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12th IEEE/IPSJ International Symposium on Applications and the Internet (SAINT) / IEEE Annual Signature Conference on Computer Software and Applications (COMPSAC)

作者： Satoh, Akihiro Nakamura, Yutaka Ikenaga, Takeshi Kyushu Inst Technol Kitakyushu Fukuoka 804 Japan

ISBN: (纸本)9780769547374

SSH services are run on many hosts with various scopes other than just operation, so dictionary attack against the service is a common security threat. SANS has reported the emergence of distributed SSH dictionary attacks, which are very stealthy in comparison with a simple one. Since even one success of such an attack causes serious problems, administrators should implement countermeasures. SSH dictionary attacks have been detected in two basic ways that rely on either log files or network traffic. Both approaches, however, have limitations. The first approach imposes upon administrators heavy maintenance costs, which grow linearly with the number of hosts in networks. The second approach cannot distinguish between successful and unsuccessful attacks. Of more immediate concern, neither approach is effective against stealthy attacks because the login attempts of these attacks have little impact on log files or network traffic. An ideal method would be able to detect individual attacks and distinguish between an attack's success or failure, using information derived from only network traffic. In this paper, we describe such a method, which was developed by combining two novel elements. First, on the basis of our assumptions, we use two criteria: "existence of a connection protocol" and "difference in the inter-arrival time of an auth-packet". These criteria are not available, though, owing to the confidentiality and flexibility of the SSH protocol. Second, we resolve this problem by identifying transition points of a sub-protocol through flow features and machine learning algorithms. We evaluate the effectiveness of the proposed method through experiments on real traffic traces collected at the edge in our campus networks. The experimental results are encouraging for this research direction, though they are derived from reduced datasets of SSH dictionary attacks and under simplifying assumptions. The significant contribution is the demonstration that an ideal method

关键词： ssh dictionary attack flow analysis network operation machine learning algorithm

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AZOrange - High performance open source machine learning for QSAR modeling in a graphical programming environment

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JOURNAL OF CHEMINFORMATICS 2011年第1期3卷 1-10页

作者： Stalring, Jonna C. Carlsson, Lars A. Almeida, Pedro Boyer, Scott AstraZeneca R&D Computat Toxicol Global Safety Assessment S-43153 Molndal Sweden EngInMotion Ltd P-3510070 Viseu Portugal

Background: machine learning has a vast range of applications. In particular, advanced machine learning methods are routinely and increasingly used in quantitative structure activity relationship (QSAR) modeling. QSAR data sets often encompass tens of thousands of compounds and the size of proprietary, as well as public data sets, is rapidly growing. Hence, there is a demand for computationally efficient machine learning algorithms, easily available to researchers without extensive machine learning knowledge. In granting the scientific principles of transparency and reproducibility, Open Source solutions are increasingly acknowledged by regulatory authorities. Thus, an Open Source state-of-the-art high performance machine learning platform, interfacing multiple, customized machine learning algorithms for both graphical programming and scripting, to be used for large scale development of QSAR models of regulatory quality, is of great value to the QSAR community. Results: This paper describes the implementation of the Open Source machine learning package AZOrange. AZOrange is specially developed to support batch generation of QSAR models in providing the full work flow of QSAR modeling, from descriptor calculation to automated model building, validation and selection. The automated work flow relies upon the customization of the machine learning algorithms and a generalized, automated model hyper-parameter selection process. Several high performance machine learning algorithms are interfaced for efficient data set specific selection of the statistical method, promoting model accuracy. Using the high performance machine learning algorithms of AZOrange does not require programming knowledge as flexible applications can be created, not only at a scripting level, but also in a graphical programming environment. Conclusions: AZOrange is a step towards meeting the needs for an Open Source high performance machine learning platform, supporting the efficient development of hig

关键词： Quantitative Structure Activity Relationship machine learning algorithm Quantitative Structure Activity Relationship Model Generalization Accuracy Artificial Neural Network algorithm

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Adaptive machine learning Approach for Emotional Email Classification

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International Conference on Ergonomics and Health Aspects of Work with Computers (EHAWC)/14th International Conference on Human-Computer Interaction (HCI)

作者： Karthik, K. Ponnusamy, R. Vinayaka Missions Univ Aarupadai Veedu Inst Technol Dept Comp Sci & Engn Madras 603104 Tamil Nadu India

ISBN: (纸本)9783642216169

Emotional e-mail classification is one of the important issues in the service oriented organizations. E-mails are served in a first come first serve basis. Few e-mails express the unfair treatment or dissatisfaction of service. It is essential to serve such e-mails with a high priority. In this paper an attempt is made to identify such mails which express the strong emotions of the customers/stakeholders. This system classifies the e-mails in to three categories via positive, negative and other mails. An adaptive machine learning algorithm that uses combined SVD and KNN methods is developed to solve the problem of emotional e-mail classification. Also an emotional dictionary is used as a central component of this system that serves various emotional words and phrases for classification. The system also adaptive in nature and adapts various new words and phrases that explicates the emotion.

