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Enhancing blockchain scalability and security: the early fraud detection (EFD) framework for optimistic rollups

2024, Shristy Gupta, Amritesh Kumar, Lokendra Vishwakarma, Das, Debasis

Blockchain is an emerging technology that improves efficiency, transparency, and security in applications such as fintech, smart cities, healthcare, etc. However, blockchain technology faces scalability issues as the volume of transactions grows. One solution to enhance the scalability is offloading transactions outside the main blockchain layer using the Optimistic Rollup. In this context, we propose the Early Fraud Detection (EFD) framework that utilizes Optimistic Rollups and incorporates early fraud proofs by applying Bloom–Merkle trees that aim to reduce the challenger’s verification time and cost. The EFD framework has been tested using the Ethereum Mainnet Test Network and developed with Solidity. It demonstrates that the proposed EFD framework reduces the total cost to users by 25%. Moreover, it is robust against security threats, including Double Spending, Sybil, and Denial-of-Service (DOS) attacks.

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SmartGrid-NG: Blockchain Protocol for Secure Transaction Processing in Next Generation Smart Grid

2024, Lokendra Vishwakarma, Das, Debasis, Sajal K. Das, Christian Becker

With the advent of Blockchain and the Internet of Things (IoT), the Smart Grid is a rapidly growing technology in decentralized energy distribution and trading. However, this advancement came with some serious cyber security challenges and attacks, such as single-point failure due to a centralized architecture of smart grids, slow transaction processing, emerging cybersecurity threats, double-spending, fork, and fault tolerance. We propose a comprehensive framework for the smart grid called SmartGrid-NG to solve all these issues. Instead of using blockchain as a blackbox plugin tool, we also propose a reputation-based blockchain protocol called GridChain to increase the performance of blockchain-based smart grid systems. The security analysis illustrates that the SmartGrid-NG withstands the attacks mentioned above. The performance analysis also states that the consensus delay is reduced to 80%, throughput is increased up to 60%, and computation overhead and energy consumption are reduced up to 70%.

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Synergizing Vision and Language in Remote Sensing: A Multimodal Approach for Enhanced Disaster Classification in Emergency Response Systems

2024, Shubham Gupta, Nandini Saini, Kundu, Suman, Chiranjoy Chattopadhyay, Das, Debasis

As remote sensing capabilities continue to advance, there is a growing interest in leveraging computer vision and natural language processing for enhanced interpretation of remote sensing scenes. This paper explores the integration of textual information with images to augment traditional disaster classification methods. Our approach utilizes a predefined vision-language model to generate descriptive captions for images, fostering a more nuanced understanding of the remote sensing data. Next, we seamlessly integrate the generated textual information with image data through multimodal training, employing a multimodal deep learning method for disaster classification. The system categorizes input data into predefined disaster categories, presenting a comprehensive and accurate approach to emergency response system development. Experimental evaluations conducted on the AIDER dataset (Aerial Image Database for Emergency Response applications) showcase the efficacy of our approach, demonstrating improved accuracy compare to unimodal approach and reliability in disaster classification. This research contributes to the advancement of intelligent emergency response systems by harnessing the synergy between vision and language in the context of remote sensing.

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Clouds on the Road: A Software-Defined Fog Computing Framework for Intelligent Resource Management in Vehicular Ad-Hoc Networks

2024, Ankur Nahar, Koustav Kumar Mondal, Das, Debasis, Rajkumar Buyya

The integration of software-defined networking (SDN) and cloud radio access networks (CRANs) into vehicular ad hoc networks (VANETs) presents intricate challenges to achieving stringent service level objectives (SLOs). These objectives include optimizing data flow and resource management, achieving low latency and rapid response times, and ensuring network resilience under fluctuating conditions. Traditional load balancing and clustering approaches, designed for more static environments, fall short in the dynamic and variable context of VANETs. This necessitates a paradigm shift towards more adaptive and robust strategies to meet these advanced SLOs reliably. This paper proposes a software-defined vehicular fog computing (SDFC) framework that refines resource allocation in VANETs. Our SDFC framework utilizes an intelligent controller placement that strategically positions decision-making entities within the network to optimize data flow and resource distribution. This placement is governed by a dynamic clustering algorithm that responds to variable network conditions, an advancement over the static mappings used by traditional methods. By incorporating parallel processing principles, the framework ensures that computational tasks are distributed effectively across network nodes, reducing bottlenecks and enhancing overall network agility. Empirical evaluations (testbed) and simulation results of our framework indicate a substantial increase in network efficiency: a 28% improvement in average response time, a 23% decrease in network latency, and a 25% faster convergence to optimal resource distribution compared to state-of-the-art methods. These improvements testify to the framework's ability to underscore its potential to refine operational efficacy within VANETs.

