Publications | Intelligent and Sustainable Edge Computing (I-SEC) Lab

2026

MilliSARImageNet: A 2D High-Resolution Millimeter-Wave SAR Image Dataset

Lhamo Dorje, Nihal Poredi, J. Madden, and 3 more authors

In 2026 IEEE Consumer Communications & Networking Conference (CCNC) 2026
Is ENF a Good Physical Fingerprint?

Mohsen Hatami, Lhamo Dorje, Xiaohua Li, and 1 more author

In 2026 IEEE Consumer Communications & Networking Conference (CCNC) 2026
BRAIN: Blockchain Reputation Architecture for Interest-Flooding Neutralization in Named Data Networking for Internet of Things

Simeon Ogunbunmi, Shantanu Pal, Erik Blasch, and 2 more authors

In 2026 IEEE International Conference on Pervasive Computing and Communications (PerCom) 2026
CerVaLens: On-Device Multimodal AI-Generated Content Detection for Mobile Authentication

Van Tuan Pham, Hozefa Lakadawala, Amy Delgado, and 3 more authors

In SPIE Defense + Commercial Sensing 2026 2026
Echo Chamber Index: A GNN-Based Metric for Detecting Echo Chambers in Multi-Agent AI Networks

Van Tuan Pham, Qian Qu, Joon Suk Lee, and 2 more authors

In SPIE Defense + Commercial Sensing 2026 2026
Physically Anchored Trust for MCP-Orchestrated AIoT Swarms

Van Tuan Pham, Hozefa Lakadawala, Yu Chen, and 1 more author

In 2026 International Conference on Information Fusion (FUSION) 2026
Securing AI Agents in Cyber-Physical Systems: A Survey of Environmental Interactions, Deepfake Threats, and Defenses

Mohsen Hatami, Van Tuan Pham, Hozefa Lakadawala, and 1 more author

arXiv preprint arXiv:2601.20184 2026

2025

ANCHOR-Grid: Authenticating Smart Grid Digital Twins Using Real-World Anchors

Mohsen Hatami, Qian Qu, Yu Chen, and 3 more authors

Sensors 2025
Securing Smart Grid Digital Twins via Real-World ENF Anchors Against Deepfake Attacks

Mohsen Hatami, Qian Qu, Deeraj Nagothu, and 4 more authors

In 2025 IEEE International Conference on Pervasive Computing and Communications Workshops (SPT-IoT) 2025
Electric Network Frequency as Environmental Fingerprint for Metaverse Security: A Comprehensive Survey

Mohsen Hatami, Lhamo Dorje, Xiaohua Li, and 1 more author

Computers 2025
Future Outdoor Safety Monitoring: Integrating Human Activity Recognition with the Internet of Physical-Virtual Things

Yu Chen, Jia Li, Erik Blasch, and 1 more author

Applied Sciences 2025
SAVE: Securing Avatars in Virtual Healthcare Through Environmental Fingerprinting for Elder Safety Monitoring

Qian Qu, Yu Chen, and Erik Blasch

Future Internet 2025
Secure Avatars via Environmental Fingerprints for Virtual Health Monitoring Services

Qian Qu, Han Sun, Jenny Chen, and 2 more authors

In ICC 2025 - IEEE International Conference on Communications 2025
ANCHOR: Authenticating Avatars and Virtual Objects via Anchors in the Real World

Qian Qu, and Yu Chen

In Disruptive Technologies in Information Sciences IX 2025
Topological Frequency Feature Analysis for AI-Generated Image Authentication

Nihal Poredi, Paul T. Schrader, G. Makenzie Cosgrove, and 2 more authors

In Disruptive Technologies in Information Sciences IX 2025
Echoes Amplified: A Study of AI-Generated Content and Digital Echo Chambers

Ashley Kearney, Nihal Poredi, Joseph A. Shelton, and 4 more authors

In Disruptive Technologies in Information Sciences IX 2025
LiMAR: Lightweight Microchained Architecture for UAV Reputation Management

Simeon Ogunbunmi, Ronghua Xu, and Yu Chen

In 2025 IEEE International Conference on Blockchain and IoT (BIoT) 2025
Immersive Future: A Case Study of Metaverse in Preparing Students for Career Readiness

Leyli Nouraei Yeganeh, Amber Simpson, Nicole Fenty, and 4 more authors

In 2025 IEEE International Conference on Metaverse Computing, Networking, and Applications (MetaCom) 2025
The Future of Education: A Multi-Layered Metaverse Classroom Model for Immersive and Inclusive Learning

