<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</abstract>

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.

ANN Modelling on Vulnerabilities Detection in Code Smells-Associated Android Applications

Foundations of Computing and Decision Sciences

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

There has been a lot of software design concerns in recent years that come under the code smell. Android Applications Developments experiences more security...

ANN Modelling on Vulnerabilities Detection in Code Smells-Associated Android Applications

Data publikacji: 23 lut 2022

Zakres stron: 3 - 26

Otrzymano: 15 sty 2021

Przyjęty: 30 wrz 2021

DOI: https://doi.org/10.2478/fcds-2022-0001

Słowa kluczowe
Software Vulnerabilities, Code Smells, Android, ANN, Apriori Algorithm, Deep Learning, Machine Learning

© 2022 Aakanshi Gupta et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

ANN Modelling on Vulnerabilities Detection in Code Smells-Associated Android Applications

Data publikacji: 23 lut 2022

Zakres stron: 3 - 26

Otrzymano: 15 sty 2021

Przyjęty: 30 wrz 2021

DOI: https://doi.org/10.2478/fcds-2022-0001

Słowa kluczoweSoftware Vulnerabilities, Code Smells, Android, ANN, Apriori Algorithm, Deep Learning, Machine Learning

© 2022 Aakanshi Gupta et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Słowa kluczowe
Software Vulnerabilities, Code Smells, Android, ANN, Apriori Algorithm, Deep Learning, Machine Learning