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BLISS: blockchain-based integrated security system for internet of things (IoT) applications

2024, Lokendra Vishwakarma, Das, Debasis

Cybersecurity is an essential part of IoT device functionality. Malicious acts cause the disclosure of private data, putting device performance at risk. As a result, creating efficient security solutions for authentication and confidentiality of both IoT devices and data exchange and integrity of data exchange networks has become a significant challenge. Furthermore, the traditional security mechanisms’ high computation demands are not suitable for resource-constrained specific IoT devices. Therefore, we have proposed a security system for IoT applications using blockchain called BLISS, which ensures robust identification, authentication, confidentiality, and integrity of IoT devices and data exchange. Using smart contracts, the BLISS creates trustful clusters of IoT devices through an authentication process for data exchange. The BLISS is implemented on the Raspberry Pi 4 and the desktop PC, considering the Raspberry Pi 4 as an IoT device and the desktop as a cluster head. The performance analysis of the BLISS demonstrates the enhanced performance in the context of computation and energy consumption, which is 62–65% reduced. The storage and communication overhead is reduced by up to 70% compared with state-of-the-art schemes. The security analysis showed that the proposed scheme withstands many IoT-specific cyber threads.

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EVDNET: Towards Explainable Multi Scale, Anchor Free Vehicle Detection Network in High Resolution Aerial Imagery

2024, Nandini Saini, Shubham Gupta, Chiranjoy Chattopadhyay, Das, Debasis, Kundu, Suman

The rapid advancement in deep learning-based object detection methods has made them a prevalent choice for real-time applications. Families of object detectors, including one-stage detectors, two-stage detectors, and region-based CNN networks, offer superior performance in accurately detecting objects. Despite their high accuracy, the complex design and black-box functionality of these models are not directly transferable in aerial imagery. Also, raise questions among users regarding the transparency of the algorithm in locating objects. Consequently, to demystify the decision process of these models, there is a need for Explainable AI (XAI) tools. XAI enables an understanding of the significance of each pixel in an image, shedding light on the contributions that lead to the model's final output. In this context, this work will present an efficient, explainable, multi-scale vehicle detection network from high resolution aerial imagery, named as EVDNet. The EVDNet model has trained with two publicly available aerial image benchmark dataset DOTA and VEDAI. To enhance interpretability, we leverage XAI method using GradCam. The experimental results not only showcase the effectiveness and performance of the EVDNet model but also provide valuable insights into the object detection process. This research contributes to bridging the gap between complex object detection models and user understanding, offering a more transparent and interpretable approach to high-resolution aerial imagery analysis.

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SECURE: Secure and Efficient Protocol Using Randomness and Edge-Computing for Drone-Assisted Internet of Vehicles

2024, Himani Sikarwar, Harsha Vasudev, Das, Debasis, Mauro Conti, Koustav Kumar Mondal

The Internet of Vehicles (IoV) faces significant challenges related to secure authentication, efficient communication, and privacy preservation due to the high mobility of vehicles, the need for real-time data processing, varying quality of communication links, and the diverse range of devices and protocols requiring interoperability. These challenges are further complicated by the large-scale, dynamic, and heterogeneous nature of IoV systems. Traditional approaches using Road Side Connecting Nodes (RSCNs) face challenges like limited range, high costs, and single points of failure. Drone-assisted IoV (DIoV) networks address these issues by using Unmanned Aerial Vehicles (UAVs) as mobile edge nodes, enhancing connectivity, extending coverage, and improving adaptability and resilience. To address these challenges, we propose SECURE, a drone-assisted, Physically Unclonable Function (PUF)-based authentication and privacy-preserving protocol integrated with edge computing. This architecture replaces RSCNs with edge nodes and incorporates UAVs as mobile edge nodes, providing extended coverage, reduced latency, and enhanced adaptability. The PUFs in SECURE generate unique hardware-based cryptographic keys, adding an additional layer of security, while edge computing offloads computational tasks, improves network efficiency, and further reduces latency. The formal security analysis, conducted using the Random Oracle Model (ROM), proves the robustness of the session key against active and passive adversaries. Furthermore, informal security analysis demonstrates that SECURE effectively resists various security attacks, while achieving confidentiality, integrity, and authenticity in DIoV. In SECURE, we have considered two types of devices for experiments: NVIDIA Jetson Xavier NX and Raspberry Pi 4. The performance analysis, considering the results from Jetson Xavier NX, demonstrates that SECURE achieves maximum upto approximately 82.1% less communication cost and 78% faster computation time compared to the state-of-the-art schemes.

