Device fingerprinting using deep convolutional neural networks

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Abstract: Device fingerprinting is a problem of identifying a network device using network traffic data to secure against cyber-attacks. Automated device classification from a large set of network traffic features space is challenging for the devices connected in cyberspace. In this work, the idea is to define a device-specific unique fingerprint by analyzing solely the inter-arrival time of packets as a feature to identify a device. Neural networks are the universal function approximation that learns abstract, high-level, nonlinear representations of training data. A deep convolution neural network is used on images of inter-arrival time signatures to fingerprint 58 non-IoT devices of five to eleven types. We compared the ResNet-50 layer and basic CNN-5 Layer architectures to evaluate the performance. We observed that device-type identification models perform better than device identification. We also found that when deep learning models are attacked over device signature, the models identify the change in signature and classify the device in the wrong class, thereby improving the classification performance of the models. The performance of the models to detect the attacks is significantly different from each other, though both models indicate the system under attack.