BITS Faculty Publications

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Author: search.filters.author.Alladi, Tejasvi

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    An efficient and scalable byzantine fault tolerant consensus for vehicular networks
    (IEEE, 2025) Alladi, Tejasvi
    Vehicular networks represent a new distributed system paradigm that requires robust fault tolerance to ensure reliable operation. As a burgeoning area of research, the scalability and optimization of consensus mechanisms for these networks are critical. Traditional Byzantine Fault Tolerant (BFT) algorithms like PBFT are not inherently optimized for the localized needs of vehicular networks, suffering from scalability issues due to their global nature and high messaging complexity. In response, we introduce a two-tiered consensus framework that refines PBFT for the specific context of vehicular networks. By organizing nodes into clusters based on geographic proximity, our approach reduces messaging complexity from O(n2) to O(n1.5), significantly improving scalability. The framework distinguishes between local and global state transitions, adding two phases to the PBFT protocol to manage these efficiently. This tailored consensus process aligns with the localized communication patterns of vehicular networks, enhancing both efficiency and scalability. The framework addresses the critical challenges of traditional BFT algorithms in vehicular networks, offering a solution that is both scalable and resilient. It is a step toward enabling vehicular networks to fulfil their potential as a reliable component of modern distributed systems.
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    VADGAN: An Unsupervised GAN Framework for Enhanced Anomaly Detection in Connected and Autonomous Vehicles
    (IEEE, 2024-09) Narang, Pratik; Alladi, Tejasvi
    The utilization of Connected and Autonomous Vehicles (CAVs) is on the rise, driven by their ability to provide vehicular services such as enhancing vehicle safety, aiding in intelligent decision-making, and ensuring continuous operation. CAVs achieve their objectives by employing wireless Vehicle-to-Everything (V2X) communication within Intelligent Transportation Systems (ITS) to establish connections with vehicles within the same network and roadside units. However, it has been observed that certain vehicles violate network constraints by transmitting erroneous messages, resulting in abnormal behaviour. Consequently, there is a growing need for a system that can verify the accuracy of information broadcast by each vehicle regarding its vehicle coordinates (along with relevant data depending on the application) at designated frequencies and under authorized pseudo-identities. Addressing the limitations faced by prior generative AI model applications, such as Variational Autoencoders (VAEs), this paper presents an unsupervised anomaly detection framework using Generative Adversarial Networks (GANs) optimized for CAVs. Our framework tested across LSTM, RNN, and GRU architectures shows superior performance with LSTM, focusing on vehicle dynamics–position, speed, acceleration, and heading–to effectively identify 11 specific attack types, marking a significant advancement in anomaly detection for CAVs.
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    HybridSecNet: In-Vehicle Security on Controller Area Networks Through a Hybrid Two-Step LSTM-CNN Model
    (IEEE, 2024-06) Alladi, Tejasvi; Chamola, Vinay
    The modern Intelligent Vehicle (IV) is a complex technological marvel that heavily relies on the Controller Area Network (CAN) bus system to enable seamless communication among different electronic control units (ECUs). However, the CAN bus system lacks security mechanisms for authentication and authorization, leaving it vulnerable to various attacks. Malicious actors can freely broadcast CAN messages without protection, making the system susceptible to DoS, Fuzzing, and Spoofing attacks. Therefore, it is crucial to devise methods to safeguard modern vehicles from such threats. In this research paper, we introduce HybridSecNet, A hybrid two-step LSTM-CNN Model for Intrusion Detection, a deep learning-based architecture specifically designed to bolster in-vehicle security on Controller Area Networks (CAN). HybridSecNet comprises two stages of classification: the first stage employs long short-term memory (LSTM) to categorize input data as either normal or attacked, and the second stage further classifies the attacks into specific types using Convolutional Neural Networks (CNN). This two-step approach significantly enhances classification accuracy and reliability, yielding remarkable results with accuracy, precision, recall, and an F1-score of approximately 99.5% for CAN bus network attacks. Comparative analyses with existing single-step models underscore the superiority of our proposed model, demonstrating its potential to revolutionize in-vehicle security in the realm of modern intelligent vehicles.
