A new approach to intrusion detection in databases by using artificial neuro fuzzy inference system

2015 ◽  
Vol 7 (3/4) ◽  
pp. 254 ◽  
Author(s):  
Anitarani Brahma ◽  
Suvasini Panigrahi
Keyword(s):  
Intrusion Detection ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
New Approach ◽  
Neuro Fuzzy

A new approach to estimate anthropometric measurements by adaptive neuro-fuzzy inference system

2003 ◽  
Vol 32 (2) ◽  
pp. 105-114 ◽  
Author(s):  
M. Dursun Kaya ◽  
A. Samet Hasiloglu ◽  
Mahmut Bayramoglu ◽  
Hakki Yesilyurt ◽  
A. Fahri Ozok
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Anthropometric Measurements ◽  
Inference System ◽  
New Approach ◽  
Neuro Fuzzy

scholarly journals Computation of Risk Severity of the Malicious Node using Adaptive Neuro Fuzzy Inference System (ANFIS)

2019 ◽  
Vol 8 (1) ◽  
pp. 9-14
Author(s):  
R. Dharmarajan ◽  
V. Thiagarasu
Keyword(s):  
Intrusion Detection ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Detection System ◽  
Malicious Nodes ◽  
Inference System ◽  
Detection Systems ◽  
Network Intrusion ◽  
Neuro Fuzzy ◽  
Network Intrusion Detection Systems

The Intrusion Detection System (IDS) can be employed broadly for safety network. Intrusion Detection Systems (IDSs) are commonly positioned alongside with other protecting safety mechanisms, such as authentication and access control, as a subsequent line of defence that guards data structures. In this paper, Adaptive Neuro Fuzzy Inference System has utilized to predict the risk severity of the malicious nodes found the previous classification phase.

scholarly journals Using Adaptive Neuro-Fuzzy Inference System in Alert Management of Intrusion Detection Systems

2012 ◽  
Vol 4 (11) ◽  
pp. 32-38 ◽  
Author(s):  
Zahra Atashbar Orang ◽  
Ezzat Moradpour ◽  
Ahmad Habibizad Navin ◽  
Amir Azimi Alasti Ahrabim ◽  
Mir Kamal Mirnia
Keyword(s):  
Intrusion Detection ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Intrusion Detection Systems ◽  
Inference System ◽  
Detection Systems ◽  
Neuro Fuzzy

Loss of Excitation Faults Detection in Hydro-Generators Using an Adaptive Neuro Fuzzy Inference System

2016 ◽  
Vol 1 (2) ◽  
pp. 300 ◽  
Author(s):  
Mohamed Salah El-Din Abdel Aziz ◽  
Mohamed Elsamahy ◽  
Mohamed Moustafa ◽  
Fahmy Bendary
Keyword(s):  
Design Process ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Impedance Measurements ◽  
New Approach ◽  
Line Voltage ◽  
Artificial Intelligent ◽  
Neuro Fuzzy ◽  
Generator Terminal

<em>This paper presents a new approach for Loss of Excitation (LOE) faults detection in Hydro-generators using Adaptive Neuro Fuzzy Inference System. The proposed scheme was trained by data from simulation of a 345kV system under various faults conditions and tested for different loading conditions. Details of the design process and the results of performance using the proposed technique are discussed in the paper. Two different techniques are discussed in this article according to the type of inputs to the proposed ANFIS unit, the generator terminal impedance measurements (R &amp; X) and the generator RMS Line to Line voltage and Phase current (Vtrms &amp; Ia). The two proposed techniques results are compared with each other and are compared with the traditional distance relay response in addition to other technique. The results show that the proposed Artificial Intelligent based technique is efficient in the Loss of Excitation faults (LOE) detection process and the obtained results are very promising</em>.

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