Browsing by Author "Dua, Amit"

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Amalgamating Vehicular Networks With Vehicular Clouds, AI, and Big Data for Next-Generation ITS Services
(IEEE, 2024-01) Dua, Amit
Advances in the connected vehicle and cloud computing technologies, Big data, and artificial intelligence techniques have opened new research opportunities. We can integrate them to work out the issues originating from transportation complexities and offer improved services. In this work, we present a seamless multi-module multi-layer vehicular cloud computing system developed using resources of parked vehicles, cloud computing facilities, and vehicular networking technologies. It can offer transportation-specific AI and Big data-empowered services to on-road vehicles. As use cases, we present two innovative and improved services, vehicular Big data mining and vehicular route optimization. A physical testbed is formed to show the feasibility of this work. Results analysis shows that the systems perform better than the standalone systems and servers under different scenarios. Relevant fundamental challenges and future outlooks are also highlighted in this work.
Block-CPS: Blockchain and Non-Cooperative Game-Based Data Pricing Scheme for Car Sharing
(IEEE, 2022-12) Dua, Amit
This article proposes a blockchain and non-cooperative game theoretic-based secure and optimized data pricing scheme, i.e.,Block-CPS. It aims to secure the data transactions between vehicle owners and customers for rides. It uses the fifth-generation (5G) communication network that offers ultrareliable low-latency communications between vehicle owners and customers. The Interplanetary file system (IPFS) storage protocol used in the proposal reduces the blockchain data storage cost. We then formulated a non-cooperative game-theoretic approach to maximize the profits for vehicle owners and customers. Formulated non-cooperative game is integrated with blockchain to provide security to the Block-CPS. The vulnerability of the developed smart contract is verified and validated using tools like smartcheck and verisol. The performance of Block-CPS is evaluated by comparing it with the traditional approaches using blockchain with 4G and LTE-A networks. The performance evaluation parameters used are system scalability, network latency, data storage cost and its computation, network throughput, profit, communication reliability, and convergence for the optimal payoff between vehicle owners and customers. The performance results shows the Block-CPS outperforms the traditional blockchain-based systems
A Blockchain Technology based Framework for Environmental and Social Impact Authenticity of a 3D Printed Product
(Elsevier, 2023) Sangwan, Kuldip Singh; Dua, Amit
This paper proposes a conceptual framework for implementing blockchain technology to enhance traceability, transparency, and authenticity of a 3D printed product. An implementation framework is developed using blockchain technologies to record and trace critical attributes during the various life cycle phases of a 3D printing value chain, viz. raw material extraction, chemical processing, polymerization, filament production, 3D printing, and end-of-life recycling of the product. The information on critical attributes of carbon footprint, workers' age, and material flow during the entire value chain is captured to provide authentic output of carbon footprint and labour age during any of the value chain activity. The uniqueness of the current work lies in offering a series of immutable transactions using blockchain technology to comprehend the circularity of 3D printing material and account for the overall carbon footprint produced by a 3D printed product considering its whole value chain. This would improve the traceability and visibility of the material supply chain for 3D printing. On the hindsight, the proposed framework is expected to assist the manufacturing firms to act as responsible manufacturers by providing the authentic data for the computation of environmental assessment as well as social issues of child labour throughout the value chain.
Blockchain-enabled secure transactions in V2G networks
(IEEE, 2025-06) Dua, Amit; Bansal, Hari Om
The increasing integration of Vehicle-to-Grid (V2G) systems in modern energy networks necessitates secure, transparent, and efficient mechanisms for energy trading. However, existing centralized approaches are susceptible to cyberattacks, lack transparency, and face scalability challenges. This paper proposes a blockchain-enabled decentralized platform to ensure secure and tamper-proof energy transactions between electric vehicles (EVs) and the grid. The system leverages smart con-tracts for automated settlements, cryptographic protocols for enhanced data privacy, and artificial intelligence (AI) models for real-time anomaly detection and fraud prevention. By integrating blockchain's transparency and immutability with AI's adaptability, the proposed solution addresses key challenges in trust, scalability, and cybersecurity. A prototype implementation demonstrates the platform's effectiveness, achieving improved transaction throughput, reduced latency, and enhanced resistance to malicious activities compared to traditional systems. Extensive simulations validate the scalability and resilience of the proposed framework under varying transaction loads and attack scenarios. This work lays the foundation for a transformative approach to energy trading, fostering increased trust, participation, and sustainability in V2G markets.
