• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.12 no.4
• /
• pp.29-39
• /
• 2002

In general, the intrusion detection method of anomalous behaviors has high false alarm rate which contains false-positive and false-negative. To increase the sensitivity of intrusion detection, we propose a method of aggregate detection to reduce false alarm rate by using correlation between misuse activity detection sensors and anomalous ones. For each normal behavior and anomalous one, we produce the reflection rate between the result from one sensor and another in off-line. Then, we apply this rate to the result of real-time detection to reduce false alarm rate.

• Journal of Environmental Impact Assessment
• /
• v.13 no.5
• /
• pp.263-276
• /
• 2004

This research is primarily intended to propose a new concept for aggregate control of impervious coverage using remote sensing and GIS. An empirical study for a case study site was conducted to demonstrate how a standard remote sensing and GIS technology can be used to assist in implementing the aggregate control for impervious coverage as intermediary between decision makers and scientists. Guidelines for a replicable methodology are presented to provide a strong theoretical basis for the standardization of factors involved in the aggregate control; the meaningful definition of land mosaic in terms of pervious areas, classification of pervious intensity, change detection for pervious areas. Detailed visual maps (e.g. estimation of impervious surface allowable) can be generated over large areas quickly and easily to increase the scientific and objective decision-making for the aggregate control. It is anticipated that this research output could be used as a valuable reference to confirm the potential of remote sensing and GIS in the aggregate control for impervious coverage.

• KIPS Transactions on Software and Data Engineering
• /
• v.11 no.6
• /
• pp.255-266
• /
• 2022

The quality control of coarse aggregate among aggregates, which are the main ingredients of concrete, is currently carried out by SPC(Statistical Process Control) method through sampling. We construct a smart factory for manufacturing innovation by changing the quality control of coarse aggregates to inspect the coarse aggregates based on this image by acquired images through the camera instead of the current sieve analysis. First, obtained images were preprocessed, and HED(Hollistically-nested Edge Detection) which is the filter learned by deep learning segment each object. After analyzing each aggregate by image processing the segmentation result, fineness modulus and the aggregate shape rate are determined by analyzing result. The quality of aggregate obtained through the video was examined by calculate fineness modulus and aggregate shape rate and the accuracy of the algorithm was more than 90% accurate compared to that of aggregates through the sieve analysis. Furthermore, the aggregate shape rate could not be examined by conventional methods, but the content of this paper also allowed the measurement of the aggregate shape rate. For the aggregate shape rate, it was verified with the length of models, which showed a difference of ±4.5%. In the case of measuring the length of the aggregate, the algorithm result and actual length of the aggregate showed a ±6% difference. Analyzing the actual three-dimensional data in a two-dimensional video made a difference from the actual data, which requires further research.

• Journal of Internet Computing and Services
• /
• v.5 no.4
• /
• pp.23-33
• /
• 2004

In this paper, we propose a novel traffic measurement based detection of network attack symptoms on high speed Internet backbone links. In order to do so, we characterize the traffic patterns from the normal and the network attacks appeared on Internet backbone links, and we derive two efficient measures for representing the network attack symptoms at aggregate traffic level. The two measures are the power spectrum and the ratio of packet counts to traffic volume of the aggregate traffic. And, we propose a new methodology to detect networks attack symptoms by measuring those traffic measures. Experimental results show that the proposed scheme can detect the network attack symptoms very exactly and quickly. Unlike existing methods based on Individual packets or flows, since the proposed method is operated on the aggregate traffic level. the computational complexity can be significantly reduced and applicable to high speed Internet backbone links.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.13 no.3
• /
• pp.135-144
• /
• 2003

An intrusion detection system shows different result over overall detection area according to its detection characteristics of inner detection algorithms or techniques. To expand detection areas, we requires an integrated detection which can be archived both by deploying a few detection systems which detect different detection areas and by combining their results. In addition to expand detection areas, we need to decrease the workload of security managers by false alarms and improve the correctness by minimizing false alerts which happen during the process of integration. In this paper, a method for aggregation detection use fuzzy inference to integrate a vague detection results which imply the characteristics of detection systems. Their analyzed detection characteristics are expressed as fuzzy membership functions and fuzzy rule bases which are applied through the process of fuzzy control. And, it integrate a vague decision results and minimize the number of false alerts by reflecting the characteristics of detection systems. Also it does minimize inference objects by applying thresholds decided through several experiments.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.6 no.11
• /
• pp.2940-2955
• /
• 2012

