7,251 research outputs found
Feature Representation for Online Signature Verification
Biometrics systems have been used in a wide range of applications and have
improved people authentication. Signature verification is one of the most
common biometric methods with techniques that employ various specifications of
a signature. Recently, deep learning has achieved great success in many fields,
such as image, sounds and text processing. In this paper, deep learning method
has been used for feature extraction and feature selection.Comment: 10 pages, 10 figures, Submitted to IEEE Transactions on Information
Forensics and Securit
Security and Privacy Issues in Wireless Mesh Networks: A Survey
This book chapter identifies various security threats in wireless mesh
network (WMN). Keeping in mind the critical requirement of security and user
privacy in WMNs, this chapter provides a comprehensive overview of various
possible attacks on different layers of the communication protocol stack for
WMNs and their corresponding defense mechanisms. First, it identifies the
security vulnerabilities in the physical, link, network, transport, application
layers. Furthermore, various possible attacks on the key management protocols,
user authentication and access control protocols, and user privacy preservation
protocols are presented. After enumerating various possible attacks, the
chapter provides a detailed discussion on various existing security mechanisms
and protocols to defend against and wherever possible prevent the possible
attacks. Comparative analyses are also presented on the security schemes with
regards to the cryptographic schemes used, key management strategies deployed,
use of any trusted third party, computation and communication overhead involved
etc. The chapter then presents a brief discussion on various trust management
approaches for WMNs since trust and reputation-based schemes are increasingly
becoming popular for enforcing security in wireless networks. A number of open
problems in security and privacy issues for WMNs are subsequently discussed
before the chapter is finally concluded.Comment: 62 pages, 12 figures, 6 tables. This chapter is an extension of the
author's previous submission in arXiv submission: arXiv:1102.1226. There are
some text overlaps with the previous submissio
ParaPlan: A Tool for Parallel Reachability Analysis of Planar Polygonal Differential Inclusion Systems
Andrei Sandler, and Olga Tveretina, ‘ParaPlan: A Tool for Parallel Reachability Analysis of Planar Polygonal Differential Inclusion Systems’, in Patricia Bouyer, Andrea Orlandini and Pierluigi San Pietro, eds. Proceedings Eight International Symposium on Games, Automata, Logics and Formal Verification (GandALF 2017), Rome, Italy, 20-22 September 2017, Electronic Proceedings in Theoretical Computer Science, Vol. 256: 283-296, September 2017. © 2017 The Author(s). This work is licensed under the Creative Commons Attribution License CC BY 4.0 https://creativecommons.org/licenses/by/4.0/We present the ParaPlan tool which provides the reachability analysis of planar hybrid systems defined by differential inclusions (SPDI). It uses the parallelized and optimized version of the algorithm underlying the SPeeDI tool. The performance comparison demonstrates the speed-up of up to 83 times with respect to the sequential implementation on various benchmarks. Some of the benchmarks we used are randomly generated with the novel approach based on the partitioning of the plane with Voronoi diagrams
Flexible Neural Network Architecture for Handwritten Signatures Recognition
This article illustrates modeling of flexible neural networks for handwritten signatures preprocessing. An input signature is interpolated to adjust inclination angle, than descriptor vector is composed. This information is preprocessed in proposed flexible neural network architecture, in which some neurons are becoming crucial for recognition and adapt to classification purposes. Experimental research results are compared in benchmark tests with classic approach to discuss efficiency of proposed solution
A Framework for Uncertain Cloud Data Security and Recovery Based on Hybrid Multi-User Medical Decision Learning Patterns
Machine learning has been supporting real-time cloud based medical computing systems. However, most of the computing servers are independent of data security and recovery scheme in multiple virtual machines due to high computing cost and time. Also, this cloud based medical applications require static security parameters for cloud data security. Cloud based medical applications require multiple servers to store medical records or machine learning patterns for decision making. Due to high Uncertain computational memory and time, these cloud systems require an efficient data security framework to provide strong data access control among the multiple users. In this work, a hybrid cloud data security framework is developed to improve the data security on the large machine learning patterns in real-time cloud computing environment. This work is implemented in two phases’ i.e. data replication phase and multi-user data access security phase. Initially, machine decision patterns are replicated among the multiple servers for Uncertain data recovering phase. In the multi-access cloud data security framework, a hybrid multi-access key based data encryption and decryption model is implemented on the large machine learning medical patterns for data recovery and security process. Experimental results proved that the present two-phase data recovering, and security framework has better computational efficiency than the conventional approaches on large medical decision patterns
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