Department of Computer Science and Information Systems

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Author: search.filters.author.Gupta, ShashankStart date: 2020

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    Asynchronous deep reinforcement learning for semantic communication and digital-twin deployment in transportation networks
    (IEEE, 2025-08) Gupta, Shashank
    The dynamically evolving and technologically-driven hybrid landscape of transportation networks integrated with advanced edge computing capabilities has demonstrated efficient communication and computation techniques to guarantee robust quality of services (QoS) to vehicles. However, conventional communication systems in the Internet of Vehicles (IoV) still encounter challenges in providing meaningful low-latency communication and AI-assisted real-time synchronization on the edge. One reason is that it has exhausted the Shannon limit by utilizing cellular, NOMA, and Wi-Fi technologies. Therefore, we present an integrated approach leveraging Semantic Communication (SC), and Digital Twin (DT) deployment to tackle the challenges caused by high-dimensional data exchanges and resource spectrum crunch leading to inevitable latency constraints. SC stimulates meaningful transmission of data to high-mobility vehicles by providing a relevant knowledge base (KB) and DT deployment. In this paper, we established the vehicular SC (VSC) model, and DT deployment strategy. We formulate a multi-objective optimization problem (MOP) to maximize the overall QoS of the system by jointly optimizing VSC and DT deployment. Compared to traditional deep-reinforcement learning (DRL) schemes, we propose a Digital Twin Semantic Sensing using the Multi-vehicle DRL ( DTS2 -MVDL) algorithm which addresses the MOP and persistent issues of multi-dimensional, continuous, and discrete nature of the vehicular environment. Lastly, we employ age of Information (AoI), latency, and QoS as the performance metrics to determine the algorithmic efficiency.
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    Leveraging precision agriculture techniques using UAVs and emerging disruptive technologies
    (Elsevier, 2024-07) Gupta, Shashank
    The next great innovation in Unmanned Aerial Vehicles (UAV) technology is smart UAVs, which aim to provide new possibilities in numerous applications. There is an increasing usage of UAVs in various fields of civil applications including live tracking, wireless connectivity, distribution of goods, remote sensing, protection and surveillance, precision agriculture, and review of civil infrastructure. UAVs or drones have a tremen- dous potential to provide smart farming with various productive solutions. Internet of Things (IoT) technologies together with UAVs are anticipated to transform agriculture, allowing decision- making in days rather than weeks, offering substantial cost savings and yield increases. These technologies are employed in a number of different ways, from monitoring crop status and amount of moisture in soil in real time to using drones to help with activities such as the application of pesticide spray. Nonethe- less, the employment of such IoT and smart networking technol- ogy, exposes the smart farming ecosystem to cyber security risks and vulnerabilities. This survey gives a detailed understanding of UAV applications in Precision Agriculture (PA). In this survey, we demonstrate a comprehensive analysis on security and privacy in a smart farming scenario. In this complex and dispersed cyber- physical environment, we describe how Blockchain technology along with 5 G in UAVs communication network can dissipate the security issues of the network. The survey addresses possible scenarios for cyber threats and the advancement in the fields of machine learning and artificial intelligence that can boost cybersecurity. At last, the survey outlines open research issues and future directions in the field of cybersecurity in UAVs and PA.
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    Optimizing quality-of-service (QOS) using semantic sensing and digital-twin in pro-dynamic internet of vehicles (IOV)
    (IEEE, 2024-11) Gupta, Shashank
    The emerging autonomous driving industry expects real-time information to be communicated in less amount of time. Most of the extant research works on deterministic or stochastic channels, which are deemed unrealistic for pro-dynamic Internet of Vehicles (IoV) communications. Semantic communication provides a novel concept of serving high-mobility vehicles with faster vehicular communications by using digital twin (DT) technology. However, the low-latency demand, intermittent connectivity, and signal attenuation in the IoV canyon pose big challenges. To facilitate the efficient functioning of Intelligent Transport Systems (ITS) applications, we integrate DT, which is a co-simulation of software such as CARLA, SUMO, python, etc., to improve the semantic communication and quality of service (QoS) of the IoV scenario. Further, we have formulated a vehicular sensing and computation model that incorporates system cost and DT migration cost as their key metrics to evaluate the QoS of the system. We have proposed a pro-dynamic algorithm based on digital-twin deep reinforcement learning (DT-DRL) to decode the QoS maximization problem. Numerical results reveal the superiority of our method by decreasing the cost of the system and improving latency, maintaining the semantic real-time communication in IoV.
