Intrusion detection in networks using crow search optimization algorithm with adaptive neuro-fuzzy inference system

2020 ◽  
Vol 79 ◽  
pp. 103261 ◽  
Author(s):  
S Manimurugan ◽  
Al-qdah Majdi ◽  
Mustaffa Mohmmed ◽  
C Narmatha ◽  
R Varatharajan
Keyword(s):  
Intrusion Detection ◽  
Optimization Algorithm ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Search Optimization ◽  
Neuro Fuzzy

scholarly journals Wildland Fire Susceptibility Mapping Using Support Vector Regression and Adaptive Neuro-Fuzzy Inference System-Based Whale Optimization Algorithm and Simulated Annealing

2021 ◽  
Vol 10 (6) ◽  
pp. 382
Author(s):  
A’kif Al-Fugara ◽  
Ali Nouh Mabdeh ◽  
Mohammad Ahmadlou ◽  
Hamid Reza Pourghasemi ◽  
Rida Al-Adamat ◽  
...  
Keyword(s):  
Simulated Annealing ◽  
Optimization Algorithm ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Wildland Fire ◽  
Hybrid Models ◽  
Support Vector ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Whale Optimization

Fires are one of the most destructive forces in natural ecosystems. This study aims to develop and compare four hybrid models using two well-known machine learning models, support vector regression (SVR) and the adaptive neuro-fuzzy inference system (ANFIS), as well as two meta-heuristic models, the whale optimization algorithm (WOA) and simulated annealing (SA) to map wildland fires in Jerash Province, Jordan. For modeling, 109 fire locations were used along with 14 relevant factors, including elevation, slope, aspect, land use, normalized difference vegetation index (NDVI), rainfall, temperature, wind speed, solar radiation, soil texture, topographic wetness index (TWI), distance to drainage, and population density, as the variables affecting the fire occurrence. The area under the receiver operating characteristic (AUROC) was used to evaluate the accuracy of the models. The findings indicated that SVR-based hybrid models yielded a higher AUROC value (0.965 and 0.949) than the ANFIS-based hybrid models (0.904 and 0.894, respectively). Wildland fire susceptibility maps can play a major role in shaping firefighting tactics.

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.

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