Leyli Nouraei Yeganeh, Nicole Fenty, Yu Chen, and 2 more authors

Future Internet 2025
STREAM: A Semantic Transformation and Real-Time Educational Adaptation Multimodal Framework in Personalized Virtual Classrooms

Leyli Nouraei Yeganeh, Yu Chen, Nicole Fenty, and 2 more authors

Future Internet 2025

2024

A Survey of the Real-Time Metaverse: Challenges and Opportunities

Mohsen Hatami, Qian Qu, Yu Chen, and 3 more authors

Future Internet 2024
The Microverse: A Task-Oriented Edge-Scale Metaverse

Qian Qu, Mohsen Hatami, Ronghua Xu, and 6 more authors

Future Internet 2024
A Rapid Response System for Elderly Safety Monitoring Using Progressive Hierarchical Action Recognition

Han Sun, and Yu Chen

IEEE Transactions on Neural Systems and Rehabilitation Engineering 2024
Detecting Reentrancy Vulnerability in Smart Contracts Using Graph Convolution Networks

Hozefa Lakadawala, Komla Dzigbede, and Yu Chen

In 2024 IEEE 21st Consumer Communications & Networking Conference (CCNC) 2024
Evasive Camouflage Attack of RF Sensing and Imaging Systems

Lhamo Dorje, Xiaohua Li, and Soamar Homsi

In ICC 2024 - IEEE International Conference on Communications 2024

2023

ECOM: Epoch Randomness-Based Consensus Committee Configuration for IoT Blockchains

Ronghua Xu, Deeraj Nagothu, and Yu Chen

2023
A Virtual Community Healthcare Framework in Metaverse Enabled by Digital Twins

Qian Qu, Han Sun, and Yu Chen

In International Congress on Communications, Networking, and Information Systems 2023
RODAD: Resilience Oriented Decentralized Anomaly Detection for Urban Air Mobility Networks

Sixiao Wei, Hui Huang, Genshe Chen, and 4 more authors

In 2023 Integrated Communication, Navigation and Surveillance Conference (ICNS) 2023
Light-Weight Real-Time Senior Safety Monitoring using Digital Twins

Qian Qu, Han Sun, and Yu Chen

In Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation 2023
AUSOME: authenticating social media images using frequency analysis

Nihal Poredi, Deearj Nagothu, and Yu Chen

In Disruptive Technologies in Information Sciences VII 2023

2022

DeFakePro: Decentralized Deepfake Attacks Detection Using ENF Authentication

Deeraj Nagothu, Ronghua Xu, Yu Chen, and 2 more authors

IT Professional Sep 2022

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Advancements in generative models, such as deepfake, allow users to imitate a targeted person and manipulate online interactions. It has been recognized that disinformation may cause disturbance in society and ruin the foundation of trust. This article presents DeFakePro, a decentralized consensus mechanism-based deepfake detection technique in online video conferencing tools. Leveraging electrical network frequency (ENF), an environmental fingerprint embedded in digital media recording affords a consensus mechanism design called proof-of-ENF (PoENF) algorithm. The similarity in ENF signal fluctuations is utilized in the PoENF algorithm to authenticate the media broadcasted in conferencing tools. By utilizing the video conferencing setup with malicious participants to broadcast deepfake video recordings to other participants, the DeFakePro system verifies the authenticity of the incoming media in both audio and video channels.
Deterring Deepfake Attacks with an Electrical Network Frequency Fingerprints Approach

