Malware Detection Using Decision Tree Based SVM Classifier for IoT

作     者：Anwer Mustafa Hilal Siwar Ben Haj Hassine Souad Larabi-Marie-Sainte Nadhem Nemri Mohamed K.Nour Abdelwahed Motwakel Abu Sarwar Zamani Mesfer Al Duhayyim 

作者机构：Department of Computer and Self DevelopmentPreparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Computer ScienceCollege of Science and ArtsKing Khalid UniversityMahayil AsirSaudi Arabia Department of Computer ScienceCollege of Computer and Information SciencesPrince Sultan UniversitySaudi Arabia Department of Computer ScienceCollege of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Natural and Applied SciencesCollege of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第72卷第7期

页      面：713-726页

核心收录：

学科分类：0711[理学-系统科学] 1002[医学-临床医学] 1001[医学-基础医学(可授医学、理学学位)] 08[工学] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Deanship of Scientific Research, King Faisal University, DSR, KFU, (RGP 1/279/42) Prince Sultan University, PSU 

主　　题：Blockchain malware detection classification feature selection internet of medical things 

摘      要：The development in Information and Communication Technology has led to the evolution of new computing and communication environment.Technological revolution with Internet of Things(IoTs)has developed various applications in almost all domains from health care,education to entertainment with sensors and smart devices.One of the subsets of IoT is Internet of Medical things(IoMT)which connects medical devices,hardware and software applications through internet.IoMT enables secure wireless communication over the Internet to allow efficient analysis of medical data.With these smart advancements and exploitation of smart IoT devices in health care technology there increases threat and malware attacks during transmission of highly confidential medical data.This work proposes a scheme by integrating machine learning approach and block chain technology to detect malware during data transmission in IoMT.The proposed Machine Learning based Block Chain Technology malware detection scheme(MLBCT-Mdetect)is implemented in three steps namely:feature extraction,Classification and blockchain.Feature extraction is performed by calculating the weight of each feature and reduces the features with less weight.Support Vector Machine classifier is employed in the second step to classify the malware and benign nodes.Furthermore,third step uses blockchain to store details of the selected features which eventually improves the detection of malware with significant improvement in speed and accuracy.ML-BCT-Mdetect achieves higher accuracy with low false positive rate and higher True positive rate.