BITS Faculty Publications
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Item Bayesian deep learning meets self-attention: a risk-aware approach to advertisement optimization(IEEE, 2025-05) Bhatia, Ashutosh; Tiwari, KamleshIn the highly competitive landscape of e-commerce advertising, maximizing Return on Advertising Spend (ROAS) is critical, yet remains inherently uncertain due to auction-based bidding dynamics and fluctuating market conditions. Traditional deterministic models fail to capture this uncertainty, necessitating a probabilistic approach that balances predictive accuracy with interpretability. To address this challenge, the paper proposes a novel Hierarchical Bayesian Deep Learning framework that integrates a Bayesian Belief Network (BBN) for structured probabilistic reasoning and a Mixture Density Network (MDN) for full distributional modeling of ROAS. The BBN models dependencies among campaign variables, offering interpretable insights, while the hierarchical deep learning architecture overcomes scalability limitations in high-dimensional settings through self-attention mechanisms. Experiments demonstrate up to 22.8% lower RMSE and 27.4% better Negative Log Likelihood (NLL) and up to 31.2% lower Kullback-Leibler divergence (KLD) than state-of-the-art methods (DeepAR, Prophet, NGBoost), achieving an R2 of 98% with an inference speed of 5.2 ms per campaign, making real-time bidding feasible. Ablation studies confirm that attention-driven feature selection and calibrated uncertainty quantification significantly enhance both predictive performance and explainability, identifying key drivers of campaign success. By providing precise, uncertainty-aware, and explainable predictions, this approach enables adaptive bidding strategies, optimized budget allocation, and risk management, setting a new benchmark for intelligent decision-making in digital advertising.Item Deep learning approaches for driver distraction detection using driver facing cameras: literature review and empirical study using cnn classifiers on a 100-driver image dataset(2025-05) Bhatia, Ashutosh; Sharma, Yashvardhan; Tiwari, KamleshDistracted driving contributes to thousands of fatalities and injuries globally. According to India’s Ministry of Road Transport and Highways (MoRTH), distraction-related behaviors such as rear-end and off-road collisions accounted for nearly one-fourth of all traffic incidents in 2022. The U.S. National Highway Traffic Safety Administration (NHTSA) reported 3,275 deaths and over 324,000 injuries from distraction-related crashes in 2023. In Europe, the European Road Safety Observatory (ERSO) observed handheld phone use by drivers in up to 9.4% of vehicles across member states, with self-reported texting rates reaching 53%. Despite numerous studies and surveys on driver distraction detection, existing literature remains fragmented, often combining multiple sensor modalities or distraction with related driver states such as fatigue. Prior empirical efforts also lack a unified benchmarking strategy to assess model generalization under shifts in viewpoint or spectral input. This paper presents a focused survey and empirical study of visiononly distraction detection using deep learning models applied to driver-facing camera inputs. It introduces a conceptual model linking behavioral cues to cognitive distraction, defines the visionbased Driver Distraction Detection (vDDD) system with alert logic, and develops structured taxonomies of datasets, architectures, and learning strategies. Using the 100-Driver dataset, the empirical study evaluates 26 CNN classifiers under 64 crossdomain configurations, systematically analyzing generalization across modality and camera view changes. Results show that frontal RGB-trained models generalize better than their NIRtrained counterparts and that lightweight models trade off accuracy under rare class scenarios for faster inference. The study establishes the vDDD paradigm as a vision-based behavioral modeling approach for distraction detection using driver-facing camera data. It outlines future research directions in spectrumaligned augmentation, attention modeling, and lightweight visuallanguage fusion, emphasizing deployment-focused strategies such as quantization, contrastive learning, and progressive fine-tuning.Item PUF-AQKD: a hardware-assisted quantum key distribution protocol for man-in-the-middle attack mitigation pdf(IEEE, 2025-05) Bhatia, Ashutosh; Bitragunta, Sainath; Tiwari, KamleshThe Quantum Key Distribution (QKD) protocol utilizes quantum mechanics principles for cryptographic key exchange, ensuring absolute secrecy. Current QKD techniques are susceptible to man-in-the-middle (MITM) attacks due to the absence of an inherent mechanism for identity verification within the quantum channel. For authentication, these systems rely on classical or post-quantum cryptography, which diminishes the perfect security advantage provided by QKD. We present a Physical Unclonable Function (PUF)-based authenticated QKD protocol (PUF-AQKD), which avoids the necessity for authenticated classical channels and is useful in mitigating MITM attacks. The fundamental concept of PUF-AQKD is to implement a phase shift in the basis used for