Page 217 - Kaleidoscope Academic Conference Proceedings 2021

Session 3: Contributions to security

S3.1 Strengthen the security of cyberspace with device-independent quantum randomness
Ming-Han Li (CAS Quantum Network Co., Ltd., China); Qiang Zhang (Jinan Institute of Quantum

Technology, China)

With the advancement of the information age, the risk of information security keeps increasing.
Randomness is the core of network and information system security, and it is the basic support of
the entire network trust system. In this paper, we introduce the concept of Device-Independent
Quantum Random Number Generator (DIQRNG), which provides randomness with the highest
security level. To better integrate with Information and Communications Technology (ICT)
systems, we propose the principle and architectural framework of a randomness beacon based on
DIQRNG. It is a public service that can be applied in multiple scenarios, such as contract signing
and confidential disclosure. Its related application cases are also currently being studied in the ITU
QIT4N focus group.
S3.2 Abnormal activity recognition using deep learning in streaming video for indoor application

Dhananjay Kumar and Srinivasan Ramapriya Sailaja (Anna University, India)

Human activity recognition has emerged as a challenging research domain for video analysis. The
major issue for abnormal activity recognition in a streaming video is the presence of the large
spatio-temporal data along with the constraints of communication networks affecting the quality
of received data for analysis. In this paper, we propose a deep learning-based system to identify
abnormal human activities using a combination of Skeleton Activity Forecasting (SAF) and a Bi-
LSTM network. The generated skeleton joint points of a human subject are used for the pose
estimation. The skeleton tracking and regions of interest points are estimated on a streaming video
from an IP networked camera. The extracted interest points and their corresponding features are
optimized and used to classify them as normal, abnormal or suspicious actions. The proposed
system complies with Recommendation ITU-T H.627 "Signalling and protocols for a video
surveillance system" and has been experimented and evaluated over benchmarked data sets for the
recognition of human actions. The system performance attains a precision of 85.6% and an
accuracy of 97.2% in recognizing different actions.

S3.3 Research on security and privacy for IoT-domotics
Jinxue Cheng and Xiaoming Lu (China Mobile (Hang Zhou) Information Technology Co. Ltd,
China); Qin Qiu (China Mobile Communications Group Co. Ltd, China); Qing Lu (China Mobile
(Hang Zhou) Information Technology Co. Ltd, China)

This paper describes the basic characteristics of IoT-domotics, and proposes an IoT-domotics
reference model based on IoT-domotics entities, including service, IoT-domotics gateway, IoT-
domotics devices and physical entities, and networks. Then, based on the IoT-domotics reference
model, this paper analyzes the security and privacy risks of the IoT-domotics for different IoT-
domotics entities. Considering the characteristics of the IoT-domotics, this paper also proposes the
security control principles of the IoT-domotics, and gives the corresponding security and privacy
controls, aiming to provide technical support for IoT-domotics security and promote the security
application of IoT-domotics technology. Finally, this paper compares the supporting control
schemes implemented by some researchers to demonstrate the advantages and disadvantages of
existing IoT-domotics security control schemes.

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