A method and device for detecting botnets in a cloud-computing infrastructure are provided. The method includes gathering data feeds over a predefined detection time window to produce a detection dataset, wherein the detection dataset includes at least security events and a first set of bot-labels related to the activity of each of at least one virtual machine in the cloud-computing infrastructure during the detection time window; generating, using the detection dataset, a features vector for each of a plurality of virtual machines in the cloud-computing infrastructure, wherein the features vector is based on idiosyncratic (iSync) scores related to botnet activity; transmitting each generated features vector to a supervised machine learning decision model to generate a label indicating if each of the plurality of virtual machines is a bot based on the respective features vector; and determining each virtual machine labeled as a bot as being part of a botnet.