scholarly journals A System to Detect and Block SQL Injection with the help of Multi Agent System using Artificial Neural Network

2013 ◽  
Vol 71 (12) ◽  
pp. 21-26
Author(s):  
Rohit Garde ◽  
D R Anekar ◽  
Niraj Kulkarni ◽  
Mayur Ghadge
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Multi Agent System ◽  
Agent System ◽  
Sql Injection ◽  
Multi Agent ◽  
Artificial Neural

scholarly journals Multi-Agent System for Detecting and Blocking SQL Injection

2013 ◽  
Vol 64 (15) ◽  
pp. 42-45
Author(s):  
Niraj Kulkarni ◽  
D R Anekar ◽  
Mayur Ghadge ◽  
Rohit Garde
Keyword(s):  
Multi Agent System ◽  
Agent System ◽  
Sql Injection ◽  
Multi Agent

scholarly journals A SELF-ADAPTABLE DISTRIBUTED CBR VERSION OF THE EQUIVOX SYSTEM

2016 ◽  
Vol 28 (04) ◽  
pp. 1650028
Author(s):  
Julien Henriet ◽  
Christophe Lang ◽  
Ronnie Muthada Pottayya ◽  
Karla Breschi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Three Dimensional ◽  
Multi Agent System ◽  
Accidental Exposure ◽  
Interpolation Accuracy ◽  
Multi Agent ◽  
Artificial Neural Network Ann ◽  
Numerical Representations ◽  
Voxel Phantoms

Three dimensional (3D) voxel phantoms are numerical representations of human bodies, used by physicians in very different contexts. In the controlled context of hospitals, where from 2 to 10 subjects may arrive per day, phantoms are used to verify computations before therapeutic exposure to radiation of cancerous tumors. In addition, 3D phantoms are used to diagnose the gravity of accidental exposure to radiation. In such cases, there may be from 10 to more than 1000 subjects to be diagnosed simultaneously. In all of these cases, computation accuracy depends on a single such representation. In this paper, we present EquiVox which is a tool composed of several distributed functions and enables to create, as quickly and as accurately as possible, 3D numerical phantoms that fit anyone, whatever the context. It is based on a multi-agent system. Agents are convenient for this kind of structure, they can interact together and they may have individual capacities. In EquiVox, the phantoms adaptation is a key phase based on artificial neural network (ANN) interpolations. Thus, ANNs must be trained regularly in order to take into account newly capitalized subjects and to increase interpolation accuracy. However, ANN training is a time-consuming process. Consequently, we have built Equivox to optimize this process. Thus, in this paper, we present our architecture, based on agents and ANN, and we put the stress on the adaptation module. We propose, next, some experimentations in order to show the efficiency of the EquiVox architecture.

scholarly journals MASKS: A Multi-Artificial Neural Networks System's verification approach

2020 ◽  
Author(s):  
Amirhoshang Hoseinpour Dehkordi ◽  
Majid Alizadeh ◽  
Ebrahim Ardeshir-Larijani ◽  
Ali Movaghar
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Special Property ◽  
External Information ◽  
Multi Agent Systems ◽  
Classification Problems ◽  
Multi Agent ◽  
Artificial Neural

<div>Artificial Neural networks are one of the most widely applied approaches for classification problems. However, developing an errorless artificial neural network is in practice impossible, due to the statistical nature of such networks. The employment of artificial neural networks in critical applications has rendered any such emerging errors, in these systems, incredibly more significant. Nevertheless, the real consequences of such errors have not been addressed, especially due to lacking verification approaches. This study aims to develop a verification method that eliminates errors through the integration of multiple artificial neural networks. In order to do this, first of all, a special property has been defined, by the authors, to extract the knowledge of these artificial neural networks. </div><div>Furthermore, a multi-agent system has been designed, itself comprised of multiple artificial neural networks, in order to check whether the aforementioned special property has been satisfied, or not. Also, in order to help examine the reasoning concerning the aggregation of the distributed knowledge, itself gained through the combined effort of separate artificial neural networks and acquired external information sources, a dynamic epistemic logic-based method has been proposed.</div><div>Finally, we believe aggregated knowledge may lead to self-awareness for the system. As a result, our model shall be capable of verifying specific inputs, if the cumulative knowledge of the entire system proves its correctness. </div><div>In conclusion, and formulated for multi-agent systems, a knowledge-sharing algorithm (Abbr. MASKS) has been developed. Which after being applied on the MNIST dataset successfully reduced the error rate to roughly one-eighth of previous runs on individual artificial neural network in the same model. </div>

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