Bloom filter based efficient caching scheme for content distribution in vehicular networks
(IEEE, 2019) Dua, Amit
With the progression of Internet of Things (IoT), the number of connected vehicles in smart cities have shown an exponential growth. In order to enable fast data transfer, these vehicles communicate with each other and the roadside units. However, the data generated by these connected vehicles has overwhelming increased in recent times, which in turn overloads the traditional host-centric approach. For this reason, vehicular content-centric networking (VCCN) has emerged as a revolution, which provides efficient content distribution among vehicles. However, cache management is one of the most important aspects, which needs to be handled effectively. Therefore, in this paper, a bloom filter-based cache management scheme is proposed to enhance the performance of data transfer between smart connected vehicles. This algorithm uses the functionality of cache in vehicles to enable cooperative content distribution. The evaluation of bloom filter-based cache management scheme shows that the bloom filter reduces the search time in contrast to other data structures, which in turn increases the efficiency of retrieval from cache. The simulation results show that the proposed scheme achieves faster retrieval time and an efficient allotment system for helper vehicles in a smart city environment.
Coalition Games for Performance Evaluation in 5G and Beyond Networks: A Survey
(IEEE, 2022-01) Dua, Amit
The 5G network is an emerging field of the research community. 5G is a multi-disciplinary network that aims to support a wide range of services. 5G network has an objective to support a massive number of connected devices. Game theory has an extensive role in wireless network management. Game theory is an approach to analyzing and modeling the system where multiple actors have a role in decision-making with independent objectives and actions. The game theory is an exciting methodology to control the strategic behavior of players and generate an efficient outcome. Coalition game theory can play a crucial role in ensuring cooperation among a massive number of devices. This article provides insight into the current research trends in 5G using coalition games. The work presented in the survey is divided into three categories, namely resource management, interference management, and miscellaneous. This article also provides the foundation about 5G and coalition games highlight the scope of future research.
Collaborative P2P context-aware information propagation in vehicular ad hoc networks
(IEEE, 2015) Dua, Amit
With exponential growth of the Internet users in past few years, there is a need of context-aware information sharing among different inter-connected entities over the Internet. The prime objective of the inter-connected objects is that within a minimum use of available resources, maximum output with respect to parameters such as throughput and delay can be achieved. But, due to high velocity and irrelevant information propagation, there may be a performance degradation in some part of the network with respect to these parameters. To address these issues, we have designed novel algorithms for context-aware information propagation among the vehicles. The proposed scheme consists of algorithms for data access, data dissemination, and data suggestion. These algorithms are based on reliability of vehicle which is calculated as soon as vehicles enter the network and is updated after each successful execution of various operations of information propagation. The scheme works on by increasing the reliability which in turn solve the broadcast storm problem in which sometime irrelevant information may also be sent to the vehicles. Simulation results prove the merit of the proposed scheme over the other existing schemes with respect to parameters such as message overhead, connectivity ratio, and resources utilization.
