Loading…
PIndroid: A novel Android malware detection system using ensemble learning
The extensive usage of smartphones has been the major driving force behind a drastic increase of new security threats. The stealthy techniques used by malware make them hard to detect with signature based intrusion detection and anti-malware methods. In this paper, we present PIndroid|a novel Permis...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Default Article |
| Published: |
2017
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/2134/24695 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1818171267448045568 |
|---|---|
| author | Fauzia Idrees Muttukrishnan Rajarajan Mauro Conti Yogachandran Rahulamathavan Tom Chen |
| author_facet | Fauzia Idrees Muttukrishnan Rajarajan Mauro Conti Yogachandran Rahulamathavan Tom Chen |
| author_sort | Fauzia Idrees (7185641) |
| collection | Figshare |
| description | The extensive usage of smartphones has been the major driving force behind a drastic increase of new security threats. The stealthy techniques used by malware make them hard to detect with signature based intrusion detection and anti-malware methods. In this paper, we present PIndroid|a novel Permissions and Intents based framework for identifying Android malware apps. To the best of our knowledge, PIndroid is the first solution that uses a combination of permissions and intents supplemented with multiple stages of classifiers for malware detection. Ensemble techniques are applied for optimization of detection results. We apply the proposed approach on 1,745 real world applications and obtain 99.8% accuracy which is the best reported to date. Empirical results suggest that our proposed framework built on permissions and intents is effective in detecting malware applications. |
| format | Default Article |
| id | rr-article-9463715 |
| institution | Loughborough University |
| publishDate | 2017 |
| record_format | Figshare |
| spelling | rr-article-94637152017-01-01T00:00:00Z PIndroid: A novel Android malware detection system using ensemble learning Fauzia Idrees (7185641) Muttukrishnan Rajarajan (7185323) Mauro Conti (7185398) Yogachandran Rahulamathavan (2497186) Tom Chen (2035723) Malware classification Permissions Intents Ensemble methods Colluding applications The extensive usage of smartphones has been the major driving force behind a drastic increase of new security threats. The stealthy techniques used by malware make them hard to detect with signature based intrusion detection and anti-malware methods. In this paper, we present PIndroid|a novel Permissions and Intents based framework for identifying Android malware apps. To the best of our knowledge, PIndroid is the first solution that uses a combination of permissions and intents supplemented with multiple stages of classifiers for malware detection. Ensemble techniques are applied for optimization of detection results. We apply the proposed approach on 1,745 real world applications and obtain 99.8% accuracy which is the best reported to date. Empirical results suggest that our proposed framework built on permissions and intents is effective in detecting malware applications. 2017-01-01T00:00:00Z Text Journal contribution 2134/24695 https://figshare.com/articles/journal_contribution/PIndroid_A_novel_Android_malware_detection_system_using_ensemble_learning/9463715 CC BY-NC-ND 4.0 |
| spellingShingle | Malware classification Permissions Intents Ensemble methods Colluding applications Fauzia Idrees Muttukrishnan Rajarajan Mauro Conti Yogachandran Rahulamathavan Tom Chen PIndroid: A novel Android malware detection system using ensemble learning |
| title | PIndroid: A novel Android malware detection system using ensemble learning |
| title_full | PIndroid: A novel Android malware detection system using ensemble learning |
| title_fullStr | PIndroid: A novel Android malware detection system using ensemble learning |
| title_full_unstemmed | PIndroid: A novel Android malware detection system using ensemble learning |
| title_short | PIndroid: A novel Android malware detection system using ensemble learning |
| title_sort | pindroid: a novel android malware detection system using ensemble learning |
| topic | Malware classification Permissions Intents Ensemble methods Colluding applications |
| url | https://hdl.handle.net/2134/24695 |