models provide mechanisms for the production of intracluster stars as tidally stripped material or debris generated through numerous galaxy interactions during the hierarchical growth of the galaxy cluster. These mechanisms predict that most intracluster stars in local galaxy clusters are long-accumulated material since z~1. However, there is no observational evidence to verify this prediction. Here we report observations of abundant ICL for a massive (above $10^{14}$ solar masses) galaxy cluster at a redshift of z=1.24, when the Universe was 5 billion years old. We found that more than 10 per cent of the total light of the cluster is contributed by the diffuse ICL out to 110 kpc from the center of the cluster, comparable to 5-20 per cent in local, massive galaxy cluster. Furthermore, we found that the colour of the brightest cluster galaxy located in the core of the cluster is consistent with that of the ICL out to 200 kpc. Our results demonstrate that the majority of the intracluster stars present in the local Universe, contrary to most previous theoretical and observational studies, were built up during a short period and early (z>1) in the history of the Virgo-like massive galaxy cluster formation, and might be concurrent with the formation of the brightest cluster galaxy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.5819-5840
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• 2018

Considering the topology of hierarchical tree structure, each cluster in WSNs is faced with various attacks launched by malicious nodes, which include network eavesdropping, channel interference and data tampering. The existing intrusion detection algorithm does not take into consideration the resource constraints of cluster heads and sensor nodes. Due to application requirements, sensor nodes in WSNs are deployed with approximately uncorrelated security weights. In our study, a novel and versatile intrusion detection system (IDS) for the optimal defense strategy is primarily introduced. Given the flexibility that wireless communication provides, it is unreasonable to expect malicious nodes will demonstrate a fixed behavior over time. Instead, malicious nodes can dynamically update the attack strategy in response to the IDS in each game stage. Thus, a multi-stage intrusion detection game (MIDG) based on Bayesian rules is proposed. In order to formulate the solution of MIDG, an in-depth analysis on the Bayesian equilibrium is performed iteratively. Depending on the MIDG theoretical analysis, the optimal behaviors of rational attackers and defenders are derived and calculated accurately. The numerical experimental results validate the effectiveness and robustness of the proposed scheme.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.1
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• pp.91-96
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• 2012

Ad-hoc networks can be used various environment, which it is difficult to construct infrastructures, such as shadowing areas, disaster areas, war area, and so on. In order to support to considerable and various wireless services, more spectrum resources are needed. However, efficient utilization of the frequency resource is difficult because of spectrum scarcity and the conventional frequency regulation. Ad-hoc networks employing cognitive radio (CR) system that guarantee high spectrum utilization provide effective way to increase the network capacity. In CR based wireless ad-hoc networks, cluster-head decides the existence of primary user using sensing information of primary user from each ad-hoc device. However, it is still defective research to decide cluster head among the a lot of ad-hoc devices. So, in this paper, we show the decision method of cluster head in CR based wireless and detection probabilities of primary user based on decision method of cluster head.