ConvXSS: A deep learning-based smart ICT framework against code injection attacks for HTML5 web applications in sustainable smart city infrastructure
(Elsevier, 2022-05) Dua, Amit; Gupta, Shashank
In this paper we propose ConvXSS, a novel deep learning approach for the detection of XSS and code injection attacks, followed by context-based sanitization of the malicious code if the model detects any malicious code in the application. Firstly, we briefly discuss XSS and code injection attacks that might pose threat to sustainable smart cities. Along with this, we discuss various approaches proposed previously for the detection and alleviation of these attacks followed by their respective limitations. Then we propose our deep learning model adopting whose novelty is based on the approach followed for Data Pre-Processing. Then we finally propose Context-based Sanitization to replace the malicious part of the code with sanitized code. Numerical experiments conducted on various datasets have shown various results out of which the best model has an accuracy of 99.42%, a precision of 99.81% and a recall of 99.35%. When compared with other state of the art techniques in this domain, our approach shows at par or in the best case, better results in terms of detection speed and accuracy of CSS attacks.
Cross-chain Transaction Validation using Lock-and-Key Method for Multi-System Blockchain
(IEEE, 2023) Dua, Amit
Blockchains have profoundly impacted finance and administration, but there are several issues with the current blockchain platforms, including a lack of system interoperability. Currently used blockchain application platforms only work within their networks. Although the underlying concept of all blockchain networks is mainly similar, it involves centralised third-party mediators to transact from other blockchain networks. The current third-party intermediates establish security and trust by keeping track of “account balances” and attesting to the validity of transactions in a centralised ledger. The lack of sufficient inter-blockchain connectivity hinders the mainstream adoption of blockchain. Blockchain technology may be a solid solution for many systems if it grows and works with other systems. For the multi-system blockchain concept to materialise, a mechanism that would connect and communicate with the blockchain systems of various entities in a distributed manner (without any intermediary) while maintaining the property of trust and integrity established by individual blockchains is required. Several methods for verifying cross-chain transactions have been explored in this paper among various blockchains. The efficient verification of cross-chain transactions faces many difficulties, and current research has yet to scratch the surface. In addition to summarising and categorising these strategies, the report also suggests a novel mechanism that gets beyond the existing drawbacks.
Decision tree and SVM-based data analytics for theft detection in smart grid
(IEEE, 2016-03) Dua, Amit
Nontechnical losses, particularly due to electrical theft, have been a major concern in power system industries for a long time. Large-scale consumption of electricity in a fraudulent manner may imbalance the demand-supply gap to a great extent. Thus, there arises the need to develop a scheme that can detect these thefts precisely in the complex power networks. So, keeping focus on these points, this paper proposes a comprehensive top-down scheme based on decision tree (DT) and support vector machine (SVM). Unlike existing schemes, the proposed scheme is capable enough to precisely detect and locate real-time electricity theft at every level in power transmission and distribution (T&D). The proposed scheme is based on the combination of DT and SVM classifiers for rigorous analysis of gathered electricity consumption data. In other words, the proposed scheme can be viewed as a two-level data processing and analysis approach, since the data processed by DT are fed as an input to the SVM classifier. Furthermore, the obtained results indicate that the proposed scheme reduces false positives to a great extent and is practical enough to be implemented in real-time scenarios.
Deep Learning Approach for SDN-Enabled Intrusion Detection System in IoT Networks
(MDPI, 2023-01) Dua, Amit
Owing to the prevalence of the Internet of things (IoT) devices connected to the Internet, the number of IoT-based attacks has been growing yearly. The existing solutions may not effectively mitigate IoT attacks. In particular, the advanced network-based attack detection solutions using traditional Intrusion detection systems are challenging when the network environment supports traditional as well as IoT protocols and uses a centralized network architecture such as a software defined network (SDN). In this paper, we propose a long short-term memory (LSTM) based approach to detect network attacks using SDN supported intrusion detection system in IoT networks. We present an extensive performance evaluation of the machine learning (ML) and deep learning (DL) model in two SDNIoT-focused datasets. We also propose an LSTM-based architecture for the effective multiclass classification of network attacks in IoT networks. Our evaluation of the proposed model shows that our model effectively identifies the attacks and classifies the attack types with an accuracy of 0.971. In addition, various visualization methods are shown to understand the dataset’s characteristics and visualize the embedding features.
