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3rd International Conference on Advanced Cloud and Big Data (CBD)

作者： Xiao, Xi Yan, Ruibo Ye, Runguo Li, Qing Peng, Sancheng Jiang, Yong Tsinghua Univ Grad Sch Shenzhen Shenzhen Peoples R China China Elect Standardizat Inst Beijing Peoples R China Zhaoqing Univ Sch Comp Sci Zhaoqing Peoples R China

ISBN: (纸本)9781467385374

Security on mobile devices is becoming increasingly important. HTML5 are widely used to develop mobile applications due to its portability on multi platforms. However it is allowed to mix data and code together in web technology. HTML5-based applications are prone to suffer from code injection attacks that are similar to XSS. In this paper, at first, we introduce a more hidden type of code injection attacks, coding-based attacks. In the new type of code injection attacks, JavaScript code is encoded in a human-unreadable form. Then we use classification algorithms of machine learning to determine whether an app suffers from the code injection attack or not. The experimental result shows that the Precision of our detection method reaches 95.3%. Compare with the other method, our approach improves a lot in detection speed with the precision nearly unchanged. Furthermore, an improved access control model is proposed to mitigate the attack damage. In addition, filters are adopted to remove JavaScript code from data to prevent the attacks. The effectiveness and rationality have been validated through extensive simulations.

关键词： code injection classification algorithm machine learning access control model filter

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ARSkNN-A k-NN Classifier Using Mass Based Similarity Measure

ARS<i>k</i>NN-A <i>k</i>-NN Classifier Using Mass Based Simi...

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International Conference on Information and Communication Technologies (ICICT)

作者： Kumar, Ashish Bhatnagar, Roheet Srivastava, Sumit Manipal Univ Jaipur Jaipur 303007 Rajasthan India

k-Nearest Neighbor (k-NN) classification technique is one of the most elementary and straightforward classification methods. Although distance learning is in the core of k-NN classification, similarity can be preferred upon distance in several practical applications. This paper proposes a novel algorithm for learning a class of an instance based on a similarity measure which does not calculate distance, for k-Nearest Neighbor (k-NN) classification. (C) 2015 The Authors. Published by Elsevier B.V.

关键词： classification algorithm Data Mining Nearest Neighbor Similarity Measures

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Improving Spam Detection in Online Social Networks 1

Improving Spam Detection in Online Social Networks

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2015 International Conference on Cognitive Computing and Information Processing (CCIP)

作者： Gupta, Arushi Kaushal, Rishabh Indira Gandhi Delhi Tech Univ Women Dept Informat Technol Delhi India

ISBN: (纸本)9781479971718

Online Social Networks (OSNs) are deemed to be the most sought-after societal tool used by the masses world over to communicate and transmit information. Our dependence on these platforms for seeking opinions, news, updates, etc. is increasing. While it is true that OSNs have become a new medium for dissemination of information, at the same time, they are also fast becoming a playground for the spread of misinformation, propaganda, fake news, rumors, unsolicited messages, etc. Consequently, we can say that an OSN platform comprises of two kinds of users namely, Spammers and Non-Spammers. Spammers, out of malicious intent, post either unwanted (or irrelevant) information or spread misinformation on OSN platforms. As part of our work, we propose mechanisms to detect such users (Spammers) in Twitter social network (a popular OSN). Our work is based on a number of features at tweet-level and user-level like Followers/Followees, URLs, Spam Words, Replies and HashTags. In our work, we have applied three learning algorithms namely Naive Bayes, Clustering and Decision trees. Furthermore, to improve detection of Spammers, a novel integrated approach is proposed which "combines" the advantages of the three learning algorithms mentioned above. Improvement of spam detection is measured on the basis of Total Accuracy, Spammers Detection Accuracy and Non-Spammers Detection Accuracy. Results, thus obtained, show that our novel integrated approach that combines all algorithms outperforms other classical approaches in terms of overall accuracy and detect Non-Spammers with 99% accuracy with an overall accuracy of 87.9%.

