• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.155-162
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• 2022

Over the last 10 years, there has been rapid growth in the use of Machine Learning (ML) techniques to automate the process of intrusion threat detection at a scale never imagined before. This has prompted researchers, software engineers, and network specialists to rethink the applications of machine ML techniques particularly in the area of cybersecurity. As a result there exists numerous research documentations on the use ML techniques to detect and block cyber-attacks. This article is a systematic review involving the identification of published scholarly articles as found on IEEE Explore and Scopus databases. The articles exclusively related to the use of machine learning in Intrusion Detection Systems (IDS). Methods, concepts, results, and conclusions as found in the texts are analyzed. A description on the process taken in the identification of the research articles included: First, an introduction to the topic which is followed by a methodology section. A table is used to list identified research articles in the form of title, authors, methodology, and key findings.

• The Korean Journal of Applied Statistics
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• v.36 no.5
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• pp.399-413
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• 2023

Recently, monitoring and detecting anomalies in social networks have become an interesting research topic. In this study, we investigate the detection of abnormal changes in a network modeled by the DCSBM (degree corrected stochastic block model), which reflects the propensity of both individuals and communities. To this end, we propose three methods for anomaly detection in the DCSBM networks: One method for monitoring the entire network, and two methods for dividing and monitoring the network in consideration of communities. To compare these anomaly detection methods, we design and perform simulations. The simulation results show that the method for monitoring networks divided by communities has good performance.

• Communications for Statistical Applications and Methods
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• v.3 no.2
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• pp.21-28
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• 1996

Collinearities contained in MEM cause the same problems as they do in traditional regression model, so the detection of collinearities is a crucial topic in MEM. One diagnostic was introduced by Carrillo-Gamboa and Gunst, but their method did not work in some cases. Two alternative collinearity diagnostics that provide reasonable measure of collinearities are proposed. Simulation study is performed to compare the small-sample properties of the proposed collinearity diagnostics.

• The KIPS Transactions:PartB
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• v.12B no.1 s.97
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• pp.81-90
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• 2005

In this paper, we propose a novel approach which models multilingual story link detection by adapting the features such as timelines and multilingual spaces as weighting components to give distinctive weights to terms related to events. On timelines term significance is calculated by comparing term distribution of the documents on that day with that on the total document collection reported, and used to represent the document vectors on that day. Since two languages can provide more information than one language, term significance is measured on each language space and used to refer the other language space as a bridge on multilingual spaces. Evaluating the method on Korean and Japanese news articles, our method achieved $14.3{\%}\;and\;16.7{\%}$ improvement for mono- and multi-lingual story pairs, and for multilingual story pairs, respectively. By measuring the space density, the proposed weighting components are verified with a high density of the intra-event stories and a low density of the inter-events stories. This result indicates that the proposed method is helpful for multilingual story link detection.

• Journal of The Korean Association of Information Education
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• v.12 no.2
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• pp.183-194
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• 2008

This research was conducted from the perspective of student management focusing on such central topic as realization of research ethics on the basis of research ethics case study model. In this study, improvement method for research ethics education through means of application of DEVAC System, which is a paper plagiarism detection system, and survey on current status of research ethics in college education and degree of consciousness thereof were explored. Through these investigations, a topic relating to establishment of the foundation in order to foster consciousness of research ethics in the college education was established as the primary purpose of this study. To accomplish the purpose of this study, firstly, actual situation of paper plagiarism committed by the college students and their consciousness were surveyed. Secondly, the research ethics education was examined through means of applying DEVAC paper plagiarism detection system. The results from investigations revealed the followings: First, 424 students (65.43%) who participated in this research and survey on the fact of paper plagiarism had experience of report plagiarism, and the result of investigation showed that 49.3% of students among those who had experience of paper plagiarism committed report plagiarism more than three times in a semester. And, 34.1% of participants showed a positive response to the use of a paper plagiarism detection system in the college, and results from the investigation displayed that the creative education (39.0%) marked the highest scores as in the educational method to reinforce the research ethics. Second, the results from examination of paper plagiarism having applied DEVAC system indicated that use of this system can be an alternative to prevent paper plagiarism from students. It is realized through this study that there is a necessity in various respects to build up the foundation which will enable individual students to improve their consciousness to such a degree so as to make them clearly recognize the fact that plagiarism is criminal act.

