Download Advanced Computing and Systems for Security: Volume 1 by Rituparna Chaki, Agostino Cortesi, Khalid Saeed, Nabendu PDF

By Rituparna Chaki, Agostino Cortesi, Khalid Saeed, Nabendu Chaki

The e-book includes the prolonged model of the works which have been provided and mentioned within the moment foreign Doctoral Symposium on utilized Computation and safeguard platforms (ACSS 2015) held in the course of might 23-25, 2015 in Kolkata, India. The symposium has been together prepared by way of the AGH college of technological know-how & know-how, Cracow, Poland; Ca’ Foscari college, Venice, Italy and college of Calcutta, India. The ebook is split into volumes and offers dissertation works within the components of picture Processing, Biometrics-based Authentication, gentle Computing, information Mining, subsequent iteration Networking and community safety, distant Healthcare, Communications, Embedded platforms, software program Engineering and repair Engineering.

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Systolic array implementation of euclid’s algorithm for inversion and division in GF(2m). IEEE Trans. Comput. 47(10), 1161–1167 (1998) 17. : On computing multiplicative inverses in GF(2m). IEEE Trans. Comput. 42(8), 1010–1015 (1993) 18. : VLSI architecture for computing multiplications and inverses in GF(2m). IEEE Trans. Comput. C-34 (8), 709–717 (1985) 19. : A statistical test suite for random and pseudorandom number generators for cryptographic applications. S. Department of Commerce (2010) 20.

Feature selection algorithms can be classified into filters and wrappers [22]. Filter methods select subset of features as a preprocessing step, independent of the induction (learning) algorithm. Wrappers utilize the classifier (learning machine) performance to evaluate the goodness of feature subsets [19]. As Wrapper methods are widely recognized as superior alternative in supervised learning problems [23], we choose five wrapper methods, 24 N. Akhter et al. namely statistical dependency (SD) which estimates the statistical dependency between features and associated class labels using a quantized feature space [19, 23], mutual information (MI) measures arbitrary dependencies between random variables [19, 24], random subset feature selection (RSFS) aims to discover a set of features that perform better than an average feature of the available feature set [19], sequential forward selection (SFS) works in the opposite direction, initially starting from an empty set, the feature set is iteratively updated by including the feature which results in maximal score in each step [19], and sequential floating forward selection (SFFS) is improvisation over SFS algorithm, it uses SFS as baseline method [19] and further extends by iteratively finding the least significant features and eliminates it; this process continues till a desired number of features are not obtained.

Ahuja (&) Á A. Jana Á A. Swarnkar Á R. in A. in A. in R. in © Springer India 2016 R. Chaki et al. K. Ahuja et al. has increased rapidly in recent years, and it has become a predominant type of attacks. val=100” is used to view all products with cost less than $100. val = ‘100’ OR ‘1’ = ‘1’, the dynamically created SQL statement “SELECT * FROM Products WHERE Price < ‘100’ OR ‘1’ = ‘1’ ORDER BY ProductDescription” extracts all product information as the WHERE clause evaluates to true always. In addition to the above tautology-based SQL injection attacks, there exist various other forms of attacks with various attacker intents.

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