Malware Detection Using Decision Tree Based SVM Classifier for IoT

【Author】 Hilal, Anwer Mustafa; Hassine, Siwar Ben Haj; Larabi-Marie-Sainte, Souad; Nemri, Nadhem; Nour, Mohamed K.; Motwakel, Abdelwahed; Zamani, Abu Sarwar; Al Duhayyim, Mesfer

【Source】CMC-COMPUTERS MATERIALS & CONTINUA

【影响因子】3.860

【Abstract】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.

【Keywords】Blockchain; malware detection; classification; feature selection; internet of medical things

【发表时间】2022

【收录时间】2022-03-22

【文献类型】期刊

【主题类别】

区块链技术-区块链数据分析-