iFogSUMO: An Integrated Platform of iFogSim and SUMO to Enhance Simulation Capabilities of Adaptive Traffic Control for Smart City Applications Ashwini Matange, Jibi Abraham International Journal of Online and Biomedical Engineering, 2026 The increasing demand for smart city applications and Internet of Things (IoT) solutions has led to a growing need for simulation tools that can accurately model and analyze complex systems. iFogSim and Simulator of Urban Mobility (SUMO) are two popular simulation tools that cater to different aspects of smart city and IoT applications. iFogSim focuses on fog computing and IoT simulations, while SUMO specializes in traffic and mobility simulations. The integration of iFogSim and SUMO simulators can enable researchers and developers to design and optimize smart city applications more effectively by providing a robust testbed. This paper proposes integrating iFogSim and SUMO to create a comprehensive simulation framework, iFogSUMO, that accurately models and analyzes adaptive traffic control systems. The framework was evaluated for ambulance routing by simulating traffic with various vehicle types and executing the adaptive traffic control algorithm to assess its performance in terms of trip time, waiting time, and time loss during ambulance routing, while maintaining scalability across heterogeneous vehicle fleets.
Pre-Build OSS Compliance: Automated Dependency, License, and CVE Detection Vaishnavi Gurav, Jibi Abraham Apci 2025 2025 International Conference on Advancements in Power Communication and Intelligent Systems, 2025 Open-source software (OSS) is a critical component of modern software development, demanding robust compliance mechanisms for dependency management, license, and vulnerability detection. This paper presents an automated framework that enhances Software Composition Analysis at the pre-build stage by tracking transitive dependencies, improving license compliance by identifying licenses in files, and enhancing Common Vulnerability and Exposure (CVE) detection by integrating multiple data sources. The proposed method automates dependency identification for Python, Java, and Go, leveraging a multi-database approach to improve vulnerability detection accuracy. The framework's effectiveness is evaluated against Dependabot using its Security Alerts and SBOM export function. The study examines the most-starred GitHub projects with security alerts for each programming language. Given that no single CVE database is exhaustive, Precision, Recall, and F1-score are computed in the absence of ground truth using a probabilistic approach to assess detection performance. The findings highlight the importance of aggregating multiple vulnerability sources for comprehensive security coverage and adopting advanced license scanning techniques for improved compliance management. Despite challenges such as external data source rate limits, the proposed method provides a more effective solution for OSS compliance at the pre-build stage.
Lossless and Scalable Secret Image Sharing with Extended Bit-Depth Support using Polynomial Interpolation Rahul M. Bagdi, Jibi Abraham, Digambar Padulkar Indiscon 2025 IEEE 6th India Council International Subsections Conference Proceedings, 2025 Secret Image Sharing (SIS) has become a powerful technique for safeguarding sensitive images by splitting them into multiple shares, each individually meaningless yet collectively capable of reconstructing the original when a threshold number of shares is provided. This paper presents an Enhanced Lossless Secret Image Sharing (SIS) scheme designed to securely divide grayscale, color, and high-bit-depth medical images into multiple shares. The method employs polynomial interpolation over large prime fields combined with efficient bit-packing to achieve exact recovery without pixel distortion or truncation. By supporting extended bit-depths such as 24-bit color and 32-bit medical imaging, the scheme overcomes traditional limitations in lossless reconstruction, security, and computational efficiency. The proposed approach simplifies share generation while maintaining strict threshold-based secrecy, making it highly suitable for secure image sharing across critical domains like healthcare, defense, and secure multimedia transmission.
A Review of Traffic-Induced Stress Mitigation Through Adaptive Traffic Signaling and Fog Computing Simran Veer, Shruti Ravichandran, Preet Trivedi, Jibi Abraham, Ashwini Matange Proceedings of 5th International Conference on Pervasive Computing and Social Networking Icpcsn 2025, 2025 This review examines how advances in fog computing help mitigate physiological stress in urban residents by addressing one of its key contributing factors, traffic congestion. Commuters wait for hours on congested roads, with noise pollution and the feeling of helplessness adding to the frustration, the main cause being a steep increase in automobiles and a lack of traffic management. Traffic congestion can be associated with elevated cortisol levels, symptoms of anxiety and depression, and general decline in well-being. We have identified and analyzed research dealing with the implementation of adaptive traffic signals to enhance traffic flow and prioritize emergency vehicles. Various technologies, including machine learning, AI, and CNN, have been investigated. The majority of the present methods treat traffic data at the cloud level. This approach is expensive and uses significant bandwidth. Fog computing is an alternative for processing this traffic data close to the edge. The fog computing approach has reduced latency, making traffic systems smarter and cheaper. As a result, it can contribute to improving overall traffic management, with the potential to alleviate stress and enhance the mental well-being of commuters.
