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Application of Adaptive Neuro-Fuzzy Inference System for Information Secuirty
| Content Provider | Semantic Scholar |
|---|---|
| Author | Altaher, Altyeb Almomani, Ammar Ramadass, Sureswaran |
| Copyright Year | 2012 |
| Abstract | Problem statement: Computer networks are expanding at very fast rate and the number of network users is increasing day by day, for full ut i ization of networks it need to be secured against many threats including malware, which is harmful so ftware with the capability to damage data and systems. Fuzzy rule based classification systems co n idered as an active research area in recent yea rs, due to their unique capability of classifying. Approach: This study presents a neural fuzzy classifier based on Adaptive Neuro-Fuzzy Inference System (ANF IS) for malware detection. Firstly, the malware exe files was analyzed and the most importa nt API calls were selected and used as training and testing datasets, using the training data set t he ANFIS classifier learned how to detect the malwa re in the test dataset. Results and Conclusion: The performances of the Neuro fuzzy classifier wer e evaluated based on the performance of training and accuracy of classification, the results show that t e proposed Neuro fuzzy classifier can detect the malw are exe files effectively. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://thescipub.com/PDF/jcssp.2012.983.986.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | A-normal form Adaptive neuro fuzzy inference system Application programming interface CNS disorder Classification Fuzzy rule Malware Neuro-fuzzy Performance RS 43285-193 Test set |
| Content Type | Text |
| Resource Type | Article |