关键词： Spam detection classification algorithm twitter online social networking

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Weighing algorithms of classification and identification of situations

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DISCRETE MATHEMATICS AND APPLICATIONS 2015年第2期25卷 69-81页

作者： Chudnov, Alexander M. St Petersburg State Univ Telecommun St Petersburg Russia

The paper gives lower bounds for the minimum number m of weighings that are necessary for identification of up to t non-standard objects out of the total number of n objects being tested. For the problem with fixed deviation of weights of non-standard objects we construct a perfect algorithms with parameters n = 11, m = 5, t = 2 corresponding to the parameters of the ternary Virtakallio-Golay code. The nonexistence of a perfect weighing code with such parameters is proved.

关键词： weighing detection of false coins classification algorithm

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A novel gender classification method based on MapReduce

A novel gender classification method based on MapReduce

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IEEE International Conference on Software Engineering and Service Sciences (ICSESS)

作者： Tong Cui Haifeng Zhao Science and Technology on Information Systems Engineering Laboratory Nanjing China

ISBN: (纸本)9781479983544

A novel parallelize gender recognition method with MapReduce is presented, which successfully comprise several machine leaning algorithms which are employed for gender recognition. The mass of face sample images are gathered and separated as train dataset and test dataset, and Local Binary Pattern (LBP) features are extracted when those sample sets are pre-processed and made ready for following operations. And Principle Component Analysis (PCA) is applied to train dataset to extract the most distinguishing features. Three classification algorithms: Support Vector Machine(SVM), k-Nearest Neighborhood (k-NN) and Adaboost are implemented and compared to determine the most suitable and successful algorithm for gender parallelize machine learning (GPML). To achieve the shortest execution time, we propose to apply GPML with MapReduce to avoid parallelizing above three algorithms while also improving their scalability to big datasets. The results show that this method reduces the training computational complexity significantly when the number of computing nodes increases while gaining better speedup rates and extending performance than those on parallelize Adaboost.

关键词： face recognition feature extraction image classification learning (artificial intelligence) parallel processing principal component analysis support vector machines Adaboost GPML LBP features MapReduce PCA SVM classification algorithm face sample images gender classification method gender parallelize machine learning k-nearest neighborhood local binary pattern feature extraction machine leaning algorithms parallelized gender recognition method principle component analysis support vector machine test dataset train dataset training computational complexity gender recognition parallel meta-learning Support Vector Network principal components analysis Sorting algorithm Image classification PARALLEL PROCESSING (COMPUTERS) Facial Recognition Passive Cutaneous Anaphylaxis Feature extraction Dataset Gender ADABOOST execution time classification Methods

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Research on Characteristics Analysis and classification of Microblog Users

Research on Characteristics Analysis and Classification of M...

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2015 3rd International Conference on Machinery,Materials and Information Technology Applications(ICMMITA 2015)

作者： Jiang Xin Li WenMin State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications

Microblog has become an important part of social media, a large number of users send and thus spread information on this platform. Nowadays, the network environment of microblog is impacted by the presence of anomalies in users seriously. So the research on identifying the types of microblog users is of great significance. Based on the example of microblog, this paper selects some microblog users as research objects and thus analyzes and extracts the features of the selected users. Meanwhile, it uses statistical methods and classification methods in data mining to analyze user data. With the breakthrough point, the classification method C4.5 Decision Tree, this paper has trained the history data to form a classifier to make prognostic classification of new sample, which has realized high accuracy.

关键词： Microblog Users Feature Detection classification algorithm C4.5 Decision Tree

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ARS k NN-A k -NN Classifier Using Mass Based Similarity Measure

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Procedia Computer Science 2015年 46卷 457-462页

作者： Ashish Kumar Roheet Bhatnagar Sumit Srivastava Manipal University Jaipur Dehmi Kalan Jaipur 303007 India

k -Nearest Neighbor ( k -NN) classification technique is one of the most elementary and straightforward classification methods. Although distance learning is in the core of k -NN classification, similarity can be preferred upon distance in several practical applications. This paper proposes a novel algorithm for learning a class of an instance based on a similarity measure which does not calculate distance, for k -Nearest Neighbor ( k -NN) classification.