Deeraj Nagothu, Ronghua Xu, Yu Chen, and 2 more authors

Future Internet May 2022

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With the fast development of Fifth-/Sixth-Generation (5G/6G) communications and the Internet of Video Things (IoVT), a broad range of mega-scale data applications emerge (e.g., all-weather all-time video). These network-based applications highly depend on reliable, secure, and real-time audio and/or video streams (AVSs), which consequently become a target for attackers. While modern Artificial Intelligence (AI) technology is integrated with many multimedia applications to help enhance its applications, the development of General Adversarial Networks (GANs) also leads to deepfake attacks that enable manipulation of audio or video streams to mimic any targeted person. Deepfake attacks are highly disturbing and can mislead the public, raising further challenges in policy, technology, social, and legal aspects. Instead of engaging in an endless AI arms race “fighting fire with fire”, where new Deep Learning (DL) algorithms keep making fake AVS more realistic, this paper proposes a novel approach that tackles the challenging problem of detecting deepfaked AVS data leveraging Electrical Network Frequency (ENF) signals embedded in the AVS data as a fingerprint. Under low Signal-to-Noise Ratio (SNR) conditions, Short-Time Fourier Transform (STFT) and Multiple Signal Classification (MUSIC) spectrum estimation techniques are investigated to detect the Instantaneous Frequency (IF) of interest. For reliable authentication, we enhanced the ENF signal embedded through an artificial power source in a noisy environment using the spectral combination technique and a Robust Filtering Algorithm (RFA). The proposed signal estimation workflow was deployed on a continuous audio/video input for resilience against frame manipulation attacks. A Singular Spectrum Analysis (SSA) approach was selected to minimize the false positive rate of signal correlations. Extensive experimental analysis for a reliable ENF edge-based estimation in deepfaked multimedia recordings is provided to facilitate the need for distinguishing artificially altered media content.
A Distributed Crawler for IoVT-based Public Safety Surveillance Exploring the Spatio-Temporal Correlation

Deeraj Nagothu, Daniel Dimock, Adrian Kulesza, and 2 more authors

In Sensors and Systems for Space Applications XV Jun 2022

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Modern infrastructure development has led to a rise in deployed surveillance cameras to monitor remote locations and widespread infrastructures. In today’s networked surveillance environment, however, human operators are often overwhelmed with the huge amount of visual feeds, which causes poor judgment and delayed response to emergencies. This paper proposes a distributed crawler scheme (DiCrawler) for smart surveillance systems deployed on Internet of Video Things (IoVT). The IoVT camera nodes monitor continuous video input, track the object of interest while preserving privacy, and relay correlative information to targeted nodes for constant monitoring. Each IoVT node monitors the space inside its field of view (FoV) and notifies the neighboring nodes about the objects leaving the FoV and heading in their directions. A smart communication algorithm among IoVT nodes is designed to prevent network bandwidth bottlenecks and preserve computational power. The DiCrawler system can corroborate with human operators and assist with decision-making by raising alarms in case of suspicious behavior. The IoVT network is completely decentralized, using only peer-to-peer (P2P) communication. DiCrawler does not rely on a central server for any computations, preventing a potential bottleneck if hundreds of cameras were connected and constantly uploading data to a server. Each module is also in a compact form factor, making it viable to be mounted on traditional security surveillance cameras. Extensive experimental study on a proof-of-concept prototype validated the effectiveness of the DiCrawler design.
Evolution of Attacks on Intelligent Surveillance Systems and Effective Detection Techniques

Deeraj Nagothu, Nihal Poredi, Yu Chen, and 3 more authors

Aug 2022

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Intelligent surveillance systems play an essential role in modern smart cities to enable situational awareness. As part of the critical infrastructure, surveillance systems are often targeted by attackers aiming to compromise the security and safety of smart cities. Manipulating the audio or video channels could create a false perception of captured events and bypass detection. This chapter presents an overview of the attack vectors designed to compromise intelligent surveillance systems and discusses existing detection techniques. With advanced machine learning (ML) models and computing resources, both attack vectors and detection techniques have evolved to use ML-based techniques more effectively, resulting in non-equilibrium dynamics. The current detection techniques vary from training a neural network to detect forgery artifacts to use the intrinsic and extrinsic environmental fingerprints for any manipulations. Therefore, studying the effectiveness of different detection techniques and their reliability against the defined attack vectors is a priority to secure the system and create a plan of action against potential threats.
Real-Time Elderly Monitoring for Senior Safety by Lightweight Human Action Recognition

Han Sun, and Yu Chen

In 2022 IEEE 16th International Symposium on Medical Information and Communication Technology (ISMICT) Aug 2022

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With an increasing number of elders living alone, care-giving from a distance becomes a compelling need, particularly for safety. Real-time monitoring and action recognition are essential to raise an alert timely when abnormal behaviors or unusual activities occur. While wearable sensors are widely recognized as a promising solution, highly depending on user’s ability and willingness makes them inefficient. In contrast, video streams collected through non-contact optical cameras provide richer information and release the burden on elders. In this paper, leveraging the Independently-Recurrent neural Network (IndRNN) we propose a novel Real-time Elderly Monitoring for senior Safety (REMS) based on lightweight human action recognition (HAR) technology. Using captured skeleton images, the REMS scheme is able to recognize abnormal behaviors or actions and preserve the user’s privacy. To achieve a high accuracy, the HAR module is trained and fine-tuned using multiple databases. An extensive experimental study verified that REMS system preforms action recognition accurately and timely. REMS meets the design goals as a privacy-preserving elderly safety monitoring system and possesses the potential to be adopted in various smart monitoring systems.
SAUSA: Securing Access, Usage, and Storage of 3D Point CloudData by a Blockchain-Based Authentication Network

