Blockchain-Based Secure Architecture for Electronic Health Records Management using Smart Contracts and Attribute-Based Encryption
Optimized Hybrid Encryption Framework for Securing Real-Time Route Information in Intelligent Vehicular Networks
Healthcare Institutions Security Posture: An Open-Source Security Architecture Practical Implementation
Digital Arrest in India: Driven Cybersecurity for National Digital Security
A Dual-Layer Approach for Image and Data Encryption using Logistic Map using Python
Barriers to Cashless Payment Adoption
Exploring Natural Language Processing Chatbots and Phishing Website Detection: A Literature Perspective
Digital Arrest in India: Driven Cybersecurity for National Digital Security
A Growth of Artificial Intelligence in Crime Detection usages in Law Enforcement
A Dual-Layer Approach for Image and Data Encryption using Logistic Map using Python
An Extensive Overview on Dark Web
Electronic Health Records (EHRs) are essential for modern healthcare systems but continue to face significant challenges related to security, interoperability, and patient-centered control. This paper presents a novel Blockchain-based Electronic Health Records Abstract System (BEAS) that integrates Ethereum smart contracts with Attribute-Based Encryption (ABE) and the Elliptic Curve Integrated Encryption Scheme (ECIES) to strengthen data security and enhance patient autonomy. The system utilizes the InterPlanetary File System (IPFS) for efficient off-chain storage and employs Attribute-Based Access Control (ABAC) to ensure scalable and flexible data access. Experimental results show a 30% reduction in transaction execution time compared to conventional systems, along with improved access control and data integrity. Future enhancements will focus on optimizing encryption performance, expanding the roles of healthcare providers such as nurses, and exploring scalable storage solutions to further improve system efficiency.
Securing route information in Vehicular Ad-Hoc Networks, VANETs, is essential for maintaining privacy, data integrity, and real-time responsiveness in vehicular communications. This research introduces an enhanced hybrid encryption system that integrates Advanced Encryption Standard, AES, with Elliptic Curve Cryptography, ECC. The model demonstrates high efficiency with AES for symmetric encryption and employs ECC for secure asymmetric key exchange, thereby decreasing computational overhead compared to conventional RSA-based systems. The performance measurements underscore the framework's benefits. The execution time for encryption and decryption is significantly reduced compared to RSA+ECC hybrids. Memory consumption is optimized, requiring less space than similar methods. Energy usage is lower, making it suitable for battery-limited situations like electric and autonomous vehicles. Experimental findings indicate the framework's resilience in preserving data integrity in dynamic VANET contexts. Moreover, latency is minimized, facilitating real-time applications such as collision avoidance and traffic management. The findings confirm the AES+ECC paradigm as a scalable and efficient solution for VANETs, offering improved security and performance while addressing key challenges in intelligent transportation systems.
The healthcare industry is a critical sector that demands robust cybersecurity measures to protect sensitive data and infrastructure. With the rapid evolution of cyber threats, attack vectors, and adversarial strategies, organizations and governments face significant challenges in ensuring data security. While extensive research exists on cybersecurity threats, breaches, and the efficacy of open-source security tools, their practical implementation in real-world healthcare settings particularly where financial constraints exist remain underexplored. This study proposes an Open Security Operations Center (SOC) architecture to strengthen cybersecurity in the healthcare domain, specifically addressing the protection of Personally Identifiable Information (PII). The proposed architecture will be rigorously tested for performance, and the results will be analyzed to assess its effectiveness. The findings will contribute to developing security frameworks that enhance the cyber resilience of small and medium-scale healthcare institutions while addressing the financial and operational challenges inherent in the cybersecurity landscape.
India's rapid digital transformation has led to a significant increase in cybercrimes, including deepfake-enabled scams such as Digital Arrest fraud, highlighting the urgent need for advanced security solutions. This paper presents a novel Artificial Intelligence (AI)-driven cybersecurity framework specifically designed for real-time deepfake detection and anomaly analysis. The system employs advanced machine learning (ML) and deep learning (DL) techniques to identify inconsistencies in multimedia content, such as facial discrepancies, and detect unusual user behaviors, like suspicious financial transactions. Hypothetical results demonstrate that this AI approach yields superior threat detection rates, such as >95%, and significantly reduced false positives and response times, thereby minimizing financial losses and data breach costs. To address privacy concerns, the study emphasizes privacy-preserving AI methods. Future research will focus on enhancing AI model interpretability and exploring hybrid human-AI systems to contribute to safer digital environments and support sustainable digital transformation.
Image encryption combined with data encryption has emerged as a critical tool for safeguarding sensitive information and maintaining secrecy. This study describes how to incorporate hidden text data within an image and encrypt it using complex techniques like logistic maps. This paper presents a secure approach for data and image encryption using pixel intensity manipulation and pixel shuffling. The suggested technique ensures that the original image may be recovered after decryption, while the secret data is kept safely hidden. The performance, benefits, and security analysis of the proposed encryption scheme are all explored in detail.
The cashless payment system has experienced significant growth within the banking industry due to its convenience and numerous benefits, which makes cashless payments more appealing to consumers than traditional cash transactions. Despite these advantages, customers face barriers that hinder the full adoption of cashless payment systems. At the same time, customer trust plays a vital role in the adoption process, with factors like reliability, transparency, security, privacy, and responsiveness in digital payment platforms. This research aims to examine the barriers and trust determinants of cashless payment adoption using a convenience sampling method with a sample size of 120 respondents from Kanyakumari District. The results indicate that security concerns, digital illiteracy, and inadequate infrastructure are the primary barriers, while privacy, security measures, responsiveness, and reliability are critical in building trust among users. The study suggests various measures such as improving infrastructure, enhancing security features, and educating customers to promote the growth and adoption of cashless payment systems. Addressing these barriers and enhancing the understanding of customer trust will foster the rapid growth and adoption of cashless payment systems.
