Department of Computer Science, College of Science and Humanities Al Dawadmi, Shaqra University, Shaqra 11961, Saudi Arabia.
Sensors (Basel). 2021 Oct 27;21(21):7119. doi: 10.3390/s21217119.
Detection of unknown malware and its variants remains both an operational and a research challenge in the Internet of Things (IoT). The Internet of Medical Things (IoMT) is a particular type of IoT network which deals with communication through smart healthcare (medical) devices. One of the prevailing problems currently facing IoMT solutions is security and privacy vulnerability. Previous malware detection methods have failed to provide security and privacy. In order to overcome this issue, the current study introduces a novel technique called biserial correlative Miyaguchi-Preneel blockchain-based Ruzicka-index deep multilayer perceptive learning (BCMPB-RIDMPL). The present research aims to improve the accuracy of malware detection and minimizes time consumption. The current study combines the advantages of machine-learning techniques and blockchain technology. The BCMPB-RIDMPL technique consists of one input layer, three hidden layers, and one output layer to detect the malware. The input layer receives the number of applications and malware features as input. After that, the malware features are sent to the hidden layer 1, in which feature selection is carried out using point biserial correlation, which reduces the time required to detect the malware. Then, the selected features and applications are sent to the hidden layer 2. In that layer, Miyaguchi-Preneel cryptographic hash-based blockchain is applied to generate the hash value for each selected feature. The generated hash values are stored in the blockchain, after which the classification is performed in the third hidden layer. The BCMPB-RIDMPL technique uses the Ruzicka index to verify the hash values of the training and testing malware features. If the hash is valid, then the application is classified as malware, otherwise it is classified as benign. This method improves the accuracy of malware detection. Experiments have been carried out on factors such as malware detection accuracy, Matthews's correlation coefficient, and malware detection time with respect to a number of applications. The observed quantitative results show that our proposed BCMPB-RIDMPL method provides superior performance compared with state-of-the-art methods.
在物联网（IoT）中，检测未知恶意软件及其变体仍然是一个运营和研究挑战。医疗物联网（IoMT）是一种特殊类型的物联网网络，通过智能医疗（医疗）设备进行通信。目前，IoMT 解决方案面临的主要问题之一是安全和隐私漏洞。以前的恶意软件检测方法未能提供安全性和隐私性。为了解决这个问题，本研究引入了一种称为双序列关联 Miyaguchi-Preneel 区块链的 Ruzicka 索引深度多层感知学习（BCMPB-RIDMPL）的新技术。本研究旨在提高恶意软件检测的准确性并最小化时间消耗。本研究结合了机器学习技术和区块链技术的优势。BCMPB-RIDMPL 技术由一个输入层、三个隐藏层和一个输出层组成，用于检测恶意软件。输入层接收应用程序数量和恶意软件特征作为输入。然后，将恶意软件特征发送到隐藏层 1，在该层中使用点双序列相关进行特征选择，从而减少检测恶意软件所需的时间。然后，将选择的特征和应用程序发送到隐藏层 2。在该层中，应用基于 Miyaguchi-Preneel 密码哈希的区块链生成每个选择特征的哈希值。生成的哈希值存储在区块链中，然后在第三个隐藏层中进行分类。BCMPB-RIDMPL 技术使用 Ruzicka 指数验证训练和测试恶意软件特征的哈希值。如果哈希值有效，则将应用程序分类为恶意软件，否则将其分类为良性。这种方法提高了恶意软件检测的准确性。已经针对多种应用程序的恶意软件检测准确性、马修斯相关系数和恶意软件检测时间等因素进行了实验。观察到的定量结果表明，与最先进的方法相比，我们提出的 BCMPB-RIDMPL 方法提供了卓越的性能。
