DyHAP: Dynamic Hybrid ANFIS-PSO Approach for Predicting Mobile Malware.
To deal with the large number of malicious mobile applications (e.g. mobile malware), a number of malware detection systems have been proposed in the literature. In this paper, we propose a hybrid method to find the optimum parameters that can be used to facilitate mobile malware identification. We...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2016-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC5017788?pdf=render |
| _version_ | 1819161717509193728 |
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| author | Firdaus Afifi Nor Badrul Anuar Shahaboddin Shamshirband Kim-Kwang Raymond Choo |
| author_facet | Firdaus Afifi Nor Badrul Anuar Shahaboddin Shamshirband Kim-Kwang Raymond Choo |
| author_sort | Firdaus Afifi |
| collection | DOAJ |
| description | To deal with the large number of malicious mobile applications (e.g. mobile malware), a number of malware detection systems have been proposed in the literature. In this paper, we propose a hybrid method to find the optimum parameters that can be used to facilitate mobile malware identification. We also present a multi agent system architecture comprising three system agents (i.e. sniffer, extraction and selection agent) to capture and manage the pcap file for data preparation phase. In our hybrid approach, we combine an adaptive neuro fuzzy inference system (ANFIS) and particle swarm optimization (PSO). Evaluations using data captured on a real-world Android device and the MalGenome dataset demonstrate the effectiveness of our approach, in comparison to two hybrid optimization methods which are differential evolution (ANFIS-DE) and ant colony optimization (ANFIS-ACO). |
| first_indexed | 2024-12-22T17:16:47Z |
| format | Article |
| id | doaj.art-1dd8a66fab2744aa87fcbe7ae754c050 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-22T17:16:47Z |
| publishDate | 2016-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-1dd8a66fab2744aa87fcbe7ae754c0502022-12-21T18:18:55ZengPublic Library of Science (PLoS)PLoS ONE1932-62032016-01-01119e016262710.1371/journal.pone.0162627DyHAP: Dynamic Hybrid ANFIS-PSO Approach for Predicting Mobile Malware.Firdaus AfifiNor Badrul AnuarShahaboddin ShamshirbandKim-Kwang Raymond ChooTo deal with the large number of malicious mobile applications (e.g. mobile malware), a number of malware detection systems have been proposed in the literature. In this paper, we propose a hybrid method to find the optimum parameters that can be used to facilitate mobile malware identification. We also present a multi agent system architecture comprising three system agents (i.e. sniffer, extraction and selection agent) to capture and manage the pcap file for data preparation phase. In our hybrid approach, we combine an adaptive neuro fuzzy inference system (ANFIS) and particle swarm optimization (PSO). Evaluations using data captured on a real-world Android device and the MalGenome dataset demonstrate the effectiveness of our approach, in comparison to two hybrid optimization methods which are differential evolution (ANFIS-DE) and ant colony optimization (ANFIS-ACO).http://europepmc.org/articles/PMC5017788?pdf=render |
| spellingShingle | Firdaus Afifi Nor Badrul Anuar Shahaboddin Shamshirband Kim-Kwang Raymond Choo DyHAP: Dynamic Hybrid ANFIS-PSO Approach for Predicting Mobile Malware. PLoS ONE |
| title | DyHAP: Dynamic Hybrid ANFIS-PSO Approach for Predicting Mobile Malware. |
| title_full | DyHAP: Dynamic Hybrid ANFIS-PSO Approach for Predicting Mobile Malware. |
| title_fullStr | DyHAP: Dynamic Hybrid ANFIS-PSO Approach for Predicting Mobile Malware. |
| title_full_unstemmed | DyHAP: Dynamic Hybrid ANFIS-PSO Approach for Predicting Mobile Malware. |
| title_short | DyHAP: Dynamic Hybrid ANFIS-PSO Approach for Predicting Mobile Malware. |
| title_sort | dyhap dynamic hybrid anfis pso approach for predicting mobile malware |
| url | http://europepmc.org/articles/PMC5017788?pdf=render |
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