Biblio

Internet service providers (ISP) rely on network traffic classifiers to provide secure and reliable connectivity for their users. Encrypted traffic introduces a challenge as attacks are no longer viable using classic Deep Packet Inspection (DPI) techniques. Distinguishing encrypted from non-encrypted traffic is the first step in addressing this challenge. Several attempts have been conducted to identify encrypted traffic. In this work, we compare the detection performance of DPI, traffic pattern, and randomness tests to identify encrypted traffic in different levels of granularity. In an experimental study, we evaluate these candidates and show that a traffic pattern-based classifier outperforms others for encryption detection.

Convolutional Neural Networks (CNN) have shown astonishing results in the field of computer vision. This paper proposes a foreground segmentation algorithm based on CNN to tackle the practical challenges in the video surveillance system such as illumination changes, dynamic backgrounds, camouflage, and static foreground object, etc. The network is trained using the input of image sequences with respective ground-truth. The algorithm employs a CNN called VGG-16 to extract features from the input. The extracted feature maps are upsampled using a bilinear interpolation. The upsampled feature mask is passed through a sigmoid function and threshold to get the foreground mask. Binary cross entropy is used as the error function to compare the constructed foreground mask with the ground truth. The proposed algorithm was tested on two standard datasets and showed superior performance as compared to the top-ranked foreground segmentation methods.