ANN Modelling on Vulnerabilities Detection in Code Smells-Associated Android Applications
There has been a lot of software design concerns in recent years that come under the code smell. Android Applications Developments experiences more security issues related to code smells that lead to vulnerabilities in software. This research focuses on the vulnerability detection in Android applica...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Sciendo
2022-02-01
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| Series: | Foundations of Computing and Decision Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/fcds-2022-0001 |
| _version_ | 1798039753390555136 |
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| author | Gupta Aakanshi Sharma Deepanshu Phulli Kritika |
| author_facet | Gupta Aakanshi Sharma Deepanshu Phulli Kritika |
| author_sort | Gupta Aakanshi |
| collection | DOAJ |
| description | There has been a lot of software design concerns in recent years that come under the code smell. Android Applications Developments experiences more security issues related to code smells that lead to vulnerabilities in software. This research focuses on the vulnerability detection in Android applications which consists of code smells. A multi-layer perceptron-based ANN model is generated for detection of software vulnerabilities and has a precision value of 74.7% and 79.6% accuracy with 2 hidden layers. The focus is laid on 1390 Android classes and involves association mining of the software vulnerabilities with android code smells using APRIORI algorithm. The generated ANN model The findings represent that Member Ignoring Method (MIM) code smell shows an association with Bean Member Serialization (BMS) vulnerability having 86% confidence level and 0.48 support value. An algorithm has also been proposed that would help developers in detecting software vulnerability in the smelly source code of an android applications at early stages of development. |
| first_indexed | 2024-04-11T21:58:06Z |
| format | Article |
| id | doaj.art-ee7890ef112a4f4591b0b2174047320c |
| institution | Directory Open Access Journal |
| issn | 2300-3405 |
| language | English |
| last_indexed | 2024-04-11T21:58:06Z |
| publishDate | 2022-02-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Foundations of Computing and Decision Sciences |
| spelling | doaj.art-ee7890ef112a4f4591b0b2174047320c2022-12-22T04:01:02ZengSciendoFoundations of Computing and Decision Sciences2300-34052022-02-0147132610.2478/fcds-2022-0001ANN Modelling on Vulnerabilities Detection in Code Smells-Associated Android ApplicationsGupta Aakanshi0Sharma Deepanshu1Phulli Kritika2ASET, GGSIPU, New Delhi, IndiaExecutive Branch-IT, Indian NavyApplication Development Associate, Accenture, IndiaThere has been a lot of software design concerns in recent years that come under the code smell. Android Applications Developments experiences more security issues related to code smells that lead to vulnerabilities in software. This research focuses on the vulnerability detection in Android applications which consists of code smells. A multi-layer perceptron-based ANN model is generated for detection of software vulnerabilities and has a precision value of 74.7% and 79.6% accuracy with 2 hidden layers. The focus is laid on 1390 Android classes and involves association mining of the software vulnerabilities with android code smells using APRIORI algorithm. The generated ANN model The findings represent that Member Ignoring Method (MIM) code smell shows an association with Bean Member Serialization (BMS) vulnerability having 86% confidence level and 0.48 support value. An algorithm has also been proposed that would help developers in detecting software vulnerability in the smelly source code of an android applications at early stages of development.https://doi.org/10.2478/fcds-2022-0001software vulnerabilitiescode smellsandroidannapriori algorithmdeep learningmachine learning |
| spellingShingle | Gupta Aakanshi Sharma Deepanshu Phulli Kritika ANN Modelling on Vulnerabilities Detection in Code Smells-Associated Android Applications Foundations of Computing and Decision Sciences software vulnerabilities code smells android ann apriori algorithm deep learning machine learning |
| title | ANN Modelling on Vulnerabilities Detection in Code Smells-Associated Android Applications |
| title_full | ANN Modelling on Vulnerabilities Detection in Code Smells-Associated Android Applications |
| title_fullStr | ANN Modelling on Vulnerabilities Detection in Code Smells-Associated Android Applications |
| title_full_unstemmed | ANN Modelling on Vulnerabilities Detection in Code Smells-Associated Android Applications |
| title_short | ANN Modelling on Vulnerabilities Detection in Code Smells-Associated Android Applications |
| title_sort | ann modelling on vulnerabilities detection in code smells associated android applications |
| topic | software vulnerabilities code smells android ann apriori algorithm deep learning machine learning |
| url | https://doi.org/10.2478/fcds-2022-0001 |
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