• 한국HCI학회:학술대회논문집
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• 2007.02c
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• pp.306-317
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• 2007

We present an interactive and accurate collision detection algorithm for deformable, polygonal objects based on the streaming computational model. Our algorithm can detect all possible pairwise primitive-level intersections between two severely deforming models at highly interactive rates. In our streaming computational model, we consider a set of axis aligned bounding boxes (AABBs) that bound each of the given deformable objects as an input stream and perform massively-parallel pairwise, overlapping tests onto the incoming streams. As a result, we are able to prevent performance stalls in the streaming pipeline that can be caused by expensive indexing mechanism required by bounding volume hierarchy-based streaming algorithms. At run-time, as the underlying models deform over time, we employ a novel, streaming algorithm to update the geometric changes in the AABB streams. Moreover, in order to get only the computed result (i.e., collision results between AABBs) without reading back the entire output streams, we propose a streaming en/decoding strategy that can be performed in a hierarchical fashion. After determining overlapped AABBs, we perform a primitive-level (e.g., triangle) intersection checking on a serial computational model such as CPUs. We implemented the entire pipeline of our algorithm using off-the-shelf graphics processors (GPUs), such as nVIDIA GeForce 7800 GTX, for streaming computations, and Intel Dual Core 3.4G processors for serial computations. We benchmarked our algorithm with different models of varying complexities, ranging from 15K up to 50K triangles, under various deformation motions, and the timings were obtained as 30~100 FPS depending on the complexity of models and their relative configurations. Finally, we made comparisons with a well-known GPU-based collision detection algorithm, CULLIDE [4] and observed about three times performance improvement over the earlier approach. We also made comparisons with a SW-based AABB culling algorithm [2] and observed about two times improvement.

• Journal of the Korean Magnetics Society
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• v.16 no.6
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• pp.305-308
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• 2006

Magnetic flux leakage (MFL) pigs are traditionally used for the detection of gross corrosion on steel pipelines used for the transmission of natural gas. Alternative nondestructive evaluation (NDE) modalities are required for the detection of stress corrosion cracking (SCC) which tends to exist in colonies oriented axially along the length of the pipeline. This paper describes the use of multiphase rotating magnetic fields in the remote region of the probe as a possible SCC detection mechanism. Details of a prototype pig and test rig are given and the challenges associated with the finite element modeling of the device are discussed. Initial experimental results show that this novel NDE modality is sensitive to axially oriented tight cracks.

• Convergence Security Journal
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• v.9 no.4
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• pp.21-27
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• 2009

In the environment of development of network bandwidth and intrusion technology there is limit to the pattern analysis of all massed packets through the existing pattern matching method by the intrusion detection system. To detect the packets efficiently when they are received fragmented, it has been presented the matching method only the pattern of packets consisting with the operation system such as Esnort. Pattern matching performance is improved through the use of NMAP, the basic mechanism od Esnort, by scanning the operation system of the same network system and appling pattern match selectively scanned information and the same operation system as the received packets. However, it can be appeared the case of disregarding the receivied packets depending on the diversity of the kind of operation systems and recognition mistake of operation system of nmap. In this paper, we present and verify the improved intrusion detection system shortening the pattern matching time by the creation of hashy table through the pattern hash of intrusion detection system independently with the users system environment .in the state of flux.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.4
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• pp.661-674
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• 2021

Malware, such as a rootkit that modifies the kernel, can adversely affect the analyst's judgment, making the analysis difficult or impossible if a mechanism to evade memory analysis is added. Therefore, we plan to preemptively respond to malware such as rootkits that bypass detection through advanced kernel modulation in the future. To this end, the main structure used in the Windows kernel was analyzed from the attacker's point of view, and a method capable of modulating the kernel object was applied to modulate the memory dump file. The result of tampering is confirmed through experimentation that it cannot be detected by memory analysis tool widely used worldwide. Then, from the analyst's point of view, using the concept of tamper resistance, it is made in the form of software that can detect tampering and shows that it is possible to detect areas that are not detected by existing memory analysis tools. Through this study, it is judged that it is meaningful in that it preemptively attempted to modulate the kernel area and derived insights to enable precise analysis. However, there is a limitation in that the necessary detection rules need to be manually created in software implementation for precise analysis.

• Journal of Internet of Things and Convergence
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• v.9 no.6
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• pp.1-9
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• 2023

Malware files containing concealed malicious scripts have recently been identified within MS Office documents frequently. In response, this paper describes the design and implementation of a system that automatically detects malicious digital files using machine learning techniques. The system is proficient in identifying malicious scripts within MS Office files that exploit the OLE VBA macro functionality, detecting malicious scripts embedded within the CDH/LFH/ECDR internal field values through OOXML structure analysis, and recognizing abnormal CDH/LFH information introduced within the OOXML structure, which is not conventionally referenced. Furthermore, this paper presents a mechanism for utilizing the VirusTotal malicious script detection feature to autonomously determine instances of malicious tampering within MS Office files. This leads to the design and implementation of a machine learning-based integrated software. Experimental results confirm the software's capacity to autonomously assess MS Office file's integrity and provide enhanced detection performance for arbitrary MS Office files when employing the optimal machine learning model.