关键词： E-mail Classification Emotion machine learning algorithm Emotional Dictionary

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A new dataset evaluation method based on category overlap

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COMPUTERS IN BIOLOGY AND MEDICINE 2011年第2期41卷 115-122页

作者： Oh, Sejong Dankook Univ Dept Nanobiomed Sci Cheonan 330714 South Korea

The quality of dataset has a profound effect on classification accuracy, and there is a clear need for some method to evaluate this quality. In this paper, we propose a new dataset evaluation method using the R-value measure. This proposed method is based on the ratio of overlapping areas among categories in a dataset. A high R-value for a dataset indicates that the dataset contains wide overlapping areas among its categories, and classification accuracy on the dataset may become low. We can use the R-value measure to understand the characteristics of a dataset, the feature selection process, and the proper design of new classifiers. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Feature Feature selection R-value Dataset Classification machine learning algorithm

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Recognizing Hand Movements from a Single sEMG Sensor using Guided Under-determined Source Signal Separation

Recognizing Hand Movements from a Single sEMG Sensor using G...

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IEEE International Conference on Rehabilitation Robotics (ICORR)/International Neurorehabilitation Symposium (INRS)/International Conference on Virtual Rehabilitation (ICVR)

作者： Rivera, L. A. DeSouza, G. N.

ISBN: (纸本)9781424498628

Rehabilitation devices, prosthesis and human machine interfaces are among many applications for which surface electromyographic signals (sEMG) can be employed. Systems reliant on these muscle-generated electrical signals require various forms of machine learning algorithms for specific signature recognition. Those systems vary in terms of the signal detection methods, the feature selection and the classification algorithm used. However, in all those cases, the use of multiple sensors is a constant. In this paper, we present a new technique for source signal separation that relies on a single sEMG sensor. This proposed technique was employed in a classification framework for hand movements that achieved comparable results to other approaches in the literature, but yet, it relied on a much simpler classifier and used a very small number of features.

关键词： Accuracy Artificial neural networks Feature extraction Muscles Prosthetics SEMG sensor Source separation Training biomechanics electromyography guided under-determined source signal separation hand movement recognition human machine interfaces learning (artificial intelligence) machine learning algorithm man-machine systems medical signal detection medical signal processing muscle-generated electrical signals patient rehabilitation prosthesis prosthetics rehabilitation device source separation specific signature recognition surface electromyographic signal

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The German Traffic Sign Recognition Benchmark: A multi-class classification competition

The German Traffic Sign Recognition Benchmark: A multi-class...

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International Joint Conference on Neural Networks (IJCNN)

作者： Stallkamp, Johannes Schlipsing, Marc Salmen, Jan Igel, Christian Ruhr Univ Bochum Inst Neuroinformat D-44780 Bochum Germany Univ Copenhagen Dept Comp Sci DK-2100 Copenhagen Denmark

ISBN: (纸本)9781424496365

The "German Traffic Sign Recognition Benchmark" is a multi-category classification competition held at IJCNN 2011. Automatic recognition of traffic signs is required in advanced driver assistance systems and constitutes a challenging real-world computer vision and pattern recognition problem. A comprehensive, lifelike dataset of more than 50,000 traffic sign images has been collected. It reflects the strong variations in visual appearance of signs due to distance, illumination, weather conditions, partial occlusions, and rotations. The images are complemented by several precomputed feature sets to allow for applying machine learning algorithms without background knowledge in image processing. The dataset comprises 43 classes with unbalanced class frequencies. Participants have to classify two test sets of more than 12,500 images each. Here, the results on the first of these sets, which was used in the first evaluation stage of the two-fold challenge, are reported. The methods employed by the participants who achieved the best results are briefly described and compared to human traffic sign recognition performance and baseline results.

关键词： Benchmark testing German Traffic Sign Recognition Benchmark Histograms Humans Image color analysis Image resolution Lead Training computer vision computer vision driver assistance system driver information systems image classification image processing learning (artificial intelligence) machine learning algorithm multiclass classification competition pattern recognition problem traffic engineering computing

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Improving Question Retrieval in Community Question Answering with Label Ranking

Improving Question Retrieval in Community Question Answering...

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International Joint Conference on Neural Networks (IJCNN)

作者： Wang, Wei Li, Baichuan King, Irwin Chinese Univ Hong Kong Dept Comp Sci & Engn Shatin Hong Kong Peoples R China

ISBN: (纸本)9781424496365

Community question answering services (CQA), which provides a platform for people with diverse backgrounds to share information and knowledge, has become an increasingly popular research topic recently as made popular by sites such as Yahoo! Answers(1), answerbag(2), zhidao(3), etc. Question retrieval (QR) in CQA can automatically find the most relevant and recent questions that have been solved by other users. Current QR approaches typically consider using diverse retrieval models, but they fail to analyze users' intention. User intentions such as finding facts, interacting with others, seeking reasons, etc. reflect what the users really want to know. Hence, we propose to integrate user intention analysis into QR. Firstly, we classify questions into different and multiple types of users' intentions. Another practical problem is that there naturally exist some preferences among the possible questions types. The more relevant type should be ranked higher than types which are not so relevant. Therefore, we propose to utilize a novel label ranking method, which is a machine learning algorithm that aims to predict a ranking among all the possible labels, to perform question classification. Secondly, based on the result of question classification, we integrate user intentions with translation-based language models to explore whether a user's intention does help to improve the performance. We conduct a series of experiments with Yahoo data, and the experimental results demonstrate that our proposed improved question retrieval can indeed enhance the performance of traditional question retrieval model.

