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

IF 1.8 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Foundations of Computing and Decision Sciences Pub Date : 2022-02-01 DOI:10.2478/fcds-2022-0001

Aakanshi Gupta, Deepanshu Sharma, Kritika Phulli

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引用次数: 0

Abstract

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.

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基于代码气味的Android应用漏洞检测的神经网络建模

摘要近年来，有很多软件设计方面的问题都是代码味引起的。Android应用程序开发遇到了更多与代码气味有关的安全问题，这些气味会导致软件中的漏洞。本研究的重点是安卓应用程序中由代码气味组成的漏洞检测。生成了一个基于多层感知器的ANN模型，用于检测软件漏洞，具有74.7%和79.6%的精度值，具有2个隐藏层。重点是1390个安卓类，并使用APRIORI算法对软件漏洞与安卓代码气味进行关联挖掘。生成的ANN模型研究结果表明，成员忽略方法（MIM）代码气味与Bean成员序列化（BMS）漏洞有关，该漏洞具有86%的置信度和0.48的支持值。还提出了一种算法，可以帮助开发人员在开发的早期阶段检测安卓应用程序的臭源代码中的软件漏洞。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Foundations of Computing and Decision Sciences COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-

CiteScore

2.20

自引率

9.10%

发文量

审稿时长

29 weeks