• Journal of Information Processing Systems
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• v.20 no.3
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• pp.375-390
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• 2024

Edge computing architecture has effectively alleviated the computing pressure on cloud platforms, reduced network bandwidth consumption, and improved the quality of service for user experience; however, it has also introduced new security issues. Existing anomaly detection methods in big data scenarios with cloud-edge computing collaboration face several challenges, such as sample imbalance, difficulty in dealing with complex network traffic attacks, and difficulty in effectively training large-scale data or overly complex deep-learning network models. A lightweight deep-learning model was proposed to address these challenges. First, normalization on the user side was used to preprocess the traffic data. On the edge side, a trained Wasserstein generative adversarial network (WGAN) was used to supplement the data samples, which effectively alleviates the imbalance issue of a few types of samples while occupying a small amount of edge-computing resources. Finally, a trained lightweight deep learning network model is deployed on the edge side, and the preprocessed and expanded local data are used to fine-tune the trained model. This ensures that the data of each edge node are more consistent with the local characteristics, effectively improving the system's detection ability. In the designed lightweight deep learning network model, two sets of convolutional pooling layers of convolutional neural networks (CNN) were used to extract spatial features. The bidirectional long short-term memory network (BiLSTM) was used to collect time sequence features, and the weight of traffic features was adjusted through the attention mechanism, improving the model's ability to identify abnormal traffic features. The proposed model was experimentally demonstrated using the NSL-KDD, UNSW-NB15, and CIC-ISD2018 datasets. The accuracies of the proposed model on the three datasets were as high as 0.974, 0.925, and 0.953, respectively, showing superior accuracy to other comparative models. The proposed lightweight deep learning network model has good application prospects for anomaly traffic detection in cloud-edge collaborative computing architectures.

• The Transactions of the Korea Information Processing Society
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• v.5 no.11
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• pp.3010-3023
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• 1998

GA has a fast convergence speed in searching the one point around optimal value. But it's convergence time increase in searching the region around optimal value because it has no regional searching mechanism. BP has the tendency to converge the local minimum because it has global searching mechanism. To overcome these problems, a method in which a genetic algorithm and a back propagation are applied in turn is proposed in this paper. By using a genetic algorithm, we compute optimal synaptic strength and offset value. And then, these values are fed to the input of the back propagation. This proposed method is superior to each above method in improving the convergence speed.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.05a
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• pp.252-255
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• 2002

As more critical services are provided in the internet, the risk to these services from malicious users increases. Several networks have experienced problems like Denial of Service(DoS) attacks recently. We analyse a network-based denial of service attack, which is called SYM flooding, to TCP-based networks. It occurs by an attacker who sends TCP connection requests with spoofed source address to a target system. Each request causes the targeted system to send instantly data packets out of a limited pool of resources. Then the target system's resources are exhausted and incoming TCP port connections can not be established. The paper is concerned with a detailed analysis of TCP SYN flooding denial of service attack. In this paper, we propose a Real Time Scan Detector(RTSD) mechanism and evaluate it\`s Performance.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.27 no.6
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• pp.343-349
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• 2014

It is summarized that potential causes of performance degradations and failure mechanisms of crystalline silicon photovoltaic (PV) modules installed in Middle East area. In addition, we also reviewed current PV module qualification test (IEC 61215) and the methods for detection of wear-out fault. The failure of PV modules in the extreme environmental conditions such as deserts is mainly due to high temperature, humidity, and dust storms. In particular, cementation phenomenon caused by combination of sand and moisture leads to rapid degradation in the performance of PV modules. In order to evaluate and guarantee the long term reliability of PV modules, specific qualification tests such as sand dust test, salt mist test and potential induce degradation test considering operating environment of PV module should be carried out.

• Proceedings of the Korean Society of Plant Pathology Conference
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• 2004.10a
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• pp.48-52
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• 2004

We have found the role of rice mitogen-activated protein kinase kinase kinase (MAPKKK), OsEDR1, as controling hypersensitive response (HR) and increased disease resistance to rice blast fungus Magnaporthe grisea. Generation of transgenic rice plants through introduction of the over-expression construct of OsEDR1 using Agrobacterium-mediated transformation results in lesion mimic phenotype. Up-regulation of defense mechanism was detected through detection of increased transcription level of rice PBZ1 and PR1a. Inoculation of rice blast fungus on the lesion mimic transgenic lines displayed significantly increased resistance. The disease symptoms were arrested like HR responses which are commonly detected in the incompatible interactions. High accumulation of phenolic compounds around developing lesions was detected under UV light. There was variation among transgenic lines on the timing of lesion progression as well as the lesion numbers on the rice leaves. Transgenic lines with few lesions also show increased resistance as well as equal amount of grain yields compared to that of wild type rice cultivar Nipponbare. This is the first report of the MAPKKK as a positive regulator molecule on defense mechanism through inducing HR-like cell death lesion mimic phenotype. The application of OsEDR1 is highly expected for the development of resistant cultivars against rice pathogens.