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Adaptive Context Based Road Accident Risk Prediction Using Spatio-Temporal Deep Learning

2024, Nishit Bhardwaj, Anupriya Pal, Das, Debasis

Traffic accidents are common urban events that pose significant risks to human safety, traffic management, and economic stability; consequently, the research community is paying increasing attention toward accident risk prediction. However, accident risk prediction is a challenging problem because accident occurrences are sparse and influenced by multiple contextual factors (e.g., POI, road structure, road type, hour of the day, and month). Therefore, in this article, we propose a novel architecture named Topographic-Weighted Context Category (TWCCnet) that adapts heterogeneous contextual category weights based on spatial-temporal correlations across sectors. Specifically, the framework consists of two parallel components: one uses convolution and stacked bidirectional gated recurrent unit (Bi-GRU) to capture spatial-temporal relationships between neighborhood sectors, while the other uses multiple graph convolution network (GCN) over resemblance graphs to capture spatial-temporal relationships between semantic sectors. At last, temporal attention is utilized on top of parallel components to learn important spatiotemporal features that have a substantial impact on traffic accidents. The extensive experiments on two publicly available citywide datasets, i.e., New York City and Chicago demonstrate the effectiveness of the proposed approach and showed (7.21, 10.7) Root Mean Squared Error (RMSE), (34.09, 20.75) Mean Average Precision (MAP), and (0.19, 0.09) Recall approximately over both datasets, respectively, outperforming baseline as well as state-of-the-art models.

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Co-Move: COVID-19 and Inter-Region Human Mobility Analysis and Prediction

2024, Sandip Kumar Burnwal, Pragati Sinha, Jayant Vyas, Das, Debasis

Humans relocate for a variety of reasons, including employment, study, tourism, family, and health. However, in COVID-19, the government imposed restrictions such as lockdowns, travel bans, and quarantine regulations, preventing many people from traveling for work, study, or leisure; thus, human mobility exhibits distinct patterns than ordinary movements. In this article, we analyze the effect of COVID-19 on interregion human mobility using curated Twitter data and propose a framework named Co-Move for human mobility prediction. There were three challenges in predicting mobility: 1) heterogenous data; 2) short and long-term periodic patterns; and 3) complex intercorrelation. To address these challenges, the framework comprises parallel multiscale convolution and long short-term memory components. Extensive experiments on real-life mobility datasets show the mean square error (MSE) of 0.0179, RMSE of 0.129, mean absolute error (MAE) of 0.1075, and outperform baseline models.

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ETradeChain: Blockchain-Based Energy Trading in Local Energy Market (LEM) Using Modified Double Auction Protocol

2024, Umang Rajendra Barbhaya, Lokendra Vishwakarma, Das, Debasis

The smart grid's local energy market (LEM) enables each renewable-energy-powered residential unit to profit from trading energy with others. However, energy trading in LEMs is witnessing many cybersecurity challenges, such as transaction integrity and user authentication. Among these, energy trading and price computing using auctions have generally been accepted. However, state-of-the-art auction schemes are centralized and unfair, meaning that prosumers are not equally benefited. We proposed ETradeChain, a platform for energy trading based on blockchain technology. The trading in ETradeChain happens with the help of a modified double auction scheme to make it fully decentralized and fair for all the members of LEM, along with information secrecy. We have developed a pseudo coin called Pcoins (Power Coins) based on the energy generated by the prosumer for energy trading in LEM. The ETradeChain uses a double auction process with Pcoin as a stake to reach a consensus on the energy transaction. Furthermore, ETradeChain employs blockchain technology to demonstrate the viability of real-time peer-to-peer (P2P) trading for practical purposes. We have set up a Testbed for the experiments using Raspberry Pi 4 model B IoT devices. The experiment results show that the ETradeChain minimized the consensus delay up to 90% with 60% high throughput. It also achieved 80% low computational overhead and 70-80% low storage and communication overhead.