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    Artificial intelligence inspired task offloading and resource orchestration in intelligent transportation systems
    (Springer, 2025) Gupta, Shashank
    Internet of Vehicles (IoV) applications require the support of communication, caching, and computation (3C) resources to offload the computation-intensive tasks and for uplifting the traffic conditions in the development of sustainable smart cities. Intelligent Transportation Systems (ITS) lack the integrated ecosystems of addressing the low-latency task handovers, resource management issues, and centralized incentivization strategies. Digital Twin (DT) aids in capturing the real-time varying resource needs of the vehicles and the communication infrastructure that will regulate the task offloading process and facilitates in incentivizing the vehicular instances. In this manuscript, we establish a digital twin counterpart ( ) of the physical IoV (PIoV) to meet the QoS requirements during dynamic offloading and the time-varying resource supply–demand of computationally intensive applications. We formulate a response delay minimization function which is solved by the proposed DT-driven context-aware dynamic offloading method (CADOM). Furthermore, we use M/M/1/N/FCFS queueing method that combats the drawbacks of handling the simultaneous deadline-based tasks in a volatile environment of PIoV. In addition, we also maximize the utilities of vehicle and RSU service satisfaction by employing a reward-based mechanism for on-demand allocation of resources based on the Stackelberg game, where the DT of vehicle is deemed as a leader and service provider RSUs as a follower. The simulation results establish that the proposed system outpaces the conventional traffic management system by emphasizing the role of in jointly optimizing the overall response latency for different task sizes and also ensure a better utility satisfaction by catering on-demand resource allocation.
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    ST-IDS: Spatio-temporal feature-based multi-tier intrusion detection system for artificial intelligence-powered connected autonomous vehicles
    (Wiley, 2025-03) Gupta, Shashank
    Advancements in 3GPP specifications and the extensive deployment of 5G networks have driven significant growth in the Internet of Vehicles (IoVs). This development has led to an increase in Connected and Autonomous Vehicles (CAVs), which provide capabilities such as automated navigation, ADAS, cruise control, and environmentally sustainable transportation in real-time. Additionally, the widespread adoption of CAVs has also escalated vulnerabilities within the IoV ecosystem, exposing it to potential cyberattacks. The integration of various functional interfaces has enlarged its attack surface, thereby increasing the risk of vehicle infiltration. Researchers have proposed various Intrusion Detection Systems (IDS) to address the ongoing risk of vehicle attacks, without applying encryption and related authentication methods for intra-and inter-vehicular communications. However, a significant limitation of many IDSs is their dependency on characteristics specific to a particular category of vehicles, which limits their adaptability. Additionally, current IDSs frequently rely on one-dimensional features such as traffic, time, etc., which limits their capability of detecting attacks in adverse scenarios. Moreover, incorporating machine learning algorithms into IDSs deployed in automated automobiles causes an increase in computational demands. We propose to develop a collaborative IDS specifically designed for cloud-based vehicle environments. We aim to improve our capabilities of identifying intrusion detection and differentiate which are malicious by using multidimensional features. A customised Convolutional Neural Network (CNN), optimised through hyperparameter tuning, is also developed for detecting the malicious vehicles and enhancing the overall IDS. To address the challenge of data diversity, we integrate various vehicular datasets into a unified feature space. This integration allows a single model to efficiently perform multi-classification tasks without frequent adjustments. Our feature space integrates dimensions such as traffic, time and so forth, levels, thereby expanding the spectrum of detectable attack scenarios. By identifying abnormal data points within this comprehensive feature framework, our system effectively identifies intrusions across a diverse range of vehicle types. As a result, our methodology supports robust intrusion detection through comprehensive multiclass vehicle classification.
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    HS-GIoV: High-speed green internet of vehicles (IoV) edge-assisted model for low-latency inference in autonomous driving
    (Elsevier, 2025-08) Gupta, Shashank
    Green IoV has emerged as a latent solution in the field of autonomous driving for the future Intelligent transportation system (ITS) accompanied with green wireless communication and computational intelligence. It facilitates enhanced traffic management applications, reduced traffic congestion and compatible V2X connectivity. However, GIoV faces significant challenges in providing seamless bandwidth for real-time video analytics, especially under adverse environments, with improved accuracy in autonomous driving. Although deep neural networks (DNN) are effective in locating vehicles, they struggle to frequently access the edge network and maintain accuracy. In addition, their substantial computational demands waste energy and render them infeasible to deploy on resource-constrained devices for low-latency real-time inference. In this paper, we propose a high-speed GIoV (HS-GIoV) framework that models the problem of energy-efficient video analytics accuracy over multiple time-periods using Lyapunov optimization. To solve this problem, we have proposed a novel on-the-fly Traffic Stream Object Detection (TSOD) algorithm which is lightweight and triggers the re-training only when there is an accuracy decline, thereby avoiding unnecessary computations. We have also proposed a heuristic algorithm that solves seamless bandwidth issue using Lagrangian relaxation. We have tested the HS-GIoV on the self-driving kit that comprises NVIDIA high-end devices. It enhances the accuracy around 20% and reduces the training time to approx. 55%.
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    Nested context-aware sanitisation and feature injection in clustered templates of JavaScript worms on the cloud-based OSN
    (Inder Science, 2020) Gupta, Shashank
    This article presents an enhanced JavaScript feature-injection based framework that obstructs the execution of cross-site scripting (XSS) worms from the virtual machines of cloud-based online social network (OSN). It calculates the features of clustered-sanitised compressed templates of JavaScript attack vectors embedded in the HTTP response messages. Any variation observed in such JavaScript feature set indicates the injection of XSS worms on the cloud-based OSN server. The injected worms will further undergo through the process of nested context-aware sanitisation for its safe interpretation on the web browser. The prototype of our framework was developed in Java and installed in the virtual machines of cloud environment. The experimental evaluation of our framework was performed on the platform of OSN-based web applications deployed in the cloud platform. The performance analysis done revealed that our framework detects the injection of malicious JavaScript code with low false negative rate and acceptable performance overhead.