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    Detecting UAV Presence Using Convolution Feature Vectors in Light Gradient Boosting Machine
    (IEEE, 2022-12) Alladi, Tejasvi; Chamola, Vinay
    The growing number of Unmanned Aerial Vehicle (UAV) applications brings with it, a rising number of privacy concerns. The high availability of commercial drones is also increasing the need for strict regulations. As far away as we are from establishing such protocols to ensure that the most basic human right to privacy is not exploited, we are further away from enforcing them. Thus, there is a need for a generalised drone detection system to detect different drones operating in a broad range of Radio Frequencies (RF). Previous attempts to tackle this problem have been made using audio, video, radar, WiFi and RF signals. While all these methods have their own benefits and drawbacks, RF has various characteristics which make them suitable for practical applications on a large scale. In this paper, we propose a novel technique called the ConvLGBM model which combines the feature extraction capability of a Convolution Neural Network (CNN) with the high classification accuracy of the Light Gradient Boosting Machine (LightGBM). We develop and evaluate the classifications done by an optimal CNN and the LightGBM model and then compare both models with the ConvLGBM.
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    Ambient Intelligence for Securing Intelligent Vehicular Networks: Edge-Enabled Intrusion and Anomaly Detection Strategies
    (IEEE, 2023-03) Alladi, Tejasvi; Chamola, Vinay
    The Internet of Things (IoT) is increasingly being deployed in smart city applications such as vehicular networks. The presence of a large number of communicating vehicles greatly increases the number and types of possible anomalies in the network. These anomalies could range from faulty vehicular data being broadcast by the vehicles to more catastrophic attacks such as disruptive attacks and Denial of Service (DoS) attacks to name a few. This calls for a need to develop robust security schemes such as intrusion detection and anomaly detection schemes. With a humongous growth in the amount of vehicular traffic data expected, artificial intelligence (AI)-based detection strategies need to be developed to address this burgeoning demand. In this article, we propose three AI-based intrusion detection strategies for vehicular network applications, leading to an effective Ambient Intelligence based vehicular network paradigm. The detection tasks are run on local edge servers deployed at the network edge. By showing the prediction results on an experimental testbed emulating the edge servers, we show the feasibility of deploying the proposed strategies in the vehicular network scenario.
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    A deep learning based misbehavior classification scheme for intrusion detection in cooperative intelligent transportation systems
    (Elsevier, 2022-07) Alladi, Tejasvi; Chamola, Vinay
    With the rise of the Internet of Vehicles (IoV) and the number of connected vehicles increasing on the roads, Cooperative Intelligent Transportation Systems (C-ITSs) have become an important area of research. As the number of Vehicle to Vehicle (V2V) and Vehicle to Interface (V2I) communication links increases, the amount of data received and processed in the network also increases. In addition, networking interfaces need to be made more secure for which existing cryptography-based security schemes may not be sufficient. Thus, there is a need to augment them with intelligent network intrusion detection techniques. Some machine learning-based intrusion detection and anomaly detection techniques for vehicular networks have been proposed in recent times. However, given the expected large network size, there is a necessity for extensive data processing for use in such anomaly detection methods. Deep learning solutions are lucrative options as they remove the necessity for feature selection. Therefore, with the amount of vehicular network traffic increasing at an unprecedented rate in the C-ITS scenario, the need for deep learning-based techniques is all the more heightened. This work presents three deep learning-based misbehavior classification schemes for intrusion detection in IoV networks using Long Short Term Memory (LSTM) and Convolutional Neural Networks (CNNs). The proposed Deep Learning Classification Engines (DCLE) comprise of single or multi-step classification done by deep learning models that are deployed on the vehicular edge servers. Vehicular data received by the Road Side Units (RSUs) is pre-processed and forwarded to the edge server for classifications following the three classification schemes proposed in this paper. The proposed classifiers identify 18 different vehicular behavior types, the F1-scores ranging from 95.58% to 96.75%, much higher than the existing works. By running the classifiers on testbeds emulating edge servers, the prediction performance and prediction time comparison of the proposed scheme is compared with those of the existing studies.