Ronghua Xu, Yu Chen, Genshe Chen, and 1 more author

Future Internet Aug 2022
uDFL: A Secure Microchained Decentralized Federated Learning Fabric atop IoT Networks

Ronghua Xu, and Yu Chen

IEEE Transactions on Network and Service Management Aug 2022
Fairledger: a Fair Proof-of-Sequential-Work based Lightweight Distributed Ledger for IoT Networks

Ronghua Xu, and Yu Chen

In 2022 IEEE International Conference on Blockchain (Blockchain) Aug 2022
A Secure Dynamic Edge Resource Federation Architecture for Cross-Domain IoT Systems

Ronghua Xu, Yu Chen, Xiaohua Li, and 1 more author

In 2022 International Conference on Computer Communications and Networks (ICCCN) Aug 2022
Robustness of Electrical Network Frequency Signals as a Fingerprint for Digital Media Authentication

Nihal Poredi, Deeraj Nagothu, Yu Chen, and 4 more authors

In 2022 IEEE 24th International Workshop on Multimedia Signal Processing (MMSP) Aug 2022

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Real-Time Elderly Monitoring for Senior Safety by Lightweight Human Action Recognition

Han Sun, and Yu Chen

In 2022 IEEE 16th International Symposium on Medical Information and Communication Technology (ISMICT) Aug 2022

Abs

With an increasing number of elders living alone, care-giving from a distance becomes a compelling need, particularly for safety. Real-time monitoring and action recognition are essential to raise an alert timely when abnormal behaviors or unusual activities occur. While wearable sensors are widely recognized as a promising solution, highly depending on user’s ability and willingness makes them inefficient. In contrast, video streams collected through non-contact optical cameras provide richer information and release the burden on elders. In this paper, leveraging the Independently-Recurrent neural Network (IndRNN) we propose a novel Real-time Elderly Monitoring for senior Safety (REMS) based on lightweight human action recognition (HAR) technology. Using captured skeleton images, the REMS scheme is able to recognize abnormal behaviors or actions and preserve the user’s privacy. To achieve a high accuracy, the HAR module is trained and fine-tuned using multiple databases. An extensive experimental study verified that REMS system preforms action recognition accurately and timely. REMS meets the design goals as a privacy-preserving elderly safety monitoring system and possesses the potential to be adopted in various smart monitoring systems.
LightMAN: A Lightweight Microchained Fabric for Assurance-and Resilience-Oriented Urban Air Mobility Networks

Ronghua Xu, Sixiao Wei, Yu Chen, and 2 more authors

Drones Aug 2022

2021

Authenticating Video Feeds Using Electric Network Frequency Estimation at the Edge

Deeraj Nagothu, Yu Chen, Alexander Aved, and 1 more author

EAI Endorsed Transactions on Security and Safety Feb 2021
DeFake: Decentralized ENF-Consensus Based DeepFake Detection in Video Conferencing

Deeraj Nagothu, Ronghua Xu, Yu Chen, and 2 more authors

In IEEE 23rd International Workshop on Multimedia Signal Processing Oct 2021
Detecting Compromised Edge Smart Cameras Using Lightweight Environmental Fingerprint Consensus

Deeraj Nagothu, Ronghua Xu, Yu Chen, and 2 more authors

In Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems Nov 2021

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Rapid advances in the Internet of Video Things (IoVT) deployment in modern smart cities has enabled secure infrastructures with minimal human intervention. However, attacks on audio-video inputs affect the reliability of large-scale multimedia surveillance systems as attackers are able to manipulate the perception of live events. For example, Deepfake audio/video attacks and frame duplication attacks can cause significant security breaches. This paper proposes a Lightweight Environmental Fingerprint Consensus based detection of compromised smart cameras in edge surveillance systems (LEFC). LEFC is a partial decentralized authentication mechanism that leverages Electrical Network Frequency (ENF) as an environmental fingerprint and distributed ledger technology (DLT). An ENF signal carries randomly fluctuating spatio-temporal signatures, which enable digital media authentication. With the proposed DLT consensus mechanism named Proof-of-ENF (PoENF) as a backbone, LEFC can estimate and authenticate the media recording and detect byzantine nodes controlled by the perpetrator. The experimental evaluation shows feasibility and effectiveness of proposed LEFC scheme under a distributed byzantine network environment.
CriPI: An Efficient Critical Pixels Identification Algorithm for Fast One-Pixel Attacks

