An agent-administrator-based security mechanism for distributed sensors and drones for smart grid monitoring

Fitwi, Alem; Chen, Yu; Zhou, N.

doi:10.1117/12.2519006

Cited by 19 publications

(20 citation statements)

References 16 publications

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“…In another research [27], a new Distributed, Agent-based Secure Mechanism was proposed for IoD and Smart grid sensors monitoring (DASMIS) scheme. It was designed to run over a hybrid of peer-to-peer (P2P) and client-server (C/ S) network architecture with reduced protocol overheads for immediate and bandwidth-efficient communication.…”

Section: Related Workmentioning

confidence: 99%

A Novel Forensic Readiness Framework Applicable to the Drone Forensics Field

Alotaibi

Al-Dhaqm

Al‐Otaibi

2022

Computational Intelligence and Neuroscience

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The Drone Forensics (DRFs) field is a branch of digital forensics, which involves the identification, capture, preservation, reconstruction, analysis, and documentation of drone incidents. Several models have been proposed in the literature for the DRF field, which generally discusses DRF from a reactive forensic perspective; however, the proactive forensic perspective is missing. Therefore, this paper proposes a novel forensic readiness framework called Drone Forensics Readiness Framework (DRFRF) using the design science method. It consists of two stages: (i) proactive forensic stage and (ii) reactive forensic stage. It considers centralized logging of all events of all the applicants within the drone device in preparation for an examination. It will speed up gathering data when an investigation is needed, permitting the forensic investigators to handle the examination and analysis directly. Additionally, digital forensics analysts can increase the possible use of digital evidence while decreasing the charge of performing forensic readiness. Thus, both the time and cost required to perform forensic readiness could be saved. The completeness, logicalness, and usefulness of DRFRF were compared to those of other models already existing in the DRF domain. The results showed the novelty and efficiency of DRFRF and its applicability to the situations before and after drone incidents.

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Section: Related Workmentioning

confidence: 99%

A Novel Forensic Readiness Framework Applicable to the Drone Forensics Field

Alotaibi

Al-Dhaqm

Al‐Otaibi

2022

Computational Intelligence and Neuroscience

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“…A survey [18] shows the attempts made to make use of some of the aforementioned technologies; they lack clear methods for precisely determining the distance between people in a crowd, though. Besides, previous works that focus on selective surveillance [6,9] and crowd surveillance using drones [13,[19][20][21] could be further developed to be employed for social distance determination, monitoring, and alerting. The machine learning technology has a wide range of applications [22,23]; as a result, it can be adopted to design and build models useful for crowd control, like monitoring social distancing.…”

Section: Social Distancingmentioning

confidence: 99%

Estimating Interpersonal Distance and Crowd Density with a Single-Edge Camera

et al. 2021

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For public safety and physical security, currently more than a billion closed-circuit television (CCTV) cameras are in use around the world. Proliferation of artificial intelligence (AI) and machine/deep learning (M/DL) technologies have gained significant applications including crowd surveillance. The state-of-the-art distance and area estimation algorithms either need multiple cameras or a reference object as a ground truth. It is an open question to obtain an estimation using a single camera without a scale reference. In this paper, we propose a novel solution called E-SEC, which estimates interpersonal distance between a pair of dynamic human objects, area occupied by a dynamic crowd, and density using a single edge camera. The E-SEC framework comprises edge CCTV cameras responsible for capturing a crowd on video frames leveraging a customized YOLOv3 model for human detection. E-SEC contributes an interpersonal distance estimation algorithm vital for monitoring the social distancing of a crowd, and an area estimation algorithm for dynamically determining an area occupied by a crowd with changing size and position. A unified output module generates the crowd size, interpersonal distances, social distancing violations, area, and density per every frame. Experimental results validate the accuracy and efficiency of E-SEC with a range of different video datasets.

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“…Today, more than a billion of CCTV cameras are pervasively deployed around the world in urban areas by governments, companies, and individuals with the main goals of ensuring physical security and public safety. In places like Beijing, London, and New York, an individual is estimated to be caught on CCTV cameras hundreds of times a day [9], [12], [26], [30]. Consequently, a tremendous amount of information about individuals is garnered without their knowledge and consent.…”

Section: A Video Surveillance Systems and Privacymentioning

confidence: 99%

“…There are a myriad of reports that substantiate the fear of abuse of CCTV cameras. The German Chancellor, Angela Merkel, was spied while at her apartment by abusing a museum security CCTV camera [2], [9], [10]. Quite recently, on March 9, 2021, a group of hackers claimed to have hacked the security CCTV camera data from Verkada and gained access to the live feeds as well as central storage of 150,000 surveillance CCTV cameras deployed in a number of companies and hospitals, including Halifax Health [7].…”

Section: Introductionmentioning

confidence: 99%

Secure and Privacy-Preserving Stored Surveillance Video Sharing atop Permissioned Blockchain

Fitwi¹,

Chen²

2021

Preprint

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At present, more than a billion closed circuit television (CCTV) cameras are watching the world. These cameras garner a lot of visual information that is often processed and stored in remote and centralized cloud servers. Multiple occasions have revealed that this traditional approach is plagued with security and privacy breaches. The breaches could be interception of raw videos while in transit to distant surveillance analytics centers (SAC), infiltration to cameras and network video records (NVR), or abuse of cameras and stored videos. Hence, the traditional video surveillance system (VSS) cannot guarantee the protection of the privacy of individuals caught on CCTV cameras. Wherefore, this paper proposes a Secure and Privacy-preserving Stored surveillance videos sharing (SePriS) mechanism for authorized users/nodes based on smart contracts, blockchain (BC) and the enciphering of video frames using DAB, a mechanism developed based on discrete cosine transform (DCT), advanced encryption standard (AES), and block shuffling (BS) algorithm. The BC-based solution creates an environment auspicious for creating a decentralized, reliable SACs and storage sites with secure and privacy-aware sharing of stored surveillance videos across SAC nodes and by law enforcers, police departments, and courts securely connected to the SAC nodes. The experiments and analyses validate that the proposed BCbased SePriS solution achieves the design purpose.

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An agent-administrator-based security mechanism for distributed sensors and drones for smart grid monitoring

Cited by 19 publications

References 16 publications

A Novel Forensic Readiness Framework Applicable to the Drone Forensics Field

A Novel Forensic Readiness Framework Applicable to the Drone Forensics Field

Estimating Interpersonal Distance and Crowd Density with a Single-Edge Camera

Secure and Privacy-Preserving Stored Surveillance Video Sharing atop Permissioned Blockchain

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