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    Blockchain in Smart Grids: A Review on Different Use Cases
    (MDPI, 2019) Alladi, Tejasvi; Chamola, Vinay
    With the integration of Wireless Sensor Networks and the Internet of Things, the smart grid is being projected as a solution for the challenges regarding electricity supply in the future. However, security and privacy issues in the consumption and trading of electricity data pose serious challenges in the adoption of the smart grid. To address these challenges, blockchain technology is being researched for applicability in the smart grid. In this paper, important application areas of blockchain in the smart grid are discussed. One use case of each area is discussed in detail, suggesting a suitable blockchain architecture, a sample block structure and the potential blockchain technicalities employed in it. The blockchain can be used for peer-to-peer energy trading, where a credit-based payment scheme can enhance the energy trading process. Efficient data aggregation schemes based on the blockchain technology can be used to overcome the challenges related to privacy and security in the grid. Energy distribution systems can also use blockchain to remotely control energy flow to a particular area by monitoring the usage statistics of that area. Further, blockchain-based frameworks can also help in the diagnosis and maintenance of smart grid equipment. We also discuss several commercial implementations of blockchain in the smart grid. Finally, various challenges to be addressed for integrating these two technologies are discussed.
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    Blockchain Applications for Industry 4.0 and Industrial IoT: A Review
    (IEEE, 2019) Alladi, Tejasvi; Chamola, Vinay
    The potential of blockchain has been extensively discussed in the literature and media mainly in finance and payment industry. One relatively recent trend is at the enterprise-level, where blockchain serves as the infrastructure for internet security and immutability. Emerging application domains include Industry 4.0 and Industrial Internet of Things (IIoT). Therefore, in this paper, we comprehensively review existing blockchain applications in Industry 4.0 and IIoT settings. Specifically, we present the current research trends in each of the related industrial sectors, as well as successful commercial implementations of blockchain in these relevant sectors. We also discuss industry-specific challenges for the implementation of blockchain in each sector. Further, we present currently open issues in the adoption of the blockchain technology in Industry 4.0 and discuss newer application areas. We hope that our findings pave the way for empowering and facilitating research in this domain, and assist decision-makers in their blockchain adoption and investment in Industry 4.0 and IIoT space.
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    Applications of blockchain in unmanned aerial vehicles: A review
    (Elsevier, 2020-06) Alladi, Tejasvi; Chamola, Vinay
    The recent advancement in Unmanned Aerial Vehicles (UAVs) in terms of manufacturing processes, and communication and networking technology has led to a rise in their usage in civilian and commercial applications. The regulations of the Federal Aviation Administration (FAA) in the US had earlier limited the usage of UAVs to military applications. However more recently, the FAA has outlined new enforcement that will also expand the usage of UAVs in civilian and commercial applications. Due to being deployed in open atmosphere, UAVs are vulnerable to being lost, destroyed or physically hijacked. With the UAV technology becoming ubiquitous, various issues in UAV networks such as intra-UAV communication, UAV security, air data security, data storage and management, etc. need to be addressed. Blockchain being a distributed ledger protects the shared data using cryptography techniques such as hash functions and public key encryption. It can also be used for assuring the truthfulness of the information stored and for improving the security and transparency of the UAVs. In this paper, we review various applications of blockchain in UAV networks such as network security, decentralized storage, inventory management, surveillance, etc., and discuss some broader perspectives in this regard. We also discuss various challenges to be addressed in the integration of blockchain and UAVs and suggest some future research directions.
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    SecAuthUAV: A Novel Authentication Scheme for UAV-Ground Station and UAV-UAV Communication
    (IEEE, 2020) Alladi, Tejasvi; Chamola, Vinay
    Unmanned Aerial Vehicles (UAVs) are becoming very popular nowadays due to the emergence of application areas such as the Internet of Drones (IoD). They are finding wide applicability in areas ranging from package delivery systems to automated military applications. Nevertheless, communication security between a UAV and its ground station (GS) is critical for completing its task without leaking sensitive information either to the adversaries or to unauthenticated users. UAVs are especially vulnerable to physical capture and node tampering attacks. Further, since UAV devices are generally equipped with small batteries and limited memory storage, lightweight security techniques are best suited for them. Addressing these issues, a lightweight mutual authentication scheme based on Physical Unclonable Functions (PUFs) for UAV-GS authentication is presented in this paper. The UAV-GS authentication scheme is extended further to support UAV-UAV authentication. We present a formal security analysis as well as old-fashioned cryptanalysis and show that our protocol provides various security features such as mutual authentication, user anonymity, etc, and is resilient against many security attacks such as masquerade, replay, node tampering, and cloning attacks, etc. We also compare the performance of our protocol with state-of-the-art authentication protocols for UAVs, based on computation, communication, and memory storage cost.