• Journal of KIISE:Software and Applications
• /
• v.30 no.5_6
• /
• pp.497-502
• /
• 2003

Content-based video copy detection is a complementary approach to watermarking. As opposed to watermarking, which relies on inserting a distinct pattern into the video stream, video copy detection techniques match content-based signatures to detect copies of video. Existing typical content-based copy detection schemes have relied on image matching which is based on key frame detection. This paper proposes a motion directional histogram, which is quantized and accumulated the direction of motion, for video copy detection. The video clip is represented by a motion directional histogram as a 1-dimensional graph. This method is suitable for real time indexing and counting the TV CF verification that is high motion video clips.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.21 no.2
• /
• pp.37-47
• /
• 2011

In the recent years, malicious codes called malware are having shown significant increase due to the code obfuscation to evade detection mechanisms. When the code obfuscation technique is applied to malwares, they can change their instruction sequence and also even their signature. These malwares which have same functionality and different appearance are able to evade signature-based AV products. Thus, AV venders paid large amount of cost to analyze and classify malware for generating the new signature. In this paper, we propose a novel approach for detecting metamorphic malwares. The proposed mechanism first converts malware's API call sequences to call graph through dynamic analysis. After that, the callgraph is converted to semantic signature using 128 abstract nodes. Finally, we extract all subgraphs and analyze how similar two malware's behaviors are through subgraph similarity. To validate proposed mechanism, we use 273 real-world malwares include obfuscated malware and analyze 10,100 comparison results. In the evaluation, all metamorphic malwares are classified correctly, and similar module behaviors among different malwares are also discovered.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2004.10a
• /
• pp.271-274
• /
• 2004

기존의 참조서명과 입력서명을 비교하는 방법 중 분절 단위 비교 방법은 전역적 방법과 점단위 방법에 비하여 우수한 장점을 가지고 있다. 그러나 분절 단위 비교 방법은 인식률과 직접적인 관계가 있는 분절의 불안정 문제점이 있다. 본 연구에서는 분절 단위 비교 방법을 이용한 서명검증의 신뢰도를 향상시키기 위해 두 가지 형태의 모델을 구축하였다. 우선 기존에 사용된 구간 분할 매칭 방법을 사용하여 매칭도를 산출하였다. 다음으로 서명의 분할된 영역을 주성분 분석 기법에 의해 특징 벡터를 산출한 후 HMM에 의해 서명 모델을 구축하였다. 산출된 두 특징을 융합하는 방법으로는 SVM 분류기를 사용하였다 실험 결과 제안된 기법은 분절 단위 기반의 구간분할매칭 기법에 비해 우수한 성능을 나타냈다.

• Journal of Electrical Engineering and Technology
• /
• v.8 no.3
• /
• pp.572-580
• /
• 2013

Power metering and monitoring system is a basic element of Smart Grid technology. This paper proposes a new Non-Intrusive Load Monitoring (NILM) method for a residential buildings sector using the measured total active power consumption. Home electrical appliances are classified by ON/OFF state models, Multi-state models, and Composite models according to their operational characteristics observed by experiments. In order to disaggregate the operation and the power consumption of each model, an algorithm which includes a switching function, a truth table matrix, and a matching process is presented. Typical profiles of each appliances and disaggregation results are shown and classified. To improve the accuracy, a Time Lagging (TL) algorithm and a Permanent-On model (PO) algorithm are additionally proposed. The method is validated as comparing the simulation results to the experimental ones with high accuracy.

• Proceedings of the IEEK Conference
• /
• 2006.06a
• /
• pp.423-424
• /
• 2006

This paper describes a 3D face recognition using different devices for 3D faces and input faces which include several different pose. Before the recognition stage, through the EC-SVD, all data have to be preprocessed and normalized. At recognition stage, we propose the multi-point signature method for measuring facial surface information. And we use the root mean square error for matching. From the experiment results, we have 92.5% recognition rate.

