• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.5132-5148
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• 2017

Cyber attacks are evolving commensurate with recent developments in information security technology. Intrusion detection systems collect various types of data from computers and networks to detect security threats and analyze the attack information. The large amount of data examined make the large number of computations and low detection rates problematic. Feature selection is expected to improve the classification performance and provide faster and more cost-effective results. Despite the various feature selection studies conducted for intrusion detection systems, it is difficult to automate feature selection because it is based on the knowledge of security experts. This paper proposes a feature selection technique to overcome the performance problems of intrusion detection systems. Focusing on feature selection, the first phase of the proposed system aims at constructing a feature subset using a sequential forward floating search (SFFS) to downsize the dimension of the variables. The second phase constructs a classification model with the selected feature subset using a random forest classifier (RFC) and evaluates the classification accuracy. Experiments were conducted with the NSL-KDD dataset using SFFS-RF, and the results indicated that feature selection techniques are a necessary preprocessing step to improve the overall system performance in systems that handle large datasets. They also verified that SFFS-RF could be used for data classification. In conclusion, SFFS-RF could be the key to improving the classification model performance in machine learning.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.6
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• pp.2806-2825
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• 2018

Unsupervised learning has shown good performance on image, video and audio classification tasks, and much progress has been made so far. It studies how systems can learn to represent particular input patterns in a way that reflects the statistical structure of the overall collection of input patterns. Many promising deep learning systems are commonly trained by the greedy layerwise unsupervised learning manner. The performance of these deep learning architectures benefits from the unsupervised learning ability to disentangling the abstractions and picking out the useful features. However, the existing unsupervised learning algorithms are often difficult to train partly because of the requirement of extensive hyperparameters. The tuning of these hyperparameters is a laborious task that requires expert knowledge, rules of thumb or extensive search. In this paper, we propose a simple and effective unsupervised feature learning algorithm for image classification, which exploits an explicit optimizing way for population and lifetime sparsity. Firstly, a sparse target matrix is built by the competitive rules. Then, the sparse features are optimized by means of minimizing the Euclidean norm ($L_2$) error between the sparse target and the competitive layer outputs. Finally, a classifier is trained using the obtained sparse features. Experimental results show that the proposed method achieves good performance for image classification, and provides discriminative features that generalize well.

• Journal of Internet Computing and Services
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• v.11 no.3
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• pp.15-24
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• 2010

In this paper, we propose an image classification which combines semantic relations of tags with contents of images to improve the satisfaction of image retrieval on application domains as huge image sharing sites. To make good use of image retrieval or classification algorithms on huge image sharing sites as Flickr, they are applicable to real tagged Web images. To classify the Web images by 'bag of visual word' based image content, our algorithm includes training the category model by utilizing the preliminary retrieved images with semantically related tags as training data and classifying the test images based on PLSA. In the experimental results on the Flickr Web images, the proposed method produced the better precision and recall rates than those from the existing method using tag information.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.1
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• pp.308-320
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• 2016

Although the face almost always has an axisymmetric structure, it is generally not symmetrical image for the face image. However, the mirror image of the face image can reflect possible variation of the poses and illumination opposite to that of the original face image. A robust minimum squared error classification (RMSEC) algorithm is proposed in this paper. Concretely speaking, the original training samples and the mirror images of the original samples are taken to form a new training set, and the generated training set is used to perform the modified minimum sqreared error classification(MMSEC) algorithm. The extensive experiments show that the accuracy rate of the proposed RMSEC is greatly increased, and the the proposed RMSEC is not sensitive to the variations of the parameters.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.5
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• pp.133-138
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• 2010

In this paper, we make up a feature image including spatial properties and statistical properties on image, and format covariance matrices using region variance magnitudes. By using it to texture classification, this paper puts a proposal for tough texture classification way to illumination, noise and rotation. Also we offer a way to minimalize performance time of texture classification using integral image expressing middle image for fast calculation of region sum. To estimate performance evaluation of proposed way, this paper use a Brodatz texture image, and so conduct a noise addition and histogram specification and create rotation image. And then we conduct an experiment and get better performance over 96%.

