Titlebook: International Joint Conference: 12th International Conference on Computational Intelligence in Secur; Seville, Spain, May Francisco Martín

metropolitan

Mirkwood: An Online Parallel Crawlersed crawler, analysing exclusively a limited set of hosts, it includes seed extraction capabilities, which allows it to autonomously obtain high quality sites to crawl. Mirkwood is designed to run in a computer cluster, taking advantage of all the cores of its individual machines (virtual or physica

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Improving SVM Classification on Imbalanced Datasets for EEG-Based Person Authenticationf genuine clients and impostors, and this issue heavily impacts on the performance of EEG-based person authentication using SVM. In this paper, we propose a new bias method for SVM binary classification to improve the performance of the minority class in imbalanced datasets. Our experiments on EEG d

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archetype

Toward an Privacy Protection Based on Access Control Model in Hybrid Cloud for Healthcare SystemsSuch collaborations raise the need for an authorization system to implement network security and protect sensitive data of each patient. Without a proper security protocol, this confidential information could be exploited by malicious attackers. This paper presents a novel access control system call

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Video Anomaly Detection and Localization in Crowded Scenesapproach to predict and localize anomaly events. In this paper, a new framework for motion extraction called BQM is proposed. Then, the regions of interest are extracted and a filtering process is applied to eliminate the non-significant ones. However, for more precision, the HFG descriptor is appli

巨碩

Beta Chaotic Map Based Image Steganography, we proposed a steganographic scheme based on Beta chaotic map in order to embed secret images bits into cover images. Thus a secure transmission of data could be established. We applied the PSNR, MSE and histogram tests on the obtained images and comparisons were made with different existing metho

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Allodynia

Vasoconstrictor

Neuro-Evolutionary Feature Selection to Detect Android Malwarethe high number of features that can be obtained from Android apps in order to improve detection. Present paper proposes wrapper feature selection by applying a genetic algorithm and a Multilayer Perceptron. In order to validate this proposal, feature selection is performed on the well-known Drebin

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