polarizing the transmitted qubits. The phase shift is dictated by PUFs, which are anticipated to result in analogous (correlated) responses for devices manufactured under similar conditions but dissimilar responses in different conditions. An adversary lacking a correlated PUF response shared by Alice and Bob would inadvertently increase the Quantum Bit Error Rates (QBER) observed at Bob’s end. We present a mathematical model to assess the efficacy of the proposed PUF-AQKD method and perform simulations utilizing the NetSquid simulator. The mathematical analysis and simulation findings indicate that PUF-AQKD can efficiently identify eavesdroppers, even during incomplete measurements, without the necessity of an authorized classical channel.Item Adaptive RIS design and optimization for cooperative ris-assisted wireless systems(IEEE, 2025-07) Bitragunta, Sainath; Bhatia, AshutoshWe propose an adaptive RIS-based cooperative transmission strategy that jointly selects one of two RIS paths and dynamically optimizes the number of active meta-atoms to maximize physical layer (PHY) secrecy capacity under a total average power constraint. Unlike existing approaches that fix the RIS size K or assume identical fading on all links, our framework uses long-term statistics to probabilistically choose between two RISs (upper or lower) with arbitrary first-hop fading, and leverages instantaneous channel state information (CSI) on the selected path to solve a convex K-sizing problem via a Lagrangian multiplier approach. We derive and present the solution for optimal K, and numerically evaluate the average PHY secrecy capacity and average PHY secrecy efficiency for the proposed optimal strategy. Numerical results show that the proposed optimal-K strategy achieves up to 35% higher average PHY secrecy capacity and 50% improvement in average PHY secrecy efficiency compared to a fixed-K benchmark strategy across moderate power thresholds. Furthermore, we present an insightful asymptotic analysis for average PHY secrecy capacity in an interesting scaling regime. Our findings demonstrate the practical benefits of adaptive RIS for cooperative PHY secure and energy-efficient beyond fifth generation (B5G) wireless systemsItem Layered blockchain-based mobile crowdsensing architecture: exploring privacy and scalability challenges across layers(Springer, 2025-04) Bhatia, Ashutosh; Tiwari, KamleshBlockchain technology has emerged as a transformative solution for addressing the limitations of traditional Mobile CrowdSensing (MCS) systems, which rely on centralized architectures. Despite its promise, the integration of blockchain into MCS introduces challenges related to privacy, scalability, and system efficiency. This paper presents a comprehensive layered architecture for enhancing blockchain-based MCS systems (BMCS), focusing on two critical dimensions: privacy and scalability. By categorizing challenges and proposed mitigation strategies, the study explores privacy risks arising from blockchain transparency and evaluates privacy-preserving mechanisms, including zero-knowledge proofs, multiparty computation, and homomorphic encryption, to protect sensitive data in decentralized environments. Scalability constraints, such as limited transaction throughput and resource intensity, are presented with targeted solutions that reduce on-chain loads and improve performance. The findings contribute actionable insights to advance BMCS systems, charting a path for resilient and scalable decentralized ecosystems.Item A layered framework for blockchain security: classification of threats and the quantum computing impact(Springer, 2025-04) Bhatia, Ashutosh; Tiwari, KamleshBlockchain technology, with its transformative potential across industries, has ushered in a new era of decentralized systems. However, its widespread adoption has exposed vulnerabilities at various layers of its architecture, posing significant challenges to security and integrity. This paper introduces a comprehensive layered framework for blockchain security, classifying threats across five architectural layers: Application, Contract, Consensus, Network, and Data. By mapping vulnerabilities to these layers, the framework highlights specific attack vectors, such as Reentrancy, Sybil, Selfish Mining, and Replay attacks, and provides targeted mitigation strategies. Furthermore, the paper examines the disruptive potential of quantum computing on blockchain security, emphasizing the need for post-quantum cryptographic solutions to future-proof blockchain systems. The classification and analysis aim to guide researchers and developers in enhancing blockchain robustness. The findings contribute actionable insights into securing blockchain ecosystems and charting future research directions, including addressing interoperability challenges, optimizing smart contract security, and strengthening consensus mechanisms against evolving threats.Item Decentralized marketplace for maintenance of electric vehicles(Springer, 2025-04) Bhatia, Ashutosh; Tiwari, KamleshAs electric vehicles (EVs) become an integral part of the global transportation ecosystem, the need for efficient and cost-effective maintenance solutions will rise. This paper explores the application