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    Future IoT-enabled threats and vulnerabilities: State of the art, challenges, and future prospects
    (Wiley, 2020-05) Gupta, Shashank
    In the recent era, the security issues affecting the future Internet-of-Things (IoT) standards has fascinated noteworthy consideration from numerous research communities. In this view, numerous assessments in the form of surveys were proposed highlighting several future IoT-centric subjects together with threat modeling, intrusion detection systems (IDS), and various emergent technologies. In contrast, in this article, we have focused exclusively on the emerging IoT-related vulnerabilities. This article is a multi-fold survey that emphasizes on understanding the crucial causes of novel vulnerabilities in IoT paradigms and issues in existing research. Initially, we have emphasized on different layers of IoT architecture and highlight various emerging security challenges associated with each layer along with the key issues of different IoT systems. Secondly, we discuss the exploitation, detection, and defense methodologies of IoT malware-enabled distributed denial of service (DDoS), Sybil, and collusion attack capabilities. We have also discussed numerous state-of-the-art strategies for intrusion detection and methods for IDS setup in future IoT systems. Third, we have presented a brief classification of existing IoT authentication protocols and a comparative analysis of such protocols based on different IoT-enabled cyber attacks. For conducting a real-time future IoT research, we have presented some emerging blockchain solutions. We have also discussed a comparative examination of some of the recently developed simulation tools and IoT test beds that are characterized based on different layers of IoT infrastructure. We have also outlined some of the open issues and future research directions and also facilitate the readers with broad classification of existing surveys in this domain that addresses several scopes related to the IoT paradigm. This survey article focuses in enabling IoT-related research activities by comparing and merging scattered surveys in this domain.
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    Security of Cryptocurrencies in blockchain technology: State-of-art, challenges and future prospects
    (Elsevier, 2020-08) Gupta, Shashank; Dua, Amit
    In contemporary era of technologies, blockchain has acquired tremendous attention from various domains. It has wide spectrum of applications ranging from finance to social services and has greatly influenced the emerging business world. Since, blockchain technology is getting embedded in the e-commerce services, the cryptocurrencies are gaining huge prevalence. Bitcoin and ethereum are few such crypto currencies, which have utilized decentralized nature of blockchain. Blockchain can be considered as a distributed database system containing immutable ledgers, which are prone to attack by malicious users. Although, from the initial digital currency to the present smart contract, the utilities of blockchain have been harnessed, the innovative technology has to rely on cryptography for its security. There are several reports, which emphases on the vulnerabilities and security of blockchain, however, there is a lack of a comprehensive and methodical survey in both application and technical views. In this survey article, the authors cover various aspects related to blockchain including its taxonomies and the situations in which a particular category of blockchain should be applied. The authors also focusses on the structure of blockchain and the working of the ongoing transactions in the cryptocurrency network. In addition, the authors also specify various categories of consensus protocols, smart contracts, forks, techniques for generating the consensus. A detailed taxonomy of blockchain along with their features and related real-world applications is also discussed. In addition, existing key platforms of blockchain related to the cryptocurrencies, hyperledger and multichain are also discussed. Existing emerging vulnerabilities of blockchain related to the recent attacks on bitcoin and etherum is also presented along with the defensive methodologies and future trends in blockchain.
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    Detecting Different Attack Instances of DDoS Vulnerabilities on Edge Network of Fog Computing using Gaussian Naive Bayesian Classifier
    (IEEE, 2020) Dua, Amit; Gupta, Shashank
    Fog computing generally uses the host's resources instead of acquiring resources from remote PC leading to less latency problems and moreover, improving the performance which makes it more competent. Distributed denials of service (DDOS) attack exhausts the existing resources which make the services inaccessible to genuine users. DDoS has deep impact on the computer networks. As a cyber-threat, it compromises the standard performance of the organization by Internet protocol (IP) spoofing, overflow of bandwidth, memory space consumption and leading to immense loss. DoS attacks are a great threat to computerized association. Primary objective of any defense system for DoS is knowledge that it exists, preferably as early prior to accumulation of attack traffic. In case of large traffic inflow to an attacked server, it is essential to categorize the legitimate acquisitions and intrusions. In this work, the authors present a model that draws out the key parameters from requests in traffic for DDoS attack recognition in fog network. It benefits from existing data, and presents competent algorithms to detect and predict most probable cases. Authors have used Bayesian Network to calculate the conditional probabilities to decide whether the new packet is normal or intruded. A log of the path of the attacker is maintained in a VHD so as to easily detect attacks that have previously occurred. Having both the systems in place, the false positives of DDoS attacks detection have decreased immensely which has been observed through the implementation of this experiment.