Wei Quan, Deeraj Nagothu, Nihal Poredi, and 1 more author

In Sensors and Systems for Space Applications XIV Apr 2021

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Deep neural networks (DNN) have been studied intensively in recent years, leading to many practical applications. However, there are also concerns about the security problems and vulnerabilities of DNN. Studies on adversarial network development have shown that relatively more minor perturbations can impact the DNN performance and manipulate its outcome. The impacts of adversarial perturbations have led to the development of advanced techniques for generating image-level perturbations. Once embedded in a clean image, these perturbations are not perceptible to human eyes and fool a well-trained deep learning (DL) convolutional neural network (CNN) classifier. This work introduces a new Critical-Pixel Iterative (CriPI) algorithm after a thorough study on critical pixels’ characteristics. The proposed CriPI algorithm can identify the critical pixels and generate one-pixel attack perturbations with a much higher efficiency. Compared to a one-pixel attack benchmark algorithm, the CriPI algorithm significantly reduces the time delay of the attack from seven minutes to one minute with similar success rates.
Econledger: A proof-of-enf consensus based lightweight distributed ledger for iovt networks

Ronghua Xu, Deeraj Nagothu, and Yu Chen

Future Internet Apr 2021
Decentralized video input authentication as an edge service for smart cities

Ronghua Xu, Deeraj Nagothu, and Yu Chen

IEEE Consumer Electronics Magazine Apr 2021
Fed-ddm: A federated ledgers based framework for hierarchical decentralized data marketplaces

Ronghua Xu, and Yu Chen

In 2021 International Conference on Computer Communications and Networks (ICCCN) Apr 2021
Microchain: A Light Hierarchical Consensus Protocol for IoT Systems

Ronghua Xu, Yu Chen, and Erik Blasch

Apr 2021
Enable Fair Proof-of-Work (PoW) Consensus for Blockchains in IoT by Miner Twins (MinT)

Qian Qu, Ronghua Xu, Yu Chen, and 2 more authors

Future Internet Apr 2021

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2020

Smart Surveillance for Public Safety Enabled by Edge Computing

Deeraj Nagothu, Ronghua Xu, Seyed Yahya Nikouei, and 2 more authors

Edge Computing: Models, technologies and applications Apr 2020
Enabling Continuous Operations for UAVs with an Autonomous Service Network Infrastructure

Michael Rosenberg, John Henry Burns, Deeraj Nagothu, and 1 more author

In Sensors and Systems for Space Applications XIII Apr 2020

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One of the major restrictions on the practical applications of unmanned aerial vehicles (UAV) is their incomplete self-sufficiency, which makes continuous operations infeasible without human oversights. The more oversight UAVs require, the less likely they are going to be commercially advantageous when compared to their alternatives. As an autonomous system, how much human interaction is needed to function is one of the best indicators evaluating the limitations and inefficiencies of the UAVs. Popular UAV related research areas, such as path planning and computer vision, have enabled substantial advances in the ability of drones to act on their own. This research is dedicated to in-flight operations, in which there is not much reported effort to tackle the problem from the aspect of the supportive infrastructure. In this paper, an Autonomous Service network infrastructure (AutoServe) is proposed. Aiming at increasing the future autonomy of UAVs, the AutoServe system includes a service-oriented landing platform and a customized communication protocol. This supportive AutoServe infrastructure will autonomize many tasks currently done manually by human operators, such as battery replacement. A proof-of-concept prototype has been built and the simulation experimental study validated the design.
Collaborative Multi-Object Tracking as an Edge Service Using Transfer Learning

Han Sun, Yu Chen, Alexander Aved, and 1 more author

In 2020 IEEE 22nd International Conference on High Performance Computing and Communications; IEEE 18th International Conference on Smart City; IEEE 6th International Conference on Data Science and Systems (HPCC/SmartCity/DSS) Apr 2020