DEEP-CARDIO: Recommendation System for Cardiovascular Disease Prediction Using IoT Network
(IEEE, 2024-05) Dua, Amit
The Internet of Things (IoTs)-based remote healthcare applications provide fast and preventative medical services to the patients at risk. However, predicting heart disease is a complex task, and diagnosis results are rarely accurate. To address this issue, a novel Recommendation System for Cardiovascular Disease (CVD) Prediction Using IoT Network (DEEP-CARDIO) has been proposed for providing prior diagnosis, treatment, and dietary recommendations for cardiac diseases. Initially, the physiological data are collected from the patients remotely by using the four biosensors, such as ECG sensor, pressure sensor, pulse sensor, and glucose sensor. An Arduino controller receives the collected data from the IoT sensors to predict and diagnose the disease. A CVD prediction model is implemented by using bidirectional-gated recurrent unit (BiGRU) attention model, which diagnoses the CVD and classifies into five available cardiovascular classes. The recommendation system provides physical and dietary recommendations to cardiac patients based on the classified data, via user mobile application. The performance of the DEEP-CARDIO is validated by Cloud Simulator (CloudSim) using the real-time Framingham’s and Statlog heart disease dataset. The proposed DEEP CARDIO method achieves an overall accuracy of 99.90%, whereas the MABC-SVM, HCBDA, and MLbPM methods achieve 86.91%, 88.65%, and 93.63%, respectively.
Demand response management using lattice-based cryptography in smart grids
(IEEE, 2018) Dua, Amit
The prolonged usage of non-renewable resources like petroleum and coal have adverse affect on the environment and has led to energy crisis in the world. In order to mitigate the situation, efficient strategies have been proposed for generation, distribution and consumption of energy obtained from renewable sources such as tidal, wind and solar power. With the advent of Smart Grids being developed world wide, the most widely accepted strategy is Demand-Response management. In this strategy, the customers or end-users are incentivized to change their energy-utility behavior with time in response to fluid price changes or to induce lower energy consumption during peak demand time. The system is controlled by a cloud of servers that monitor the demand- supply chain over a network all the time. This brings up the issue of security within the operations of the system. Current security mechanisms such as Rivest-Shamir-Alderman (RSA) public key encryption, Advanced Encryption Standard (AES) symmetric encryption, Elliptic Curve Cryptography (ECC) public-key cryptosytem and the recently proposed works are not future- proof in the world of post-quantum cryptography. This paper proposes a lattice based cryptographic scheme to ensure proper security in the system. The proposed scheme has been proven secure against major known attacks.
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.
An efficient fuzzy rule-based big data analytics scheme for providing healthcare-as-a-service
(IEEE, 2017) Dua, Amit
With advancements in information and communication technology (ICT), there is an increase in the number of users availing remote healthcare applications. The data collected about the patients in these applications varies with respect to volume, velocity, variety, veracity, and value. To process such a large collection of heterogeneous data is one of the biggest challenges that needs a specialized approach. To address this issue, a new fuzzy rule-based classifier for big data handling using cloud-based infrastructure is presented in this paper, with an aim to provide Healthcare-as-a-Service (HaaS) to the users located at remote locations. The proposed scheme is based upon the cluster formation using the modified Expectation-Maximization (EM) algorithm and processing of the big data on the cloud environment. Then, a fuzzy rule-based classifier is designed for an efficient decision making about the data classification in the proposed scheme. The proposed scheme is evaluated with respect to different evaluation metrics such as classification time, response time, accuracy and false positive rate. The results obtained are compared with the standard techniques to confirm the effectiveness of the proposed scheme.
