Abstract
Botnet detection is a critical step in stopping the spread of botnets and preventing malicious activities. However, reliable detection is still a challenging task, due to a wide variety of botnets involving ever-increasing types of devices and attack vectors. Recent approaches employing machine learning (ML) showed improved performance than earlier ones, but these ML- based approaches still have significant limitations. For example, most ML approaches can not incorporate sequential pattern analysis techniques key to detect some classes of botnets. Another common shortcoming of ML-based approaches is the need to retrain neural networks in order to detect the evolving botnets; however, the training process is time-consuming and requires significant efforts to label the training data. For fast-evolving botnets, it might take too long to create sufficient training samples before the botnets have changed again. To address these challenges, we propose a novel botnet detection method, built u