2013 12th International Conference on Machine Learning and Applications最新文献

Massive GIS Database System with Autonomic Resource Management 具有自主资源管理的海量GIS数据库系统

2013 12th International Conference on Machine Learning and Applications Pub Date : 2013-12-04 DOI: 10.1109/ICMLA.2013.161

Yun Lu, Ming Zhao, Guangqiang Zhao, Lixi Wang, N. Rishe

引用次数: 2

Aiding Intrusion Analysis Using Machine Learning 利用机器学习辅助入侵分析

2013 12th International Conference on Machine Learning and Applications Pub Date : 2013-12-04 DOI: 10.1109/ICMLA.2013.103

Loai Zomlot, S. C. Sundaramurthy, Doina Caragea, Xinming Ou

{"title":"Aiding Intrusion Analysis Using Machine Learning","authors":"Loai Zomlot, S. C. Sundaramurthy, Doina Caragea, Xinming Ou","doi":"10.1109/ICMLA.2013.103","DOIUrl":"https://doi.org/10.1109/ICMLA.2013.103","url":null,"abstract":"Intrusion analysis, i.e., the process of combing through IDS alerts and audit logs to identify real successful and attempted attacks, remains a difficult problem in practical network security defense. The major contributing cause to this problem is the high false-positive rate in the sensors used by IDS systems to detect malicious activities. The goal of our work is to examine whether a machine-learned classifier can help a human analyst filter out non-interesting scenarios reported by an IDS alert correlator, so that analysts' time can be saved. This research is conducted in the open-source SnIPS intrusion analysis framework. Throughout observing the output of SnIPS running on our departmental network, we found that an analyst would need to perform repetitive tasks in pruning out the false positives in the correlation graphs produced by it. We hypothesized that such repetitive tasks can yield (limited) labeled data that can enable the use of a machine learning-based approach to prune SnIPS' output based on the human analysts' feedback, much similar to spam filters that can learn from users' past judgment to prune emails. Our goal is to classify the correlation graphs produced from SnIPS into \"interesting\" and \"non-interesting\", where \"interesting\" means that a human analyst would want to conduct further analysis on the events. We spent significant amount of time manually labeling SnIPS' output correlations based on this criterion, and built prediction models using both supervised and semi-supervised learning approaches. Our experiments revealed a number of interesting observations that give insights into the pitfalls and challenges of applying machine learning in intrusion analysis. The experimentation results also indicate that semi-supervised learning is a promising approach towards practical machine learning-based tools that can aid human analysts, when a limited amount of labeled data is available.","PeriodicalId":168867,"journal":{"name":"2013 12th International Conference on Machine Learning and Applications","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123123494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 19

First-Order Probabilistic Model for Hybrid Recommendations 混合推荐的一阶概率模型

2013 12th International Conference on Machine Learning and Applications Pub Date : 2013-12-04 DOI: 10.1109/ICMLA.2013.118

Julia Hoxha, Achim Rettinger

引用次数: 10

Terrain Classification for a Quadruped Robot 四足机器人地形分类

2013 12th International Conference on Machine Learning and Applications Pub Date : 2013-12-04 DOI: 10.1109/ICMLA.2013.39

Jonas Degrave, Robin Van Cauwenbergh, F. Wyffels, Tim Waegeman, B. Schrauwen

引用次数: 17

Rot-SiLA: A Novel Ensemble Classification Approach Based on Rotation Forest and Similarity Learning Using Nearest Neighbor Algorithm Rot-SiLA:一种基于旋转森林和最近邻算法相似性学习的集成分类方法

2013 12th International Conference on Machine Learning and Applications Pub Date : 2013-12-04 DOI: 10.1109/ICMLA.2013.16

Muhammad Shaheryar, M. Khalid, A. M. Qamar

{"title":"Rot-SiLA: A Novel Ensemble Classification Approach Based on Rotation Forest and Similarity Learning Using Nearest Neighbor Algorithm","authors":"Muhammad Shaheryar, M. Khalid, A. M. Qamar","doi":"10.1109/ICMLA.2013.16","DOIUrl":"https://doi.org/10.1109/ICMLA.2013.16","url":null,"abstract":"Recent years have seen a great inclination towards Machine Learning classification and researchers are thinking in terms of achieving accuracy and correctness. Many studied have proved that an ensemble of classifiers outperform individual ones in terms of accuracy. Qamar et al. have developed a Similarity Learning Algorithm (SiLA) based on a combination of k nearest neighbor algorithm and Voted Perceptron. This approach is different from other state of the art algorithms in the sense that it learns appropriate similarity metrics rather than distance-based ones for all types of datasets i.e. textual as well as non-textual. In this paper, we present a novel ensemble classifier Rot-SiLA which is developed by combining Rotation Forest algorithm and SiLA. The Rot-SiLA ensemble classifier is built upon two types of approaches, one based on standard kNN and another based on symmetric kNN (SkNN), just as was the case with SiLA algorithm. It has been observed that Rot-SiLA ensemble outperforms other variants of the Rotation Forest ensemble as well as SiLA significantly when experiments were conducted with 14 UCI repository data sets. The significance of the results was determined by s-test.","PeriodicalId":168867,"journal":{"name":"2013 12th International Conference on Machine Learning and Applications","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122200470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Unsupervised Video Summarization via Dynamic Modeling-Based Hierarchical Clustering 基于动态建模的分层聚类的无监督视频摘要

