An Efficient Malware Detection Approach for Malicious Android Application
Artificial intelligence is changing the game for cybersecurity, analyzing enormous amount of risky data, increasing response times and enlarging the abilities of under-resourced security tasks. While security as IT percentage grows at a fast pace, the cost of security beaches grows at a more rapid p...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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University of Sindh
2022-04-01
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| Series: | University of Sindh Journal of Information and Communication Technology |
| Subjects: | |
| Online Access: | https://sujo.usindh.edu.pk/index.php/USJICT/article/view/3843 |
| _version_ | 1797805601274724352 |
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| author | MADIHA AMJAD HUSSAIN Dr. Shariq Mahmood Khan SHAHZAD MEMON SYED RAZA HUSSAIN |
| author_facet | MADIHA AMJAD HUSSAIN Dr. Shariq Mahmood Khan SHAHZAD MEMON SYED RAZA HUSSAIN |
| author_sort | MADIHA AMJAD HUSSAIN |
| collection | DOAJ |
| description | Artificial intelligence is changing the game for cybersecurity, analyzing enormous amount of risky data, increasing response times and enlarging the abilities of under-resourced security tasks. While security as IT percentage grows at a fast pace, the cost of security beaches grows at a more rapid pace. The malware targeting Android is growing. Android systems holds more than 70 percent of the market share.This paper presents a simple APK analysis approach with the help of neural networks to identify malicious and benign application. The selected methodology is efficient in detecting malwares with an accuracy of 98.42% and false positive rate of 0.0121 |
| first_indexed | 2024-03-13T05:54:30Z |
| format | Article |
| id | doaj.art-0cedc5afcd1b4eb991235236195b484b |
| institution | Directory Open Access Journal |
| issn | 2521-5582 2523-1235 |
| language | English |
| last_indexed | 2024-03-13T05:54:30Z |
| publishDate | 2022-04-01 |
| publisher | University of Sindh |
| record_format | Article |
| series | University of Sindh Journal of Information and Communication Technology |
| spelling | doaj.art-0cedc5afcd1b4eb991235236195b484b2023-06-13T06:16:57ZengUniversity of SindhUniversity of Sindh Journal of Information and Communication Technology2521-55822523-12352022-04-01531201243843An Efficient Malware Detection Approach for Malicious Android ApplicationMADIHA AMJAD HUSSAIN0Dr. Shariq Mahmood Khan1SHAHZAD MEMON2SYED RAZA HUSSAIN3Department of Computer Science and Information Technology, NED University of Engineering and Technology, Karachi, PakistanDepartmetn of Computer Science and Information Technology NEDUET, KarachiA.H.S. Bukhari Institute of Information & Communication Technology, Faculty of Engineering & Technology, University of Sindh, Jamshoro Department of Electronics Engineering, University of Sindh, Jamshoro, PakistanArtificial intelligence is changing the game for cybersecurity, analyzing enormous amount of risky data, increasing response times and enlarging the abilities of under-resourced security tasks. While security as IT percentage grows at a fast pace, the cost of security beaches grows at a more rapid pace. The malware targeting Android is growing. Android systems holds more than 70 percent of the market share.This paper presents a simple APK analysis approach with the help of neural networks to identify malicious and benign application. The selected methodology is efficient in detecting malwares with an accuracy of 98.42% and false positive rate of 0.0121https://sujo.usindh.edu.pk/index.php/USJICT/article/view/3843malwares, android, neural network, apk, security |
| spellingShingle | MADIHA AMJAD HUSSAIN Dr. Shariq Mahmood Khan SHAHZAD MEMON SYED RAZA HUSSAIN An Efficient Malware Detection Approach for Malicious Android Application University of Sindh Journal of Information and Communication Technology malwares, android, neural network, apk, security |
| title | An Efficient Malware Detection Approach for Malicious Android Application |
| title_full | An Efficient Malware Detection Approach for Malicious Android Application |
| title_fullStr | An Efficient Malware Detection Approach for Malicious Android Application |
| title_full_unstemmed | An Efficient Malware Detection Approach for Malicious Android Application |
| title_short | An Efficient Malware Detection Approach for Malicious Android Application |
| title_sort | efficient malware detection approach for malicious android application |
| topic | malwares, android, neural network, apk, security |
| url | https://sujo.usindh.edu.pk/index.php/USJICT/article/view/3843 |
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