Page 234 - Kaleidoscope Academic Conference Proceedings 2020

Session 8: Security in industrial applications
S8.1 PERT: Payload encoding representation from transformer for encrypted traffic classification
Hong Ye He, Zhi Guo Yang and Xiang Ning Chen, ZTE Corporation, China

Traffic identification becomes more important yet more challenging as related encryption
techniques are rapidly developing nowadays. In difference to recent deep learning methods that
apply image processing to solve such encrypted traffic problems, in this paper, we propose a
method named Payload Encoding Representation from Transformer (PERT) to perform automatic
traffic feature extraction using a state-of-the-art dynamic word embedding technique. Based on
this, we further provide a traffic classification framework in which unlabeled traffic is utilized to
pre-train an encoding network that learns the contextual distribution of traffic payload bytes. Then,
the downward classification reuses the pre-trained network to obtain an enhanced classification
result. By implementing experiments on a public encrypted traffic data set and our captured
Android HTTPS traffic, we prove the proposed method can achieve an obvious better effectiveness
than other compared baselines. To the best of our knowledge, this is the first time the encrypted
traffic classification with the dynamic word embedding alone with its pre-training strategy has
been addressed.

S8.2 Visual action recognition using deep learning in video surveillance systems
Dhananjay Kumar, Priyanka T and Aishwarya Murugesh, Anna University, India; Ved P. Kafle,
National Institute of Information and Communications Technology (NICT), Japan

The skeleton tracking technique allows the usage of the skeleton information of human-like objects
for action recognition. The major challenge in action recognition in a video surveillance system is
the large variability across and within subjects. In this paper, we propose a deep-learning-based
novel framework to recognize human actions using skeleton estimation. The main component of
the framework consists of pose estimation using a stacked hourglass network (HGN). The pose
estimation module provides the skeleton joint points of humans. Since the position of skeleton
varies according to the point of view, we apply transformations on the skeleton points to make it
invariable to rotation and position. The skeleton joint positions are identified using HGN-based
deep neural networks (HGN-DNN), and the feature extraction and classification is carried out to
obtain the action class. The skeleton action sequence is encoded using Fisher Vector before
classification. The proposed system complies with Recommendation ITU-T H.626.5 "Architecture
for intelligent visual surveillance systems", and has been evaluated over benchmarked human
action recognition data sets. The evaluation results show that the system performance achieves a
precision of 85% and the accuracy of 95.6% in recognizing actions like wave, punch, kick, etc.
The HGN-DNN model meets the requirements and service description specified in
Recommendation ITU-T F.743.

S8.3 STCCS: Segmented time controlled count-min sketch
Ismail Khram and Maha Shamseddine, Beirut Arab University, Lebanon; Wassim Itani, University
of Houston-Victoria, Texas, USA

IoT is a concept consisting of many components powered by different techniques and technologies.
However, due to computation restrictions, encryption algorithms had to be adapted, often at the
expense of lower data security levels and strength. To maintain data privacy between source and
sink we present in this paper a data sketching algorithm that utilizes bandwidth by providing a
summary of the data to the cloud. The input data stream goes through a hashing algorithm which
produces a hexadecimal representation of the data before going through the sketching algorithm.
At the algorithm the data is categorized and the corresponding hash cell value updated. Note is
also taken of the arrival time of the data considered anomalous to allow the manager to take
corrective action if it is deduced that the periodic appearance of the information is successive in
nature.

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