@article {10.3844/jcssp.2012.983.986,
article_type = {journal},
title = {Application of Adaptive Neuro-Fuzzy Inference System for Information Secuirty},
author = {Altaher, Altyeb and Almomani, Ammar and Ramadass, Sureswaran},
volume = {8},
number = {6},
year = {2012},
month = {Apr},
pages = {983-986},
doi = {10.3844/jcssp.2012.983.986},
url = {https://thescipub.com/abstract/jcssp.2012.983.986},
abstract = {Problem statement: Computer networks are expanding at very fast rate and the number of network users is increasing day by day, for full utilization of networks it need to be secured against many threats including malware, which is harmful software with the capability to damage data and systems. Fuzzy rule based classification systems considered as an  active research area  in recent years, due to their unique capability of classifying. Approach: This study presents a neural fuzzy classifier based on Adaptive Neuro-Fuzzy Inference System (ANFIS) for malware detection. Firstly, the malware exe files was analyzed and the most important API calls were selected and used as training and testing datasets, using the training data set the ANFIS classifier learned how to detect the malware in the test dataset. Results and Conclusion: The performances of the Neuro fuzzy classifier were evaluated based on the performance of training and accuracy of classification, the results show that the proposed Neuro fuzzy classifier can detect the malware exe files effectively.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}