• Journal of Internet Computing and Services
• /
• v.23 no.5
• /
• pp.79-85
• /
• 2022

Anomaly detection is a method to detect and block abnormal data flows in general users' data sets. The previously known method is a method of detecting and defending an attack based on a signature using the signature of an already known attack. This has the advantage of a low false positive rate, but the problem is that it is very vulnerable to a zero-day vulnerability attack or a modified attack. However, in the case of anomaly detection, there is a disadvantage that the false positive rate is high, but it has the advantage of being able to identify, detect, and block zero-day vulnerability attacks or modified attacks, so related studies are being actively conducted. In this study, we want to deal with these anomaly detection mechanisms, and we propose a new mechanism that performs both anomaly detection and classification while supplementing the high false positive rate mentioned above. In this study, the experiment was conducted with five configurations considering the characteristics of various algorithms. As a result, the model showing the best accuracy was proposed as the result of this study. After detecting an attack by applying the Extra Tree and Three-layer ANN at the same time, the attack type is classified using the Extra Tree for the classified attack data. In this study, verification was performed on the NSL-KDD data set, and the accuracy was 99.8%, 99.1%, 98.9%, 98.7%, and 97.9% for Normal, Dos, Probe, U2R, and R2L, respectively. This configuration showed superior performance compared to other models.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.35 no.2
• /
• pp.71-88
• /
• 2010

A density-based outlier detection such as an LOF (Local Outlier Factor) tries to find an outlying observation by using density of its surrounding space. In spite of several advantages of a density-based outlier detection method, the computational complexity of outlier detection has been one of major barriers in its application. In this paper, we present an LOF algorithm that can reduce computation time of a density based outlier detection algorithm. A kd-tree indexing and approximated k-nearest neighbor search algorithm (ANN) are adopted in the proposed method. A set of experiments was conducted to examine performance of the proposed algorithm. The results show that the proposed method can effectively detect local outliers in reduced computation time.

• Journal of Communications and Networks
• /
• v.11 no.1
• /
• pp.20-25
• /
• 2009

The maximum likelihood detection with QR decomposition and M-algorithm (QRM-MLD) has been presented as a suboptimum multiple-input multiple-output (MIMO) detection scheme which can provide almost the same performance as the optimum maximum likelihood (ML) MIMO detection scheme but with the reduced complexity. However, due to the lack of parallelism and the regularity in the decoding structure, the conventional QRM-MLD which uses the tree-structure still has very high complexity for the very large scale integration (VLSI) implementation. In this paper, we modify the tree-structure of conventional QRM-MLD into trellis-structure in order to obtain high operational parallelism and regularity and then apply the Viterbi algorithm to the QRM-MLD to ease the burden of the VLSI implementation.We show from our selected numerical examples that, by using the QRM-MLD with our proposed trellis-structure, we can reduce the complexity significantly compared to the tree-structure based QRM-MLD while the performance degradation of our proposed scheme is negligible.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.41 no.6
• /
• pp.99-108
• /
• 2004

In this paper, a new fast detection and clutter rejection method is proposed for CCD-image-based Automatic Target Detection System. For defence application, fast computation is a critical point, thus we concentrated on the ability to detect various targets with simple computation. In training stage, 1D template set is generated by regional vertical projection and K-means clustering, and binary tree structure is adopted to reduce the number of template matching in test stage. We also use adaptive skip-width by Correlation-based Adaptive Predictive Search(CAPS) to further improve the detecting speed. In clutter rejection stage, we obtain Fourier Descriptor coefficients from boundary information, which are useful to rejected clutters.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.16 no.6
• /
• pp.1199-1203
• /
• 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 KSRS Conference
• /
• 2003.11a
• /
• pp.1042-1044
• /
• 2003

A methodology of the integrating the similar color circle search of the spectral data and segmentation of the height data is developed. The method is then applied to study areas, and the results by IKONOS, LIDAR and data fusion are verified with the ground truth, and examined in terms of the accuracy. Results show that with the data fusion the accuracy are improved by about 15% in most of the study areas. The methodology for the detection of individual tree stands by data fusion is explored, and the utility of combinatorial use of the spectral and the height information is demonstrated.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.25 no.1
• /
• pp.69-74
• /
• 2021

Internet access is also increasing as online activities increase due to the influence of Corona 19. However, network attacks are also diversifying by malicious users, and DDoS among the attacks are increasing year by year. These attacks are detected by intrusion detection systems and can be prevented at an early stage. Various data sets are used to verify intrusion detection algorithms, but in this paper, CICIDS2017, the latest traffic, is used. DDoS attack traffic was analyzed using the decision tree. In this paper, we analyzed the traffic by using the decision tree. Through the analysis, a decisive feature was found, and the accuracy of the decisive feature was confirmed by proceeding the decision tree to prove the accuracy of detection. And the contents of false positive and false negative traffic were analyzed. As a result, learning the feature and the two features showed that the accuracy was 98% and 99.8% respectively.

• Journal of the Korea Society of Computer and Information
• /
• v.13 no.1
• /
• pp.75-80
• /
• 2008

This paper proposes a method to detect scene change using global decision tree that extract boundary cut that have width of big change that happen by camera brake from difference value of frames. First, calculate frame difference value through regional X2-histogram and normalization, next, calculate distance between difference value using normalization. Shot boundary detection is performed by compare global threshold distance with distance value for two adjacent frames that calculating global threshold distance based on distance between calculated difference value. Global decision tree proposed this paper can detect easily sudden scene change such as motion from object or camera and flashlight.

• IE interfaces
• /
• v.22 no.2
• /
• pp.126-134
• /
• 2009

As product quality and yield are essential factors in semiconductor manufacturing, monitoring the main manufacturing steps is a critical task. For the purpose, FDC(Fault detection and classification) is used for diagnosing fault states in the processes by monitoring data stream collected by equipment sensors. This paper proposes an FDC model based on decision tree which provides if-then classification rules for causal analysis of the processing results. Unlike previous decision tree approaches, we reflect the structural aspect of the data stream to FDC. For this, we segment the data stream into multiple subregions, define structural features for each subregion, and select the features which have high relevance to results of the process and low redundancy to other features. As the result, we can construct simple, but highly accurate FDC model. Experiments using the data stream collected from etching process show that the proposed method is able to classify normal/abnormal states with high accuracy.

• Journal of Korea Multimedia Society
• /
• v.7 no.12
• /
• pp.1729-1736
• /
• 2004

This paper proposes an efficient collision detection method in the dynamic spatial subdivisions for the MMORPG engine which requires realtime interactions. An octree is a suitable structure for static scenes or terrain processing. An octree spatial subdivision enhances rendering speed of scenes. Current spatial subdivisions tend to be highly optimized for efficient traversal, but are difficult to update quickly for a changing geometry. When an object moves to the outside extent for the spatial subdivisions, the acceleration structure would normally have to be rebuilt. The OSP based on a tree is used to divide dynamically wide outside which is the subject of 3D MMORPG. TBV does not reconstruct all tree nodes of OSP and has reduced rebuilding times by TBV information of a target node. A collision detection is restricted to those objects contained in the visibility range of sight by using the information established in TBV. We applied the HBV and ray tracing for an efficient collision detection.