关键词： classification algorithm Data Mining Nearest Neighbor Similarity Measures

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AUTOMATIC VEHICLE classification IN SYSTEMS WITH SINGLE INDUCTIVE LOOP DETECTOR

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METROLOGY AND MEASUREMENT SYSTEMS 2014年第4期21卷 619-630页

作者： Gajda, J. Mielczarek, M. AGH Univ Sci & Technol Dept Measurement & Elect PL-30059 Krakow Poland

The work proposes a new method for vehicle classification, which allows treating vehicles uniformly at the stage of defining the vehicle classes, as well as during the classification itself and the assessment of its correctness. The sole source of information about a vehicle is its magnetic signature normalised with respect to the amplitude and duration. The proposed method allows defining a large number (even several thousand) of classes comprising vehicles whose magnetic signatures are similar according to the assumed criterion with precisely determined degree of similarity. The decision about the degree of similarity and, consequently, about the number of classes, is taken by a user depending on the classification purpose. An additional advantage of the proposed solution is the automated defining of vehicle classes for the given degree of similarity between signatures determined by a user. Thus the human factor, which plays a significant role in currently used methods, has been removed from the classification process at the stage of defining vehicle classes. The efficiency of the proposed approach to the vehicle classification problem was demonstrated on the basis of a large set of experimental data.

关键词： inductive loop detector magnetic signature vehicle classification classification algorithm

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classification algorithm sensitivity to training data with non representative attribute noise

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DECISION SUPPORT SYSTEMS 2009年第3期46卷 743-751页

作者： Mannino, Michael Yang, Yanjuan Ryu, Young Univ Colorado Sch Business Denver CO 80217 USA Univ Texas Dallas Sch Management Richardson TX 75083 USA

We present an empirical comparison of classification algorithms when training data contains attribute noise levels not representative of field data. To study algorithm sensitivity, we develop an innovative experimental design using noise situation, algorithm, noise level, and training set size as factors. Our results contradict conventional wisdom indicating that investments to achieve representative noise levels may not be worthwhile. ill general, over representative training noise Should be avoided while under representative training noise is less of a concern. However, interactions among algorithm, noise level, and training set size indicate that these general results may not apply to particular practice situations. (c) 2008 Elsevier B.V. All rights reserved.

关键词： Attribute noise Area under the Receiver Operating Curve classification algorithm

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Using classification algorithms for predicting durum wheat yield in the province of Buenos Aires

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2013年 96卷 173-179页

作者： Romero, Jose R. Roncallo, Pablo F. Akkiraju, Pavan C. Ponzoni, Ignacio Echenique, Viviana C. Carballido, Jessica A. CCT Bahia Blanca CERZOS CONICET Buenos Aires DF Argentina UNS Dept Agron Buenos Aires DF Argentina UNS DCIC Lab Invest & Desarrollo Comp Cient Buenos Aires DF Argentina CCT Bahia Blanca Planta Piloto Ingn Quim PLAPIQUI CONICET Bahia Blanca Buenos Aires Argentina

Wheat is one of the most important cereals worldwide for human nutrition. Tetraploid wheat (Triticum turgidum L. asp. durum, 2n = 28, genomes AABB) is mainly used to produce pasta. The main objective of durum wheat breeding programs is to develop varieties with good quality and high yields. Yield is a very complex trait, and depends on different yield components that are genetically controlled and affected by environmental constraints. In this context, machine learning constitutes an excellent alternative for the analysis of a high number of traits in order to extract the most relevant ones as confident predictors of the performance of this crop, allowing a better agricultural planning. Thus, we propose the use of machine learning algorithms for the classification of yield components and for the search of new rules to infer high yields at harvest of durum wheat. The main objective of this work was to obtain rules for predicting durum wheat yield through different machine learning algorithms, and compare them to detect the one that best fits the model. In order to achieve this goal, One-R, J48, Ibk and A priori algorithms were run with data collected by our research group of a RIL (recombinant inbreed lines) population growing in six different environments from the Province of Buenos Aires in Argentina. The results indicate that the A priori method obtains the best performance for all locations, and the classificators generated using the different algorithms share a common set of selected traits. Moreover, comparing these results with the previous ones obtained using different techniques, mainly QTL mapping, the traits indicated to be the most significant ones were the same. The analysis of the resulting rules shows the soundness in the agronomic relevance of the extracted knowledge. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Machine learning Expert system classification algorithm Yield Triticum turgidum Pasta wheat

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