• Journal of Digital Contents Society
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• v.19 no.2
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• pp.363-370
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• 2018

Ransomware detection has become a hot topic in computer security for protecting digital contents. Unfortunately, current signature-based and static detection models are often easily evadable by compress, and encryption. For overcoming the lack of these detection approach, we have proposed the dynamic ransomware detection system using data mining techniques such as RF, SVM, SL and NB algorithms. We monitor the actual behaviors of software to generate API calls flow graphs. Thereafter, data normalization and feature selection were applied to select informative features. We improved this analysis process. Finally, the data mining algorithms were used for building the detection model for judging whether the software is benign software or ransomware. We conduct our experiment using more suitable real ransomware samples. and it's results show that our proposed system can be more effective to improve the performance for ransomware detection.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.1989-2011
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• 2019

Vehicle detection based on aerial images is an interesting and challenging research topic. Most of the traditional vehicle detection methods are based on the sliding window search algorithm, but these methods are not sufficient for the extraction of object features, and accompanied with heavy computational costs. Recent studies have shown that convolutional neural network algorithm has made a significant progress in computer vision, especially Faster R-CNN. However, this algorithm mainly detects objects in natural scenes, it is not suitable for detecting small object in aerial view. In this paper, an accurate and effective vehicle detection algorithm based on Faster R-CNN is proposed. Our method fuse a hyperactive feature map network with Eltwise model and Concat model, which is more conducive to the extraction of small object features. Moreover, setting suitable anchor boxes based on the size of the object is used in our model, which also effectively improves the performance of the detection. We evaluate the detection performance of our method on the Munich dataset and our collected dataset, with improvements in accuracy and effectivity compared with other methods. Our model achieves 82.2% in recall rate and 90.2% accuracy rate on Munich dataset, which has increased by 2.5 and 1.3 percentage points respectively over the state-of-the-art methods.

• Journal of IKEEE
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• v.22 no.4
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• pp.1079-1087
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• 2018

Recently, computer vision field based deep learning artificial intelligence has become a hot topic among various image analysis boundaries. In this study, flames are detected in fire images using the Faster R-CNN algorithm, which is used to detect objects within the image, among various image recognition algorithms based on deep learning. In order to improve fire detection accuracy through a small amount of data sets in the learning process, we use image augmentation techniques, and learn image augmentation by dividing into 6 types and compare accuracy, precision and detection rate. As a result, the detection rate increases as the type of image augmentation increases. However, as with the general accuracy and detection rate of other object detection models, the false detection rate is also increased from 10% to 30%.

• Smart Media Journal
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• v.2 no.2
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• pp.8-13
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• 2013

Nowadays, the saliency region detection has become a popular research topic because of its uses for many applications like object recognition and object segmentation. Some of recent methods apply color distinctiveness based on an analysis of statistics of color image derivatives in order to boosting color saliency can produce the good saliency maps. However, if the salient regions comprise more than half the pixels of the image or the background is complex, it may cause bad results. In this paper, we introduce the method to handle these problems by using maximum symmetric surround. The results show that our method outperforms the previous algorithms. We also show the segmentation results by using Otsu's method.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.333-334
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• 2008

A real-time face tracking is a broad topic, covering a large spectrum of technologies and applications. Briefly face tracking is a kind of tracing technique which follows human face in any directions. It needs some algorithms such as human face detection and motion controller to track face. Moreover, both processing time and calculation time are the most important factors that influence to drive tracking system. In this paper, two algorithms are used to find human face: earn-shift algorithm and face detection algorithm using OpenCV. Fuzzy controller is utilized to move pan-tilt camera system which can move four directions along to x-y axis.