Journal of Cryptography and Network Security, Design and Codes

A Study on Cryptographic Algorithms for Data Confidentiality and Integrity

2025-02-11T12:02:39+00:00

In today’s digital landscape, safeguarding sensitive information during transactions has become increasingly critical. This project focuses on developing a secure data exchange framework by integrating cryptographic techniques, blockchain technology, and steganography. Inspired by blockchain-based asset management systems and decentralized transaction models, the study employs cryptographic methods like SHA-256 hashing, Proof of Work (PoW), and Proof of Stake (PoS) to ensure data integrity, authenticity, and confidentiality.
To further enhance security, the project incorporates multimodal steganography and Least Significant Bit (LSB) techniques for embedding sensitive information within digital media. The combined strengths of blockchain’s immutable ledger and the covert nature of steganography create a system that prioritizes end-to-end security, privacy, and resistance to tampering. The methodology involves creating a decentralized peer-to-peer network for validating transactions, using advanced encryption techniques, and applying steganographic methods to conceal data within multimedia formats.
Testing demonstrates the system’s ability to prevent data breaches, support secure transactions, and withstand cyberattacks. This innovative hybrid model offers a reliable solution for secured digital communications and transactions across finance, healthcare, and e-commerce domains. Future advancements could explore quantum-resistant cryptographic approaches and AI-driven mechanisms to detect and mitigate fraud.

Cybersecurity in e-Governance: A Comprehensive Review

2025-04-10T11:58:19+00:00

The digitization of government services, termed e-government, has redefined public administration by enhancing operational efficiency, transparency, and citizen engagement. However, this transformation has also exposed critical vulnerabilities to cyber threats, ranging from data breaches to sophisticated state-sponsored attacks. This paper provides an in-depth examination of the cybersecurity challenges inherent in e-government systems, analyzing vulnerabilities, threat vectors, and mitigation strategies through a synthesis of academic research, global case studies, and industry frameworks. Key findings underscore the persistent risks posed by legacy infrastructure, insufficient cybersecurity awareness, and resource limitations while highlighting the potential of emerging technologies such as Artificial Intelligence (AI) and blockchain to strengthen defenses. The study advocates for a holistic approach combining robust policy frameworks, public-private collaboration, and continuous workforce training to safeguard digital governance. By addressing these imperatives, governments can mitigate risks, protect sensitive citizen data, and sustain public trust in an increasingly interconnected world.

A Study on the Role of Machine Learning Techniques in Modelling and Fraud Detection in E-bidding Systems

2025-04-29T01:17:01+00:00

E-bidding has become a crucial element in modern procurement and auction systems, ensuring transparency, efficiency, and competitiveness. The integration of machine learning (ML) into E-bidding processes enhances bid evaluation, fraud detection, and decision-making. This paper explores the application of ML techniques such as predictive modelling, anomaly detection, and natural language processing (NLP) to optimize bidding strategies, assess bidder credibility, and prevent fraudulent activities. By leveraging ML algorithms, E-bidding systems can automate complex evaluations, ensuring optimal outcomes for buyers and sellers. The study highlights the impact of AI-driven decision-making in E-bidding and discusses future advancements in this domain. Machine learning has revolutionized various aspects of e-bidding processes and systems, enhancing efficiency, fairness, and decision-making. This response explores the applications of machine learning across different dimensions of e-bidding, including auction design, personalized bidding strategies, real-time bidding, and multi-agent systems. The discussion is supported by insights from relevant research papers, highlighting the advancements and innovations in this field.

Unveiling Contemporary Cyber Threats and Tactics to Fight Back

2025-04-29T01:29:17+00:00

Cyber dangers have grown more complex in the digital era, presenting serious hazards to people, businesses, and countries. Any hostile activity intended to jeopardise the availability, confidentiality, or integrity of digital systems, networks, or data is referred to as a cyber-threat. A variety of actors, including hackers, cybercriminals, and internal insiders, are the source of these dangers. The most common dangers are Denial-of-Service (DoS) assaults, which try to overwhelm and render targeted systems inoperable, malware (such viruses and worms), and cyber stalking. Organisations must implement a thorough cyber security strategy in order to successfully combat these changing threats. Implementing firewalls, using cutting-edge antivirus software, and Multi-Factor Authentication (MFA) to protect access controls are important defensive strategies. In order to fix known vulnerabilities, regular software upgrades and security patches are also crucial. Regular cyber security awareness training is equally vital since it helps to create a security-conscious staff, which greatly reduces threats like phishing attempts. Furthermore, in order to detect and address breaches as soon as they happen, Intrusion Detection Systems (IDS) and real-time network monitoring are essential. For the resilience and security of digital assets, a proactive, multi-layered defense approach is still essential as cyber threats continue to change.

Application of Information Theoretical Principles for Detection and Prevention of Waterhole Attacks: An Investigative Study

2025-04-25T05:01:07+00:00

Waterhole attacks have emerged as a stealthy and effective cyber threat, targeting specific groups by compromising trusted websites. Traditional detection mechanisms often fail to identify these attacks due to their low visibility and reliance on legitimate sites. This study investigates the application of information theoretical principles including entropy, mutual information, and divergence metrics to detect and prevent waterhole attacks. By measuring randomness, quantifying correlation, and identifying distribution drifts, information-theoretic approaches offer robust anomaly detection capabilities. The research highlights how entropy-based detection can identify irregular traffic patterns, while mutual information reveals deviations in web traffic relationships, signaling potential malicious activity. Furthermore, Kullback-Leibler Divergence (KLD) effectively captures distribution changes in network behavior, aiding in early detection. The study also explores real-world case studies and evaluates the performance of information-theoretic methods, demonstrating their accuracy and resilience in identifying stealthy attack patterns. The findings suggest that applying information theory enhances the detection of low-profile, targeted attacks by distinguishing between legitimate and anomalous activities. This study underscores the potential of adaptive, information-theoretic defense mechanisms to improve cybersecurity resilience against waterhole attacks, making them a promising solution for advanced intrusion detection systems (IDS) and threat prevention frameworks.