Abstract
With the technological development and means of communication, the Internet of Things (IoT) has become an essential role in providing many services in daily life through millions of heterogeneous but interconnected devices and nodes. This development is opening to many security and privacy challenges that can cause complete network breakdown, bypassed access control or the loss of critical data. This paper attempts to provide a preliminary analysis for malware detection within data generated by IoT-based devices and services in the form of operational codes (Opcode) sequences. Three machine learning algorithms are evaluated and compared for accuracy, precision, recall and F-measure. The results showed that the Random Forest (RF) achieved the best accuracy of 98%, followed by SVM and k-NN, both with 91%. The results are further analyzed based on the Receiver Operating Characteristic (ROC) curve and Precision-Recall curve to further illustrate the difference in performance of all three algorithms when dealing with IoT data.
Highlights
Today, the Internet of Things (IoT) has offered many services through interconnection of huge number of sensor devices, embedded systems or services (Mosenia and Jha, 2016; Azmoodeh et al, 2018)
The purpose of the experiments is to compare the performance of three algorithms, which are k-Nearest Neighbor (k-NN), Support Vector Machines (SVM) and Random Forest (RF)
This paper presented a preliminary analysis of malware detection models within the scope of Internet-ofThings (IoT) applications
Summary
The Internet of Things (IoT) has offered many services through interconnection of huge number of sensor devices, embedded systems or services (Mosenia and Jha, 2016; Azmoodeh et al, 2018). The massive expansion of IoT applications has resulted in surge of data, opening to many security and privacy challenges such as the malware attacks (Tankard, 2015; D’Orazio et al, 2016; Watson and Dehghantanha, 2016). The main issue with malware detection lies in the ineffective methods used for signing and monitoring the suspected code for known security changes. This has led to many investigation on formulating new methods and techniques that can overcome different attack vectors (Burguera et al, 2011)
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