Kalphana K R, Aanjankumar S, Surya M, Ramadevi M S, Ramela K R, Anitha T, Nagaprasad N, Krishnaraj Ramaswamy
Department of Agricultural Engineering, Mahendra Engineering College, Namakkal, Tamil Nadu, 637503, India.
School of Computing Science and Engineering (SCOPE), VIT Bhopal University, Bhopal-Indore Highway, Kothrikalan, Sehore, Madhya Pradesh, 466114, India.
Sci Rep. 2024 Sep 27;14(1):22351. doi: 10.1038/s41598-024-70544-x.
In recent times, the number of malware on Android mobile phones has been growing, and a new kind of malware is Android ransomware. This research aims to address the emerging concerns about Android ransomware in the mobile sector. Previous studies highlight that the number of new Android ransomware is increasing annually, which poses a huge threat to the privacy of mobile phone users for sensitive data. Various existing techniques are active to detect ransomware and secure the data in the mobile cloud. However, these approaches lack accuracy and detection performance with insecure storage. To resolve this and enhance the security level, the proposed model is presented. This manuscript provides both recognition algorithms based on the deep learning model and secured storage of detected data in the cloud with a secret key to safeguard sensitive user information using the hybrid cryptographic model. Initially, the input APK files and data are preprocessed to extract features. The collection of optimal features is carried out using the Squirrel search optimization process. After that, the Deep Learning-based model, adaptive deep saliency The AlexNet classifier is presented to detect and classify data as malicious or normal. The detected data, which is not malicious, is stored on a cloud server. For secured storage of data in the cloud, a hybrid cryptographic model such as hybrid homomorphic Elliptic Curve Cryptography and Blowfish is employed, which includes key computation and key generation processes. The cryptographic scheme includes encryption and decryption of data, after which the application response is found to attain a decrypted result upon user request. The performance is carried out for both the Deep Learning-based model and the hybrid cryptography-based security model, and the results obtained are 99.89% accuracy in detecting malware compared with traditional models. The effectiveness of the proposed system over other models such as GNN is 94.76%, CNN is 95.76%, and Random Forest is 96%.
近年来，安卓手机上的恶意软件数量一直在增长，一种新型恶意软件是安卓勒索软件。本研究旨在解决移动领域中对安卓勒索软件新出现的担忧。先前的研究强调，新的安卓勒索软件数量每年都在增加，这对手机用户敏感数据的隐私构成了巨大威胁。各种现有技术积极用于检测勒索软件并保护移动云中的数据。然而，这些方法在存储不安全的情况下缺乏准确性和检测性能。为了解决这个问题并提高安全级别，提出了所建议的模型。本手稿既提供了基于深度学习模型的识别算法，又使用混合加密模型将检测到的数据安全存储在云中，并使用密钥来保护用户敏感信息。最初，对输入的APK文件和数据进行预处理以提取特征。使用松鼠搜索优化过程进行最优特征的收集。之后，提出基于深度学习的模型——自适应深度显著性AlexNet分类器，以检测和分类数据是恶意还是正常。检测到的非恶意数据存储在云服务器上。为了在云中安全存储数据，采用了混合同态椭圆曲线密码术和 Blowfish 等混合加密模型，其中包括密钥计算和密钥生成过程。加密方案包括数据的加密和解密，之后在用户请求时找到应用响应以获得解密结果。对基于深度学习的模型和基于混合密码学的安全模型都进行了性能测试，与传统模型相比，检测恶意软件的准确率达到了99.89%。所建议的系统相对于其他模型，如GNN的有效性为94.76%，CNN为95.76%，随机森林为96%。
