• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.5
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• pp.949-959
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• 2024

Recently, as enterprises utilize the cloud and artificial intelligence, it is becoming increasingly difficult to protect exposed interfaces with existing perimeter security methods. Accordingly, zero trust-based comprehensive risk management is becoming necessary. Most enterprises use vulnerability inspection and bug bounty (security vulnerability reporting system) as basic risk management methods, but it is difficult to effectively respond to unpredictable problems such as zero-day attacks or open source vulnerabilities with these methods alone. Therefore, in this paper, we propose a risk response technique for the entire enterprise that links external OSINT (open source information) and CTI of national government agencies to detect threats through CTI (cyber threat intelligence) and collects the enterprise's own CTI. As a result of comparing the method of threat detection and blocking that collects the enterprise's own CTI by configuring a honeypot for effective threat detection and links it to the CTI of an external government agency, the proposed technique showed a 65.8% higher performance improvement in detection accuracy and verified the effect of reducing the number of attackers in the organization through this method

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

• International Journal of Control, Automation, and Systems
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• v.1 no.1
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• pp.119-126
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• 2003

The problem of tracking moving objects in a video stream is discussed in this pa-per. We discussed the popular technique of optical flow for moving object detection. Optical flow finds the velocity vectors at each pixel in the entire video scene. However, optical flow based methods require complex computations and are sensitive to noise. In this paper, we proposed a new method based on the Hough transform and on voting accumulation for improving the accuracy and reducing the computation time. Further, we applied the Boo-lean based edge detector for edge detection. Edge detection and segmentation are used to extract the moving objects in the image sequences and reduce the computation time of the CHT. The Boolean based edge detector provides accurate and very thin edges. The difference of the two edge maps with thin edges gives better localization of moving objects. The simulation results show that the proposed method improves the accuracy of finding the optical flow vectors and more accurately extracts moving objects' information. The process of edge detection and segmentation accurately find the location and areas of the real moving objects, and hence extracting moving information is very easy and accurate. The Combinatorial Hough Transform and voting accumulation based optical flow measures optical flow vectors accurately. The direction of moving objects is also accurately measured.

• Journal of Korea Multimedia Society
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• v.6 no.1
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• pp.67-77
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• 2003

Watermark detection has a crucial role in copyright protection and authentication for multimedia Because be the correlation -based algorithm which has widely been used in the watermark detection doesn't utilize the distributional characteristics of cover image to be marked, its performance is not optimum. So a new detection algorithm is proposed which is optimum for multiplicative watermark embedding. By relying on statistical decision method, the proposed method is derived according to the Bayes decision theory. Neyman Pearson criterion, and distribution of wavelet coefficients, thus Permitting to minimize the missed detection probability subject to a given false detection probability The superiority of the proposed method has been tested from a robustness perspective. The results confirm the superiority of the proposed technique over classical correlation -based method.

• Journal of Advanced Navigation Technology
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• v.14 no.1
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• pp.41-48
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• 2010

Intrusion detection forms a vital component of internet security. To keep pace with the growing trends, there is a critical need to replace single layer detection technology with multi layer detection. Different types of Denial of Service (DoS) attacks thwart authorized users from gaining access to the networks and we tried to detect as well as alleviate some of those attacks. We have proposed a novel cross layer intrusion detection architecture to discover the malicious nodes. The information available across different layers of protocol stack are exploited in order to improve the accuracy of detection. We have used cooperative and distributive anomaly intrusion detection with data mining technique to enhance the proposed architecture. The simulation of the proposed architecture is done in OPNET simulator and the results are analyzed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.1
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• pp.87-94
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• 2009

The intrusion detection system is one of the active fields of research in wireless networks. Intrusion detection in wireless mobile Ad hoc network is challenging because the network topologies are dynamic, lack centralization and are vulnerable to attacks. Detection of malicious nodes in an open ad-hoc network in which participating nodes do not have previous security association has to face number of challenges which is described in this paper. This paper is about determining the malicious nodes under critical conditions in the mobile ad-hoc network and deals with security and vulnerabilities issues which results in the better performance and detection of the intrusion.

• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1886-1898
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• 2015

In today’s era of advanced technological developments, the threats to the authenticity and integrity of digital images, in a nutshell, the threats to the Image Forensics Research communities have also increased proportionately. This happened as even for the ‘non-expert’ forgers, the availability of image processing tools has become a cakewalk. This image forgery poses a great problem for judicial authorities in any context of trade and commerce. Block matching based image cloning detection system is widely researched over the last 2-3 decades but this was discouraged by higher computational complexity and more time requirement at the algorithm level. Thus, for reducing time need, various dimension reduction techniques have been employed. Since a single technique cannot cope up with all the transformations like addition of noise, blurring, intensity variation, etc. we employ multiple techniques to a single image. In this paper, we have used Fuzzy logic approach for decision making and getting a global response of all the techniques, since their individual outputs depend on various parameters. Experimental results have given enthusiastic elicitations as regards various transformations to the digital image. Hence this paper proposes Fuzzy based cloning detection and classification system. Experimental results have shown that our detection system achieves classification accuracy of 94.12%. Detection accuracy (DAR) while in case of 81×81 sized copied portion the maximum accuracy achieved is 99.17% as regards subjection to transformations like Blurring, Intensity Variation and Gaussian Noise Addition.

• The Journal of Korea Robotics Society
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• v.8 no.4
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• pp.238-246
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• 2013

In this paper, a robot vision technique is presented to detect obstacles, particularly approaching humans, in the images acquired by a mobile robot that autonomously navigates in a narrow building corridor. A single low-cost color camera is attached to the robot, and a trapezoidal area is set as a region of interest (ROI) in front of the robot in the camera image. The lower parts of a human such as feet and legs are first detected in the ROI from their appearances in real time as the distance between the robot and the human becomes smaller. Then, the human detection is confirmed by detecting his/her face within a small search region specified above the part detected in the trapezoidal ROI. To increase the credibility of detection, a final decision about human detection is made when a face is detected in two consecutive image frames. We tested the proposed method using images of various people in corridor scenes, and could get promising results. This method can be used for a vision-guided mobile robot to make a detour for avoiding collision with a human during its indoor navigation.

• Journal of the Korea Society of Computer and Information
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• v.21 no.4
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• pp.9-15
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• 2016

Symmetry is everywhere in the world around us from galaxy to microbes. From ancient times symmetry is considered to be a reflection of the harmony of universe. Symmetry is not only a significant clue for human cognitive process, but also useful information for computer vision such as image understanding system. Application areas include face detection and recognition, indexing of image database, image segmentation and detection, analysis of medical images, and so on. The technique used in this paper extracts edges, and the perpendicular bisector of any two edge points is considered to be a candidate axis of symmetry. The coefficients of candidate axis are accumulated in the coefficient space. Then the axis of symmetry is determined to be the line for which the coefficient histogram has maximum value. In this paper, an improved method is proposed that utilizes the directional information of edges, which is a byproduct of the edge detection process. Experiment on 20 test images shows that the proposed method performs 22.7 times faster than the original method. In another test on 5 images with 4% salt-and-pepper noise, the proposed method detects the symmetry successfully, while the original method fails. This result reveals that the proposed method enhances the speed and accuracy of detection process at the same time.

• IEMEK Journal of Embedded Systems and Applications
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• v.12 no.4
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• pp.231-238
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• 2017

Detecting small infrared targets from the low-SCR images at a long distance is very hard. The previous Local Contrast Method (LCM) algorithm based on the human visual system shows a superior performance of detecting small targets by a background suppression technique through local contrast measure. However, its slow processing speed due to the heavy multi-scale processing overhead is not suitable to a variety of real-time applications. This paper presents a lightweight real-time small target detection algorithm, called by the Improved Selective Local Contrast Method (ISLCM), to reduce the scale-invariant computational overhead. The proposed ISLCM applies the improved local contrast measure to the predicted selective region so that it may have a comparable detection performance as the previous LCM while guaranteeing low scale-invariant computational load by exploiting both adaptive scale estimation and small target feature feasibility. Experimental results show that the proposed algorithm can reduce its computational overhead considerably while maintaining its detection performance compared with the previous LCM.