In this paper, we propose aggregate signcryption for achieving data security in UWSNs. The main challenge of these networks established in sensitive environments is offline sink visiting. Moreover, the sensors must retain collected data for long enough time to offload them onto the itinerant sink. Thus, the unattended nature of data collection intervals might offer the adversary the opportunity to apply various attacks without detection. In this paper, employing low order operations (in time and space), we propose a new secure scheme in which various security goals such as confidentiality (through encrypting), authentication and integrity (through signing) are achieved. In addition, the aggregation process of our scheme reduces the space and communication overheads both for sensors and sink, i.e. the proposed technique efficiently enables the sensors and sink to protect, verify and recover all the related data. We further compare our scheme with the best alternative work in the literature.

• Journal of Environmental Science International
• /
• v.29 no.2
• /
• pp.201-207
• /
• 2020

A new chemosensor based on a self-assembled system has been devised to detect Hg²⁺ions efficiently. We demonstrated that the amphiphilic building blocks consisting of pyrene and boronic acid (1) aggregate in aqueous solutions and provide an outstanding sensing platform for sensitive detection. The self-assembled 1 exhibited high selectivity and sensitivity for Hg²⁺ion detection via fluorescence quenching, where the Hg²⁺ion detection ensued from a fast transmetallation of 1. The Stern-Volmer (SV) quenching constant for its fluorescence quenching by Hg²⁺ions was approximately 1.58 × 10⁸ M^-1. In addition, self-assembled 1 exhibited excellent sensing abilities at nano-molar concentration levels when tap water and freshwater samples were contaminated with of Hg²⁺ ions.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.2 no.3
• /
• pp.135-149
• /
• 2008

Traditional attack detection schemes based on packets or flows have very high computational complexity. And, network based anomaly detection schemes can reduce the complexity, but they have a limitation to figure out the pattern of the distributed global scale network attack. In this paper, we propose an efficient and fast method for detecting distributed global-scale network attack symptoms in high-speed backbone networks. The proposed method is implemented at the aggregate traffic level. So, our proposed scheme has much lower computational complexity, and is implemented in very high-speed backbone networks. In addition, the proposed method can detect attack patterns, such as attacks in which the target is a certain host or the backbone infrastructure itself, via collaboration of edge routers on the backbone network. The effectiveness of the proposed method are demonstrated via simulation.

• ETRI Journal
• /
• v.34 no.6
• /
• pp.892-899
• /
• 2012

We analyze a wideband spectrum in a cognitive radio (CR) network by employing the optimal adaptive multiband sensing-time joint detection framework. This framework detects a wideband M-ary quadrature amplitude modulation (M-QAM) primary signal over multiple nonoverlapping narrowband Gaussian channels, using the energy detection technique so as to maximize the throughput in CR networks while limiting interference with the primary network. The signal detection problem is formulated as an optimization problem to maximize the aggregate achievable secondary throughput capacity by jointly optimizing the sensing duration and individual detection thresholds under the overall interference imposed on the primary network. It is shown that the detection problems can be solved as convex optimization problems if certain practical constraints are applied. Simulation results show that the framework under consideration achieves much better performance for M-QAM than for binary phase-shift keying or any real modulation scheme.

• Computers and Concrete
• /
• v.10 no.2
• /
• pp.135-147
• /
• 2012

At mesoscale, concrete is considered as a three-phase composite material consisting of the aggregate particles, the cement matrix and the interfacial transition zone (ITZ). The reconstruction of the internal structures for concrete composites requires the identification of the boundary of the aggregate particles and the cement matrix using digital imaging technology followed by post-processing through MATLAB. A parameter study covers the subsection transformation, median filter, and open and close operation of the digital image sample to obtain the optimal parameter for performing the image processing technology. The subsection transformation is performed using a grey histogram of the digital image samples with a threshold value of [120, 210] followed by median filtering with a $16{\times}16$ square module based on the dimensions of the aggregate particles and their internal impurity. We then select a "disk" tectonic structure with a specific radius, which performs open and close operations on the images. The edges of the aggregate particles (similar to the original digital images) are obtained using the canny edge detection method. The finite element model at mesoscale can be established using the proposed image processing technology. The location of the crack determined through the numerical method is identical to the experimental result, and the load-displacement curve determined through the numerical method is in close agreement with the experimental results. Comparisons of the numerical and experimental results show that the proposed image processing technology is highly effective in reconstructing the internal structures of concrete composites.