2013 12th International Conference on Machine Learning and Applications Pub Date : 2013-12-04 DOI: 10.1109/ICMLA.2013.140

Karim Ahmed, Nagia M. Ghanem, M. Ismail

引用次数: 21

A Comprehension Approach for Formalizing Privacy Rules of HIPAA for Decision Support 基于决策支持的HIPAA隐私规则形式化理解方法

2013 12th International Conference on Machine Learning and Applications Pub Date : 2013-12-04 DOI: 10.1109/ICMLA.2013.154

Imran Khan, Moheeb Alwarsh, J. Khan

引用次数: 3

Evolutionary Content Pre-fetching in Mobile Networks 移动网络中的进化内容预抓取

2013 12th International Conference on Machine Learning and Applications Pub Date : 2013-12-04 DOI: 10.1109/ICMLA.2013.79

Omar K. Shoukry, M. Fayek

{"title":"Evolutionary Content Pre-fetching in Mobile Networks","authors":"Omar K. Shoukry, M. Fayek","doi":"10.1109/ICMLA.2013.79","DOIUrl":"https://doi.org/10.1109/ICMLA.2013.79","url":null,"abstract":"Recently, an increasing number of smart phone users are eagerly using the cellular network in extensive data applications. In particular, multimedia downloads generated by Internet-capable smart phones and other portable devices (such as iPad) have been widely recognized as the major source for strains in cellular networks, to a degree where service quality for all users is significantly impacted. Lately, patterns in both the content consumption as well as the Wi-Fi access by the users were alleged to be available. In this paper we introduce a technique to schedule the content for prefetching based on mobile usage patterns. This technique utilizes both a content profile as well as a bandwidth profile to schedule content for prefetching. Users can then use the cached version of the content in order to achieve a better user experience and reduce the peak-to-average ratio in mobile networks, especially during peak hours of the day. An experiment using real users traces was conducted and the results after applying the proposed evolutionary scheduling algorithm show that up to 70 percent of the user content requests can be fulfilled i.e. the content was successfully cached before request.","PeriodicalId":168867,"journal":{"name":"2013 12th International Conference on Machine Learning and Applications","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114327137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Hybrid Ontology-Based Information Extraction for Automated Text Grading 基于混合本体的文本自动分级信息提取

2013 12th International Conference on Machine Learning and Applications Pub Date : 2013-12-04 DOI: 10.1109/ICMLA.2013.73

Fernando Gutierrez, D. Dou, Adam Martini, S. Fickas, Hui Zong

引用次数: 22

Evolving Hybrid Neural Fuzzy Network for System Modeling and Time Series Forecasting 演化混合神经模糊网络用于系统建模和时间序列预测

2013 12th International Conference on Machine Learning and Applications Pub Date : 2013-12-04 DOI: 10.1109/ICMLA.2013.152

R. Rosa, F. Gomide, R. Ballini

{"title":"Evolving Hybrid Neural Fuzzy Network for System Modeling and Time Series Forecasting","authors":"R. Rosa, F. Gomide, R. Ballini","doi":"10.1109/ICMLA.2013.152","DOIUrl":"https://doi.org/10.1109/ICMLA.2013.152","url":null,"abstract":"This paper introduces an evolving hybrid fuzzy neural network-based modeling approach using neurons based on uninorms and sigmoidal activation functions in a feed forward structure. The evolving neural network simultaneously adapts its structure and updates its weights using a stream of data. Currently, learning from data streams is a challenging and important issue because often traditional learning methods are impracticable to handle nonstationary and dynamic environments from where data come from. Uninorm-based neurons generalize fuzzy neurons models based on triangular norms and co norms. Uninorms increase the flexibility and generality of fuzzy neurons because they can modify their processing capabilities by adjusting their identity elements. In addition to structural plasticity induced by evolving network structures, identity elements adjustment adds functional plasticity in neural network processing. A recursive procedure to granulate the input space and uncover the evolving neural network structure, and an extreme learning-based algorithm to learn network weights are developed to train the neural network. Computational results show that the evolving neural fuzzy network is competitive when compared with representative methods of the current state of the art in evolving modeling.","PeriodicalId":168867,"journal":{"name":"2013 12th International Conference on Machine Learning and Applications","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125723559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 24