Defense Mechanism to Thwart Model Poisoning on Non-IID Data based Federated Learning for Credit Fraud Detection System Sneha Venkateswaran, Qasim Shaikh, Sehajdeep Singh, Jibi Abraham, Apeksha Bochare 2025 International Conference on Emerging Smart Computing and Informatics Esci 2025, 2025 The ever-growing sophistication of credit fraud, encompassing methods like identity theft, necessitates robust detection mechanisms. While machine learning algorithms analyze transaction data for fraud patterns, they struggle with complex schemes, fairness, and privacy concerns. This paper proposes a federated learning approach to address these challenges, leveraging the inherent heterogeneity of transaction data. By partitioning data based on merchant categories and geographical regions, this approach enables collaborative model training tailored to each category’s unique characteristics without compromising data privacy. Moreover, federated learning adapts models to diverse transaction contexts, effectively addressing the non-IID nature of the data and enhancing fraud detection across different segments. Furthermore, countermeasures against model poisoning attacks are incorporated to ensure the integrity and efficacy of the overall system.
Creating an Experimental Setup in Mininet for Traffic Flow Collection During DDoS Attack Shreya Jare, Jibi Abraham 2024 8th International Conference on Computing Communication Control and Automation Iccubea 2024, 2024 The proliferation of network-enabled devices and the increasing reliance on digital services have underscored the importance of effective network monitoring and security measures. DDoS attacks continue to pose significant threats to network availability and performance, targeting critical infrastructure and disrupting business operations. This research paper presents a detailed methodology for constructing an experimental setup using Mininet to simulate and collect traffic flow data during Distributed Denial of Service (DDoS) attacks. The paper outlines the steps involved in setting up the network environment, configuring traffic generation, launching DDoS attacks, and collecting traffic flow data for analysis. The proposed setup provides researchers with a controlled environment to study DDoS attack behaviors and evaluate mitigation strategies.
Unitary Operator Based Quantum Secure Access Structure for Medical Image Sharing Digambar Padulkar, Jibi Abraham IEEE Region 10 Annual International Conference Proceedings TENCON, 2024 Ensuring the secure storage and sharing of medical records in the cloud is increasingly crucial due to vulnerabilities in classical encryption methods, such as key manipulation and cloud collusion. This paper introduces a novel quantum-based approach using Quantum Fourier Transform (QFT) and Unitary operators. We designed three quantum circuits for different image sizes: one for the Unitary operator, one for encryption, and one for decryption. DICOM images are first transformed into quantum images with the NEQR algorithm, then encrypted using a Unitary operator, which serves as the encryption key. This key is securely transmitted via quantum teleportation, enhancing protection against classical and quantum attacks. Extensive simulations show the method's effectiveness and robustness in safeguarding medical data during cloud storage and transmission. This makes it a promising solution for secure medical image sharing in quantum communication networks.
Binary Voting Protocol Using Quantum Secret Sharing Pragati Bhale, Digambar Padulkar, Jibi Abraham 7th IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions Csitss 2023 Proceedings, 2023
Resilient Fog-Based Adaptive Traffic Control System Ashwini Matange, Varun Taneja, Aarya Chaumal, Pallavi Buwa, Jibi Abraham 2023 14th International Conference on Computing Communication and Networking Technologies Icccnt 2023, 2023
Composite Privacy Preservation in Location Based Advertisement Aayush Yadav, Deepika Goyal, Kishan Patel, Jibi Abraham, Ashwini Matange Proceedings of International Conference on Computing Communication Security and Intelligent Systems Ic3sis 2022, 2022
A hybrid cloud approach for deduplication with attribute-based encryption International Journal of Innovative Technology and Exploring Engineering, 2019
Privacy Preserving Data Aggregation on Secure Cloud Saket Komawar, Mayur Batwal, Shubham Shah, Snehkumar Shahani, Jibi Abraham Proceedings 2018 4th International Conference on Computing Communication Control and Automation Iccubea 2018, 2018
An inter-VM communication model supporting live migration Mrugani Kurtadikar, Apurva Patil, Pooja Toshniwal, Jibi Abraham Proceedings 2013 International Conference on Cloud and Ubiquitous Computing and Emerging Technologies Cube 2013, 2013
Simulation of distributed key management protocols in clustered wireless sensor networks International Journal of Security and Its Applications, 2007
Security protocols for Wireless Sensor Networks based on Tiny Diffusion and Elliptic curves Proceedings of the IASTED International Conference on Networks and Communication Systems 2006, 2006