• International Journal of Internet, Broadcasting and Communication
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• v.9 no.4
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• pp.1-7
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• 2017

Clipart is artificial visual contents that are created using various tools such as Illustrator to highlight some information. Here, the style of the clipart plays a critical role in determining how it looks. However, previous studies on clipart are focused only on the object recognition [16], segmentation, and retrieval of clipart images using hand-craft image features. Recently, some clipart classification researches based on the style similarity using CNN have been proposed, however, they have used different CNN-models and experimented with different benchmark dataset so that it is very hard to compare their performances. This paper presents an experimental analysis of the clipart classification based on the style similarity with two well-known CNN-models (Inception Resnet V2 [13] and VGG-16 [14] and transfers learning with the same benchmark dataset (Microsoft Style Dataset 3.6K). From this experiment, we find out that the accuracy of Inception Resnet V2 is better than VGG for clipart style classification because of its deep nature and convolution map with various sizes in parallel. We also find out that the end-to-end training can improve the accuracy more than 20% in both CNN models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.12
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• pp.5116-5134
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• 2015

In this paper, due to the network security defense is mainly static defense, a dynamic classification network security defense strategy model is proposed by analyzing the security situation of complex computer network. According to the network security impact parameters, eight security elements and classification standard are obtained. At the same time, the dynamic classification algorithm based on fuzzy theory is also presented. The experimental analysis results show that the proposed model and algorithm are feasible and effective. The model is a good way to solve a safety problem that the static defense cannot cope with tactics and lack of dynamic change.

• Journal of Internet Computing and Services
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• v.1 no.1
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• pp.63-72
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• 2000

Data mining means a process of nontrivial extraction of hidden knowledge or potentially useful information from data in large databases. Data mining algorithm is a multi-disciplinary field of research; machine learning, statistics, and computer science all make a contribution. Different classification schemes can be used to categorize data mining methods based on the kinds of tasks to be implemented and the kinds of application classes to be utilized, and classification has been identified as an important task in the emerging field of data mining. Since classification is the basic element of human's way of thinking, it is a well-studied problem in a wide varietyof application. In this paper, we propose a classifier system based on genetic algorithm with robust property, and the proposed system is evaluated by applying it to nDmC problem related to classification task in data mining.

• Journal of the Korea Society of Computer and Information
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• v.11 no.2 s.40
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• pp.15-23
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• 2006

Millions of documents are already on the Internet, and new documents are being formed all the time. This poses a very important problem in the management and querying of documents to classify them on the Internet by the most suitable means. However, most users have been using the document classification method based on a keyword. This method does not classify documents efficiently, and there is a weakness in the category of document that includes meaning. Document classification by a person can be very correct sometimes and often times is required. Therefore, in this paper, We wish to classify documents by using a neural network algorithm and C4.5 algorithms. We used resume data forming by XML for a document classification experiment. The result showed excellent possibilities in the document category. Therefore, We expect an applicable solution for various document classification problems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2162-2179
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• 2019

The performance of text classification is highly related to the feature selection methods. Usually, two tasks are performed when a feature selection method is applied to construct a feature set; 1) assign score to each feature and 2) select the top-N features. The selection of top-N features in the existing filter-based feature selection methods is biased by their discriminative power and the empirical process which is followed to determine the value of N. In order to improve the text classification performance by presenting a more illustrative feature set, we present an approach via a potent representation learning technique, namely DBN (Deep Belief Network). This algorithm learns via the semantic illustration of documents and uses feature vectors for their formulation. The nodes, iteration, and a number of hidden layers are the main parameters of DBN, which can tune to improve the classifier's performance. The results of experiments indicate the effectiveness of the proposed method to increase the classification performance and aid developers to make effective decisions in certain domains.