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Video cameras have been pervasively deployed in Smart Cities and timely processing of the huge amount of video data brings up new challenges. Each individual camera is only able to cover a limited range due to the effective monitoring distances and the complicated layout of a building or an area. Once the object of interest moves away from the camera field of view, the system may lose object tracking. When the object appears and is captured by another camera, object detection, feature abstraction, and behavior classification require an association with other video data exploitation results. Hence, pragmatic approaches for object re-verification (or re-acquisition) are needed so as to not infer the object as a new track. In this paper, we propose a novel Collaborative Multi-Object Tracking system as an Edge service (CoMOTE) based on transfer learning technology. A transfer learning-based human detection and tracking method is introduced, which allocates most of the computing tasks on edge devices. An edge server is adopted to integrate information from edge cameras for decision-making to enable multi-view surveillance. The CoMOTE scheme not only reduces the dependence on a central server but also leads to a lower communication cost. Implemented on edge nodes (Raspberry Pi 4) and tested with WildTrack multi-view video database, extensive experimental results verify that the CoMOTE prototype achieved the design goal and outperforms multiple state-of-the-art methods.
Blendsps: A blockchain-enabled decentralized smart public safety system

Ronghua Xu, Seyed Yahya Nikouei, Deeraj Nagothu, and 2 more authors

Smart Cities Apr 2020
BIT: A blockchain integrated time banking system for community exchange economy

Ronghua Xu, Zhe Zhai, Yu Chen, and 1 more author

In 2020 IEEE International Smart Cities Conference (ISC2) Apr 2020
Hybrid blockchain-enabled secure microservices fabric for decentralized multi-domain avionics systems

Ronghua Xu, Yu Chen, Erik Blasch, and 3 more authors

In Sensors and Systems for Space Applications XIII Apr 2020
Poster: MicroFL: A Lightweight, Secure-by-Design Edge Network Fabric for Decentralized IoT Systems

Ronghua Xu, Yu Chen, and Jian Li

In The Network and Distributed System Security Symposium (NDSS) Apr 2020
Decentralized access control for IoT based on blockchain and smart contract

Ronghua Xu, Yu Chen, and Erik Blasch

Apr 2020
Collaborative Multi-Object Tracking as an Edge Service Using Transfer Learning

Han Sun, Yu Chen, Alexander Aved, and 1 more author

In 2020 IEEE 22nd International Conference on High Performance Computing and Communications; IEEE 18th International Conference on Smart City; IEEE 6th International Conference on Data Science and Systems (HPCC/SmartCity/DSS) Apr 2020

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Video cameras have been pervasively deployed in Smart Cities and timely processing of the huge amount of video data brings up new challenges. Each individual camera is only able to cover a limited range due to the effective monitoring distances and the complicated layout of a building or an area. Once the object of interest moves away from the camera field of view, the system may lose object tracking. When the object appears and is captured by another camera, object detection, feature abstraction, and behavior classification require an association with other video data exploitation results. Hence, pragmatic approaches for object re-verification (or re-acquisition) are needed so as to not infer the object as a new track. In this paper, we propose a novel Collaborative Multi-Object Tracking system as an Edge service (CoMOTE) based on transfer learning technology. A transfer learning-based human detection and tracking method is introduced, which allocates most of the computing tasks on edge devices. An edge server is adopted to integrate information from edge cameras for decision-making to enable multi-view surveillance. The CoMOTE scheme not only reduces the dependence on a central server but also leads to a lower communication cost. Implemented on edge nodes (Raspberry Pi 4) and tested with WildTrack multi-view video database, extensive experimental results verify that the CoMOTE prototype achieved the design goal and outperforms multiple state-of-the-art methods.

2019

A Distributed Agent-Based Framework for a Constellation of Drones in a Military Operation

A.H. Fitwi, D. Nagothu, Y. Chen, and 1 more author

In Proc. - Winter Simul. Conf. Apr 2019

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\textcopyright 2019 IEEE. A seamless communication capability is important in military operations. Likewise, enhanced security, increased capacity, and robust communication mechanisms are vital for humanitarian and disaster-response operations. Often, a system of wide-band satellites is employed for real-time exchange of information and over-the-horizon control, but the communications are prone to denial of service (DoS) attacks, and delayed redeployment. Hence, a swarm of drones could be deployed in mission-critical operations in times of urgency for a secured and robust distributed-intercommunication which is essential for survivability and successful completion of missions. In this paper, a distributed-agent-based framework for secure and reliable information exchange between drones in a constellation is proposed. The framework comprises a mechanism for path planning simulation and estimation, a flexible network architecture for improved client-server(C/S) and peer-to-peer (P2P) connectivity, as well as agents for identity authentications and secure communications. The framework has been simulated and verified with results showing promise.
Detecting Malicious False Frame Injection Attacks on Surveillance Systems at the Edge Using Electrical Network Frequency Signals

