Z. Berkay Celik

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—Deep learning takes advantage of large datasets and computationally efficient training algorithms to outperform other approaches at various machine learning tasks. However, imperfections in the training phase of deep neural networks make them vulnerable to adversarial samples: inputs crafted by adversaries with the intent of causing deep neural networks to(More)
We consider the problem of using flow-level data for detection of botnet command and control (C&C) activity. We find that current approaches do not consider timing-based calibration of the C&C traffic traces prior to using this traffic to salt a background traffic trace. Thus, timing-based features of the C&C traffic may be artificially distinctive,(More)
Advances in deep learning have led to the broad adoption of Deep Neural Networks (DNNs) to a range of important machine learning problems, e.g., guiding autonomous vehicles, speech recognition, mal-ware detection. Yet, machine learning models, including DNNs, were shown to be vulnerable to adversarial samples—subtly (and often humanly indistinguishably)(More)
Modern detection systems use sensor outputs available in the deployment environment to probabilistically identify attacks. These systems are trained on past or synthetic feature vectors to create a model of anomalous or normal behavior. Thereafter, run-time collected sensor outputs are compared to the model to identify attacks (or the lack of attack). While(More)
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