of game theory, specifically reverse auctions, to establish a decentralized, driver-centric marketplace for EV maintenance, promoting sustainability. The proposed system enables EV drivers to act as price determiners by launching individual smart contracts, which serve as reverse auction platforms. Maintenance providers can then bid on these contracts, competing to offer the most cost-effective services. By leveraging blockchain technology, smart contracts ensure transparency, trust, and secure fund management throughout the process. This innovative approach empowers EV drivers, reduces maintenance costs, and promotes a competitive service market. The paper outlines the underlying mechanisms, system architecture, and potential benefits of this model, along with a discussion of its implementation challenges and future implications for the EV ecosystem.Item Sybil-resilient publisher selection mechanism in blockchain-based mcs systems(IEEE, 2025) Bhatia, Ashutosh; Tiwari, KamleshIn Blockchain-based Mobile CrowdSensing (BMCS) systems, publishers (data collectors) can exploit the ability to create multiple blockchain identities, enabling Sybil attacks. Selfish, malicious, and collusive Sybil behaviors undermine both reward and majority-based data validation mechanisms, discouraging honest participation and threatening system integrity. Existing solutions often fail to address these issues, particularly in environments dominated by selfish or malicious publishers. This paper proposes a novel two-phase publisher selection mechanism to mitigate Sybil attacks in BMCS systems. Phase-I employs a modified Proof-of-Stake (PoS) mechanism with carefully calibrated parameters, including staked amount, coinage, reputation, and randomness. The strategic combination of staked amount and coinage increases the difficulty of Sybil attacks as the system scales over time. Phase-II introduces a lightweight, reputation-based Proof-of-Work (PoW) mechanism tailored for Mobile CrowdSensing (MCS) environments, where puzzle difficulty adjusts dynamically based on the publisher's reputation. Reputation and penalization mechanisms are central to the proposed mechanism, ensuring robust prevention of task domination, selfish behavior, and malicious activities while fostering honest participation. Comprehensive on-chain and off-chain simulations demonstrate the proposed mechanism's effectiveness in mitigating Sybil attacks, reducing their impact, and promoting fair participation.Item Privacy-preserving password-based authentication using zero-knowledge proofs(IEEE, 2025-03) Bhatia, Ashutosh; Tiwari, KamleshPasswords remain fundamental to user authentication, including handheld devices, wearables, personal computers, and network devices. Privacy concerns have led to the development of new password guidelines and alternatives, yet these have not seen widespread adoption among users. Increasing skepticism towards the service providers has made users reluctant to share sensitive information, including passwords. While current security protocols ensure data protection in transit, assurances regarding the security and privacy of data at rest are often assumed without verification. Traditional best practices for password storage involve hashing, which still requires the original password to be shared as plaintext or as a hash. Each of these methods has its vulnerabilities. For instance, an adversary can sniff network packets to capture the original password or the hash value, potentially compromising the authentication system. To address these issues, we propose a framework for password-based authentication using graph isomorphism as a zero-knowledge proof technique. This framework aims to replace conventional authentication methods and enhance password privacy. The results demonstrate the proposed framework's effectiveness in ensuring secure and private password authentication.Item Quantum key distribution optimization: reducing communication overhead in post-processing steps(IEEE, 2025-03) Bhatia, Ashutosh; Bitragunta, Sainath; Tiwari, KamleshQuantum Key Distribution (QKD) is a ground-breaking method in modern cryptography that uses quantum mechanics to establish secure communication channels. Unlike classical cryptographic techniques, QKD provides unconditional security based on quantum principles, such as the no-cloning theorem and the uncertainty principle. However, existing QKD systems often suffer from high overhead in key post-processing, affecting efficiency and scalability, especially in resource-constrained environments such as IoT. This paper addresses these challenges by introducing two key optimizations to enhance the efficiency and security of QKD systems. First, we propose a method using Pseudorandom Number Generators (PRNGs) to determine key bit positions for verification by Alice and Bob, significantly reducing communication over-head. Second, we employ hash-based subsequence comparison to minimize data exchange and leverage the cryptographic strength of hash functions. Results demonstrate that these strategies effectively reduce key post-processing overhead and improve the efficiency of QKD systems in real-world conditions making QKD more practical and scalable for diverse application contexts.