An efficient scheme for path planning in internet of drones
(IEEE, 2019) Dua, Amit
The Internet of Drones (IoD) is a multi-layered, control architecture to regulate and coordinate the navigation of Unmanned Aerial Vehicles (UAV) in a shared public airspace. UAVs have the potential to be employed in public space for pur- poses like surveillance, monitoring, package delivery, emergency services, etc. For proper operation, an efficient path planning among IoD is required so that they can adaptively decide their path for data dissemination. Most of the existing solutions for this problem have made unreasonable assumptions and do not offer scalability. The scheme proposed in the paper provides a network architecture for the scalable solution of UAVs in an urban environment addressing issues of path planning, safety, privacy, and network connectivity. The scheme has been tested using exhaustive simulation and results prove that the proposed scheme is efficient in terms of reducing the overall cost and delivery time with the increasing weight of payload in the drones.
Efficient TDMA based virtual back off algorithm for adaptive data dissemination in VANETs
(IEEE, 2014-01) Dua, Amit
With an exponential growth of Internet related technologies, there is an emergence of new class of efficient data dissemination using Vehicular Ad Hoc networks for the safety of peoples. As there are limited number of channels in IEEE 802.11 a/b/g/p standards, so efficient mechanisms are required to allocate the same for new incoming requests from the users. Keeping in view of the same, we propose a modified TDMA based virtual back off algorithm for VANETs. Vehicles are assumed to be arrived using Poisson distribution and served as exponential. By varying the distance from RSUs, we have provided the analysis of end-to end delay for varying speed of vehicles.
An energy efficient data dissemination and information retrieval scheme for VANET
(IEEE, 2016-02) Dua, Amit
With the increasing scarcity of resources, the need for energy efficient solutions has reached its pinnacle. Vehicular ad hoc networks (VANETs) is no exception and there is a need for exclusive solutions for energy efficient data dissemination and information retrieval. The proposed scheme classifies the vehicles into different types based on their storage and computation capacity. Cluster heads (CHs) are elected for each energy zone according to type of vehicle, and act as information center. Apart from CHs, the dissemination of data and access of information in efficient and correct manner is made possible with the help of vehicles having high processing capability. The broadcast storm problem that consumes high energy in terms of bandwidth, is solved using proposed game theoretic decision model. The proposed model gives suggestion to vehicles whether to disseminate the information or not based on the current energy situation of the network. Simulation results prove that the proposed model saves energy and time by minimizing the overhead and delay. The network energy is efficiently utilized by achieving higher packet delivery ratio and lower delay than other state-of-art protocols.
Energychain: enabling energy trading for smart homes using blockchains in smart grid ecosystem
(ACM Digital Library, 2018) Dua, Amit
The amalgamation of information and communication technologies in power industry has led to a revolution known as smart grid (SG). The energy consumers interact with the power utility using a bidirectional communication channel for energy trading in SG ecosystem. However, the traditional energy trading mechanisms strongly rely on trusted third parties which act as a single point of failure. Therefore, it is important to equip SG with a decentralized and secure energy trading system which can execute contracts and handle negotiations among various trading parties. Hence, in this paper, EnergyChain, a blockchain model for storing and accessing the data generated by smart homes in a secure manner is proposed. EnergyChain works in following phases: 1) a miner node is selected on the basis of power capacity of various smart homes, 2) a block creation and validation scheme is presented, and 3) a transaction handling mechanism is designed for secure energy trading. After evaluation, the superiority of EnergyChain is validated. The results obtained show that EnergyChain outperforms the traditional scheme in terms of communication costs and computation time.
Enhancing flow security in RYU controller through set operations
(IEEE, 2017) Dua, Amit
Software-Define Networking (SDN) is a new generation architecture for managing and controlling the network in cost-effective and efficient way. This architecture segregates the network such that forwarding and control plan is now decoupled. The network is divided into three planes: Application plane, Control plane, and Data plane. SDN provides a programming interface with which a network administrator can modify the flow of data through flow rules. With these benefits and dynamism, it also open new security threats and challenges. In this paper, the possible threats at various SDN layers are discussed. Following which a possible solution to one of security threat proposed. The proposed solution based on set operation has been proven to be secure and practically applicable