Deeraj Nagothu, Y. Yu Chen, Erik Blasch, and 2 more authors

Sensors (Basel). Apr 2019

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Over the past few years, the importance of video surveillance in securing national critical infrastructure has significantly increased, with applications including the detection of failures and anomalies. Accompanied by the proliferation of video is the increasing number of attacks against surveillance systems. Among the attacks, False Frame Injection (FFI) attacks that replay video frames from a previous recording to mask the live feed has the highest impact. While many attempts have been made to detect FFI frames using features from the video feeds, video analysis is computationally too intensive to be deployed on-site for real-time false frame detection. In this paper, we investigated the feasibility of FFI attacks on compromised surveillance systems at the edge and propose an effective technique to detect the injected false video and audio frames by monitoring the surveillance feed using the embedded Electrical Network Frequency (ENF) signals. An ENF operates at a nominal frequency of 60 Hz/50 Hz based on its geographical location and maintains a stable value across the entire power grid interconnection with minor fluctuations. For surveillance system video/audio recordings connected to the power grid, the ENF signals are embedded. The time-varying nature of the ENF component was used as a forensic application for authenticating the surveillance feed. The paper highlights the ENF signal collection from a power grid creating a reference database and ENF extraction from the recordings using conventional short-time Fourier Transform and spectrum detection for robust ENF signal analysis in the presence of noise and interference caused in different harmonics. The experimental results demonstrated the effectiveness of ENF signal detection and/or abnormalities for FFI attacks.
A Microservice-Enabled Architecture for Smart Surveillance Using Blockchain Technology

D. Nagothu, R. Xu, S.Y. Nikouei, and 1 more author

In 2018 IEEE Int. Smart Cities Conf. ISC2 2018 Apr 2019

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\textcopyright 2018 IEEE. While the smart surveillance system enhanced by the Internet of Things (IoT) technology becomes an essential part of Smart Cities, it also brings new concerns in security of the data. Compared to the traditional surveillance systems that is built following a monolithic architecture to carry out lower level operations, such as monitoring and recording, the modern surveillance systems are expected to support more scalable and decentralized solutions for advanced video stream analysis at the large volumes of distributed edge devices. In addition, the centralized architecture of the conventional surveillance systems is vulnerable to single point of failure and privacy breach owning to the lack of protection to the surveillance feed. This position paper introduces a novel secure smart surveillance system based on microservices architecture and blockchain technology. Encapsulating the video analysis algorithms as various independent microservices not only isolates the video feed from different sectors, but also improve the system availability and robustness by decentralizing the operations. The blockchain technology securely synchronizes the video analysis databases among microservices across surveillance domains, and provides tamper proof of data in the trustless network environment. Smart contract enabled access authorization strategy prevents any unauthorized user from accessing the microservices and offers a scalable, decentralized and fine-grained access control solution for smart surveillance systems.
A Study on Smart Online Frame Forging Attacks against Video Surveillance System

D. Nagothu, J. Schwell, Y. Chen, and 2 more authors

In Proc. SPIE - Int. Soc. Opt. Eng. Apr 2019

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\textcopyright 2019 SPIE. Video Surveillance Systems (VSS) have become an essential infrastructural element of smart cities by increasing public safety and countering criminal activities. A VSS is normally deployed in a secure network to prevent the access from unauthorized personnel. Compared to traditional systems that continuously record video regardless of the actions in the frame, a smart VSS has the capability of capturing video data upon motion detection or object detection, and then extracts essential information and send to users. This increasing design complexity of the surveillance system, however, also introduces new security vulnerabilities. In this work, a smart, real-time frame duplication attack is investigated. We show the feasibility of forging the video streams in real-time as the camera’s surroundings change. The generated frames are compared constantly and instantly to identify changes in the pixel values that could represent motion detection or changes in light intensities outdoors. An attacker (intruder) can remotely trigger the replay of some previously duplicated video streams manually or automatically, via a special quick response (QR) code or when the face of an intruder appear in the camera field of view. A detection technique is proposed by leveraging the real-time electrical network frequency (ENF) reference database to match with the power grid frequency.
Real-Time Index Authentication for Event-Oriented Surveillance Video Query Using Blockchain