关键词： CQA Data models History Internet machine learning algorithms Prediction algorithms QR Support vector machines Training Training data Yahoo data community question answering services information retrieval label ranking method learning (artificial intelligence) machine learning algorithm question classification question retrieval improvement user intention analysis

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Predicting the fidelity of JPEG2000 compressed CT images using DICOM header information

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MEDICAL PHYSICS 2011年第12期38卷 6449-6457页

作者： Kim, Kil Joong Kim, Bohyoung Lee, Hyunna Choi, Hosik Seoul Natl Univ Coll Med Dept Radiat Appl Life Sci Seoul 110744 South Korea Seoul Natl Univ Sch Comp Sci & Engn Seoul 151742 South Korea Hoseo Univ Dept Informat Stat Asan 336795 Chungcheongnam South Korea

Purpose: To propose multiple logistic regression (MLR) and artificial neural network (ANN) models constructed using digital imaging and communications in medicine (DICOM) header information in predicting the fidelity of Joint Photographic Experts Group (JPEG) 2000 compressed abdomen computed tomography (CT) images. Methods: Our institutional review board approved this study and waived informed patient consent. Using a JPEG2000 algorithm, 360 abdomen CT images were compressed reversibly (n - 48, as negative control) or irreversibly (n - 312) to one of different compression ratios (CRs) ranging from 4:1 to 10:1. Five radiologists independently determined whether the original and compressed images were distinguishable or indistinguishable. The 312 irreversibly compressed images were divided randomly into training (n - 156) and testing (n - 156) sets. The MLR and ANN models were constructed regarding the DICOM header information as independent variables and the pooled radiologists' responses as dependent variable. As independent variables, we selected the CR (DICOM tag number: 0028, 2112), effective tube current-time product (0018, 9332), section thickness (0018, 0050), and field of view (0018, 0090) among the DICOM tags. Using the training set, an optimal subset of independent variables was determined by backward stepwise selection in a four-fold cross-validation scheme. The MLR and ANN models were constructed with the determined independent variables using the training set. The models were then evaluated on the testing set by using receiver-operating-characteristic (ROC) analysis regarding the radiologists' pooled responses as the reference standard and by measuring Spearman rank correlation between the model prediction and the number of radiologists who rated the two images as distinguishable. Results: The CR and section thickness were determined as the optimal independent variables. The areas under the ROC curve for the MLR and ANN predictions were 0.91 (95% CI;0.86

关键词： computed tomography image compression image fidelity DICOM machine learning algorithm

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Dimension reduction with gene expression data using targeted variable importance measurement

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BMC BIOINFORMATICS 2011年第1期12卷 1-12页

作者： Wang, Hui van der Laan, Mark J. Stanford Univ Dept Pediat Stanford CA 94305 USA Univ Calif Berkeley Div Biostat Berkeley CA 94720 USA

Background: When a large number of candidate variables are present, a dimension reduction procedure is usually conducted to reduce the variable space before the subsequent analysis is carried out. The goal of dimension reduction is to find a list of candidate genes with a more operable length ideally including all the relevant genes. Leaving many uninformative genes in the analysis can lead to biased estimates and reduced power. Therefore, dimension reduction is often considered a necessary predecessor of the analysis because it can not only reduce the cost of handling numerous variables, but also has the potential to improve the performance of the downstream analysis algorithms. Results: We propose a TMLE-VIM dimension reduction procedure based on the variable importance measurement (VIM) in the frame work of targeted maximum likelihood estimation (TMLE). TMLE is an extension of maximum likelihood estimation targeting the parameter of interest. TMLE-VIM is a two-stage procedure. The first stage resorts to a machine learning algorithm, and the second step improves the first stage estimation with respect to the parameter of interest. Conclusions: We demonstrate with simulations and data analyses that our approach not only enjoys the prediction power of machine learning algorithms, but also accounts for the correlation structures among variables and therefore produces better variable rankings. When utilized in dimension reduction, TMLE-VIM can help to obtain the shortest possible list with the most truly associated variables.

关键词： Dimension Reduction Lasso Principle Component Analysis machine learning algorithm Candidate List

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Evaluation of different machine learning methods for ligand-based virtual screening

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Journal of Cheminformatics 2011年第1期3卷 1-1页

作者： R Kurczab S Smusz AJ Bojarski Department of Medicinal Chemistry Institute of Pharmacology Polish Academy of Sciences Krakow 31-343 Poland

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