当前位置: X-MOL 学术Microprocess. Microsyst. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Detecting android malware using an improved filter based technique in embedded software
Microprocessors and Microsystems ( IF 2.6 ) Pub Date : 2020-05-11 , DOI: 10.1016/j.micpro.2020.103115
Varna Priya D , Visalakshi P

The technological advancements have led to evolution of sophisticated devices called smartphones. By providing extensive capabilities, they are becoming more and more popular. The Android based smartphones are preferred furthermore, due to their open-source nature. This has also led to the development of large number of malwares targeting these smartphones. Thus to protect the devices, some countermeasures are needed. Machine learning methods have gained popularity in detection of malware. This work proposes a malware detection technique in Android devices based on static analysis carried out using the Manifest files extracted from the apk files. The feature selection is performed using the proposed KNN based Relief algorithm and detection of malware is done using the proposed optimized SVM algorithm. The proposed method achieves a True Positive Rate greater than 0.70 and much reduced False Positive Rate values were obtained, with the values of False Positive Rate being very close to zero. The proposed KNN based feature selection is found to select better features in comparison with some popular existing feature selection techniques. The proposed optimized SVM technique achieves a performance that is on par with the performance of Neural Networks.



中文翻译:

使用嵌入式软件中基于过滤器的改进技术检测android恶意软件

技术进步导致称为智能手机的复杂设备的发展。通过提供广泛的功能,它们变得越来越受欢迎。此外,基于Android的智能手机还具有开源特性,因此是首选。这也导致针对这些智能手机的大量恶意软件的开发。因此,为了保护设备,需要采取一些对策。机器学习方法已在检测恶意软件中获得普及。这项工作基于使用从apk文件中提取的清单文件进行的静态分析,提出了Android设备中的恶意软件检测技术。使用建议的基于KNN的救济算法执行特征选择,并使用建议的优化SVM算法完成恶意软件检测。所提出的方法实现了大于0.70的真实肯定率,并且获得了大大降低的错误肯定率值,并且错误肯定率的值非常接近零。发现与一些流行的现有特征选择技术相比,提出的基于KNN的特征选择可以选择更好的特征。提出的优化SVM技术可实现与神经网络性能相当的性能。

更新日期:2020-05-11
down
wechat
bug