<![CDATA[Internet of things (IoT) technology is very popular these days around the world, with the development of IoT technology raises the impact of security threats and attacks on IoT devices. One of the most is the theft of data and information, one form of threat in IoT is malware. This research uses attacks in the form of bontet to detect attacks on the Internet of Things IoT) and uses Machine Learning to perform data detection of attacks on IoT devices. The method used in this research is Support Vector Mechine (SVM) by comparing three kernels namely Liner, polynominal and Radial Basis Function (RBF). This method is used to determine the level of accuracy in the detection process and compare between kernels. The results obtained are the accuracy value of 0.997 for the liner kernel, meaning that this kernel is able to separate the classes well, while the Polynomial kernel accuracy value of 0.993 is good in separating classes even though the value is smaller than the liner. Meanwhile, the RBF (Radial Basis Function) kernel has an accuracy of 1.0 (100%).]]>
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