Physical Integrity Attack Detection of Surveillance Camera with Deep Learning based Video Frame Interpolation

Pan, Jonathan

doi:10.1109/iotais47347.2019.8980385

Cited by 8 publications

(3 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Related to smart cities, Pan et al [86] adopted ConvLSTM to propose a method for detecting threats (from cyber or physical spaces) against cyber-physical surveillance cameras. The technique uses a new video frame interpolation to detect video anomalies in spatial-temporal feeds.…”

Section: Physical Attack Detectionmentioning

confidence: 99%

Deep Neural Networks for Spatial-Temporal Cyber-Physical Systems: A Survey

et al. 2023

View full text Add to dashboard Cite

Cyber-physical systems (CPS) refer to systems that integrate communication, control, and computational elements into physical processes to facilitate the control of physical systems and effective monitoring. The systems are designed to interact with the physical world, monitor and control the physical processes while in operation, and generate data. Deep Neural Networks (DNN) comprise multiple layers of interconnected neurons that process input data to produce predictions. Spatial-temporal data represents the physical world and its evolution over time and space. The generated spatial-temporal data is used to make decisions and control the behavior of CPS. This paper systematically reviews the applications of DNNs, namely convolutional, recurrent, and graphs, in handling spatial-temporal data in CPS. An extensive literature survey is conducted to determine the areas in which DNNs have successfully captured spatial-temporal data in CPS and the emerging areas that require attention. The research proposes a three-dimensional framework that considers: CPS (transportation, manufacturing, and others), Target (spatial-temporal data processing, anomaly detection, predictive maintenance, resource allocation, real-time decisions, and multi-modal data fusion), and DNN schemes (CNNs, RNNs, and GNNs). Finally, research areas that need further investigation are identified, such as performance and security. Addressing data quality, strict performance assurance, reliability, safety, and security resilience challenges are the areas that are required for further research.

show abstract

Section: Physical Attack Detectionmentioning

confidence: 99%

Deep Neural Networks for Spatial-Temporal Cyber-Physical Systems: A Survey

et al. 2023

View full text Add to dashboard Cite

show abstract

“…If this communication is carried out after an interval of every few seconds, an attacker who is intercepting this communication may not be able to decode the PTZ data but can precisely find the interval after which communication is happening. The attacker can then launch an attack that matches with the communication interval, thus avoiding detection [34].…”

Section: Pan-tilt-zoom Attacksmentioning

confidence: 99%

Attacks and Preventive Measures on Video Surveillance Systems: A Review

Vennam

Pramod²,

Thippeswamy

et al. 2021

Applied Sciences

View full text Add to dashboard Cite

Video surveillance systems are widely deployed with large systems for use in strategic places such as home security, public transportation, banks, ATM centers, city centers, airports, and public roads, and play a vital role in protecting critical infrastructures. As various attacks are possible in these systems, identifying attacks and considering suitable security measures are essential. In this paper, we present a detailed review of existing and possible threats in video surveillance, CCTV, and IP-camera systems. This provides insight for the better identification of the security risks associated with the design and deployment of these systems and promotes further research in this emerging field. We also present countermeasures to prevent and protect the surveillance systems from various security attacks.

show abstract

“…In [ 77 ], the authors propose the use of an LSTM deep neural network to predict the next frame in a surveillance footage. When the predicted next frame does not match actual next frame, then an alert is raised.…”

Section: Countermeasures and Best Practicesmentioning

confidence: 99%

The Security of IP-Based Video Surveillance Systems

Kalbo

Mirsky

Shabtai

et al. 2020

Sensors

View full text Add to dashboard Cite

Over the last decade, video surveillance systems have become a part of the Internet of Things (IoT). These IP-based surveillance systems now protect industrial facilities, railways, gas stations, and even one’s own home. Unfortunately, like other IoT systems, there are inherent security risks which can lead to significant violations of a user’s privacy. In this review, we explore the attack surface of modern surveillance systems and enumerate the various ways they can be compromised with real examples. We also identify the threat agents, their attack goals, attack vectors, and the resulting consequences of successful attacks. Finally, we present current countermeasures and best practices and discuss the threat horizon. The purpose of this review is to provide researchers and engineers with a better understanding of a modern surveillance systems’ security, to harden existing systems and develop improved security solutions.

show abstract

Physical Integrity Attack Detection of Surveillance Camera with Deep Learning based Video Frame Interpolation

Cited by 8 publications

References 13 publications

Deep Neural Networks for Spatial-Temporal Cyber-Physical Systems: A Survey

Deep Neural Networks for Spatial-Temporal Cyber-Physical Systems: A Survey

Attacks and Preventive Measures on Video Surveillance Systems: A Review

The Security of IP-Based Video Surveillance Systems

Contact Info

Product

Resources

About