当前位置: X-MOL 学术Future Gener. Comput. Syst. › 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.)
DeepAMD: Detection and identification of Android malware using high-efficient Deep Artificial Neural Network
Future Generation Computer Systems ( IF 7.5 ) Pub Date : 2020-10-19 , DOI: 10.1016/j.future.2020.10.008
Syed Ibrahim Imtiaz , Saif ur Rehman , Abdul Rehman Javed , Zunera Jalil , Xuan Liu , Waleed S. Alnumay

Android smartphones are being utilized by a vast majority of users for everyday planning, data exchanges, correspondences, social interaction, business execution, bank transactions, and almost in each walk of everyday lives. With the expansion of human reliance on smartphone technology, cyberattacks against these devices have surged exponentially. Smartphone applications use permissions to utilize various functionalities of the smartphone that can be maneuvered to launch an attack or inject malware by hackers. Existing studies present various approaches to detect Android malware but lack early detection and identification. Accordingly, there is a dire need to craft an efficient mechanism for malicious applications’ detection before they exploit the data. In this paper, a novel approach DeepAMD to defend against real-world Android malware using deep Artificial Neural Network (ANN) has been adopted including an efficiency comparison of DeepAMD with conventional machine learning classifiers and state-of-the-art studies based on performance measures such as accuracy, recall, f-score, and precision. As per the experimental analysis, DeepAMD outperforms other approaches in detecting and identifying malware attacks on both Static as well as Dynamic layers. On the Static layer, DeepAMD achieves the highest accuracy of 93.4% for malware classification, 92.5% for malware category classification, and 90% for malware family classification. On the Dynamic layer, DeepAMD achieves the highest accuracy of 80.3% for malware category classification and 59% for malware family classification in comparison with the state-of-the-art techniques.



中文翻译:

DeepAMD:使用高效的深度人工神经网络检测和识别Android恶意软件

绝大多数用户正在使用Android智能手机进行日常计划,数据交换,通信,社交,业务执行,银行交易以及几乎每天的生活。随着人们对智能手机技术的依赖程度不断提高,针对这些设备的网络攻击已呈指数级增长。智能手机应用程序使用权限来利用智能手机的各种功能,这些功能可以操纵以发起攻击或由黑客注入恶意软件。现有研究提出了多种检测Android恶意软件的方法,但缺乏早期检测和识别的能力。因此,迫切需要制定一种有效的机制,以在恶意应用程序利用数据之前对其进行检测。本文采用一种新颖的方法DeepAMD使用深度人工神经网络(ANN)防御现实世界中的Android恶意软件的方法,包括将DeepAMD与传统机器学习分类器的效率进行比较,并基于性能指标(例如准确性,召回率和f)进行最新研究-得分和精确度。根据实验分析,DeepAMD在检测和识别静态和动态层上的恶意软件攻击方面均优于其他方法。在静态层,DeepAMD对恶意软件分类的最高准确度为93.4%,对于恶意软件类别分类的准确度为92.5%,对于恶意软件系列分类的准确度为90%。在动态层上,DeepAMD 与最新技术相比,恶意软件类别分类的最高准确度为80.3%,恶意软件家族分类的最高准确度为59%。

更新日期:2020-10-19
down
wechat
bug