• Journal of Information Processing Systems
• /
• v.10 no.4
• /
• pp.555-567
• /
• 2014

Biometric performance improvement is a challenging task. In this paper, a hierarchical strategy fusion based on multimodal biometric system is presented. This strategy relies on a combination of several biometric traits using a multi-level biometric fusion hierarchy. The multi-level biometric fusion includes a pre-classification fusion with optimal feature selection and a post-classification fusion that is based on the similarity of the maximum of matching scores. The proposed solution enhances biometric recognition performances based on suitable feature selection and reduction, such as principal component analysis (PCA) and linear discriminant analysis (LDA), as much as not all of the feature vectors components support the performance improvement degree.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2015.10a
• /
• pp.1408-1410
• /
• 2015

Information about the painted road markings and other painted road objects play an important part in keeping safety of drivers. Some researchers have presented research approaches and dealt with road markings detection. In this paper, we present comprehensive survey of these techniques, and review some of them like a machine learning method, template matching method for road markings detection and classification, method of detection and classification of road markings using curve-based prototype fitting, signed edge signature method.

• Journal of KIISE:Computer Systems and Theory
• /
• v.34 no.2
• /
• pp.73-83
• /
• 2007

In network intrusion prevention, attack packets are detected and filtered out based on their attack signatures. Pattern matching is extensively used to find attack signatures and the most time-consuming execution part of Network Intrusion Prevention Systems(NIPS). Pattern matching is usually accelerated by hardware and should be performed at wire speed in NIPS. However, that alone is not good enough. First, pattern matching hardware should be able to generate sufficient pattern match information including the pattern index number and the location of the match found at wire speed. Second, it should support pattern grouping to reduce unnecessary pattern matches. Third, it should always have a constant worst-case performance even if the number of patterns is increased. Finally it should be able to update patterns in a few minutes or seconds without stopping its operations, We propose a system architecture to meet the above requirement. The system architecture can process multiple pattern characters in parallel and employs a pipeline architecture to achieve high speed. Using Xilinx FPGA simulation, we show that the new system stales well to achieve a high speed oner 10Gbps and satisfies all of the above requirements.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.34 no.11B
• /
• pp.1234-1244
• /
• 2009

Nowadays, the traffic type and behavior are extremely diverse due to the appearance of various services and applications on Internet, which makes the need of application-level traffic classification important for the efficient management and control of network resources. Although lots of methods for traffic classification have been introduced in literature, they have some limitations to achieve an acceptable level of performance in terms of accuracy and completeness. In this paper we propose an application traffic classification method using statistic signatures, defined as a directional sequence of packet size in a flow, which is unique for each application. The statistic signatures of each application are collected by our automatic grouping and extracting mechanism which is mainly described in this paper. By matching to the statistic signatures we can easily and quickly identify the application name of traffic flows with high accuracy, which is also shown by comprehensive excrement with our campus traffic data.

• Journal of Internet Computing and Services
• /
• v.12 no.6
• /
• pp.117-128
• /
• 2011

In this paper, we present a novel approach for contents-based medication image retrieval from a medication image database using the shape classification and color information of the medication. One major problem in developing a contents-based drug image retrieval system is there are too many similar images in shape and color and it makes difficult to identify any specific medication by a single feature of the drug image. To resolve such difficulty in identifying images, we propose a hybrid approach to retrieve a medication image based on shape and color features of the medication. In the first phase of the proposed method we classify the medications by shape of the images. In the second phase, we identify them by color matching between a query image and preclassified images in the first phase. For the shape classification, the shape signature, which is unique shape descriptor of the medication, is extracted from the boundary of the medication. Once images are classified by the shape signature, Hue and Saturation(HS) color model is used to retrieve a most similarly matched medication image from the classified database images with the query image. The proposed system is designed and developed especially for specific population- seniors to browse medication images by using visual information of the medication in a feasible fashion. The experiment shows the proposed automatic image retrieval system is reliable and convenient to identify the medication images.