S.Y. Nikouei, R. Xu, D. Nagothu, and 3 more authors

In 2018 IEEE Int. Smart Cities Conf. ISC2 2018 Apr 2019

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\textcopyright 2018 IEEE. Information from surveillance video is essential for situational awareness (SAW). Nowadays, a prohibitively large amount of surveillance data is being generated continuously by ubiquitously distributed video sensors. It is very challenging to immediately identify the objects of interest or zoom in suspicious actions from thousands of video frames. Making the big data indexable is critical to tackle this problem. It is ideal to generate pattern indexes in a real-Time, on-site manner on the video streaming instead of depending on the batch processing at the cloud centers. The modern edge-fog-cloud computing paradigm allows implementation of time sensitive tasks at the edge of the network. The on-site edge devices collect the information sensed in format of frames and extracts useful features. The near-site fog nodes conduct the contextualization and classification of the features. The remote cloud center is in charge of more data intensive and computing intensive tasks. However, exchanging the index information among devices in different layers raises security concerns where an adversary can capture or tamper with features to mislead the surveillance system. In this paper, a blockchain enabled scheme is proposed to protect the index data through an encrypted secure channel between the edge and fog nodes. It reduces the chance of attacks on the small edge and fog devices. The feasibility of the proposal is validated through intensive experimental analysis.
Exploration of blockchain-enabled decentralized capability-based access control strategy for space situation awareness

Ronghua Xu, Yu Chen, Erik Blasch, and 1 more author

Optical Engineering Apr 2019
Blendsm-ddm: Blockchain-enabled secure microservices for decentralized data marketplaces

Ronghua Xu, Gowri Sankar Ramachandran, Yu Chen, and 1 more author

In 2019 IEEE international smart cities conference (ISC2) Apr 2019
Blendmas: A blockchain-enabled decentralized microservices architecture for smart public safety

Ronghua Xu, Seyed Yahya Nikouei, Yu Chen, and 2 more authors

In 2019 IEEE International Conference on Blockchain (Blockchain) Apr 2019
Decentralized autonomous imaging data processing using blockchain

Ronghua Xu, Sherry Chen, Lixin Yang, and 2 more authors

In Multimodal Biomedical Imaging XIV Apr 2019

2018

Blendcac: A smart contract enabled decentralized capability-based access control mechanism for the iot

Ronghua Xu, Yu Chen, Erik Blasch, and 1 more author

Computers Apr 2018
Constructing trustworthy and safe communities on a blockchain-enabled social credits system

Ronghua Xu, Xuheng Lin, Qi Dong, and 1 more author

In Proceedings of the 15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services Apr 2018
Blendcac: A blockchain-enabled decentralized capability-based access control for iots

Ronghua Xu, Yu Chen, Erik Blasch, and 1 more author

In 2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) Apr 2018
Real-time human objects tracking for smart surveillance at the edge

Ronghua Xu, Seyed Yahya Nikouei, Yu Chen, and 4 more authors

In 2018 IEEE international conference on communications (ICC) Apr 2018
A federated capability-based access control mechanism for internet of things (iots)

Ronghua Xu, Yu Chen, Erik Blasch, and 1 more author

In Sensors and Systems for Space Applications XI Apr 2018
Smart surveillance as an edge network service: From harr-cascade, svm to a lightweight cnn

Seyed Yahya Nikouei, Yu Chen, Sejun Song, and 3 more authors

In 2018 ieee 4th international conference on collaboration and internet computing (cic) Apr 2018
A microservice-enabled architecture for smart surveillance using blockchain technology

Deeraj Nagothu, Ronghua Xu, Seyed Yahya Nikouei, and 1 more author

In 2018 IEEE international smart cities conference (ISC2) Apr 2018
Real-time index authentication for event-oriented surveillance video query using blockchain

Seyed Yahya Nikouei, Ronghua Xu, Deeraj Nagothu, and 3 more authors

In 2018 IEEE International Smart Cities Conference (ISC2) Apr 2018
Blendcac: A blockchain-enabled decentralized capability-based access control for iots

Ronghua Xu, Yu Chen, Erik Blasch, and 1 more author

In 2018 IEEE International conference on Internet of Things (iThings) and IEEE green computing and communications (GreenCom) and IEEE cyber, physical and social computing (CPSCom) and IEEE Smart Data (SmartData) Apr 2018
Real-time human detection as an edge service enabled by a lightweight cnn

Seyed Yahya Nikouei, Yu Chen, Sejun Song, and 3 more authors

In 2018 IEEE International Conference on Edge Computing (EDGE) Apr 2018