Journal Of Network security
Computer/IT ISSN 2395-6739 3 issues a year Hybrid open accessWhat does hybrid open access mean? This journal has two kinds of articles. Open access articles are free for anyone to read; the author pays an article processing charge for that. All other articles are for subscribers. Authors can also publish at no cost under the subscription-based option.
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About the journal
Journal of Network Security is a peer-reviewed hybrid open-access journal launched in 2013 focused on the publication of all the Research/Review work carried out for the security of the network.
Journal metrics
Counted from this archive, not supplied by anyone.
- 211Articles published
- 13Published in 2026
- 528Authors
- 3Open access
Journal information
- Title
- Journal Of Network security
- Issues per year
- 3 issues
- ISSN
- 2395-6739
- Publisher
- STM Journals
- Starting year
- 2013
- Subject
- Computer/IT
- Publication format
- Hybrid open access
- Language
- English
- Copyright policy
- CC BY-NC-ND 4.0
- Type
- Peer-reviewed journal (refereed)
- Article processing charge
- ₹10,000 in India · $400 outside India
- Subscription-based publication
- No cost. Author copy, DOI and certificate are chargeable.
Indexed in
Editorial board
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Editor-in-Chief
Department of Information Science and Engineering, RV College of Engineering, India
Latest articles
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Enhancing Data Security and Privacy in Machine Learning Using Federated
Abstract: Federated learning is a way of training machine learning models. It does this across devices or organizations without putting all the raw data in one place. This helps to keep data secure in modern artificial intelligence systems. This paper looks at how federated learning helps to keep data from being seen reduces the chance of something going wrong and lets people keep control over information. This makes the whole security …
Published in Journal Of Network security Read article
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Ensemble-Based Detection of Phishing URLs Using Machine Learning: A Cybersecurity Approach
Abstract: Phishing websites are among the most frequently used tactics by cybercriminals to trick people into revealing sensitive information, including usernames, passwords, banking details, and other personal data. As attackers continuously modify website structures and Uniform Resource Locators (URLs), conventional blacklist and signature-based detection techniques often fail to identify newly emerging phishing websites. This research introduces an ensemble machine learning method designed to identify phishing URLs more accurately and consistently. The …
Published in Journal Of Network security · Vol. 14, Issue 3, 2026 Read article
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BlockCertify: A Blockchain-Based Certificate Generation and Verification System
Abstract: The proliferation of fraudulent academic and professional credentials has become a critical challenge for educational institutions, employers, and regulatory bodies worldwide. Conventional certificate verification approaches, including paper-based records and centralized digital repositories, remain inherently vulnerable to insider manipulation, unauthorized alteration, and document forgery. These limitations necessitate a fundamentally more secure and trustless verification infrastructure. This paper introduces BlockCertify, a decentralized certificate issuance and verification platform built upon Ethereum blockchain technology. …
Published in Journal Of Network security · Vol. 14, Issue 3, 2026 Read article
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Merkle Trees in Digital Forensics: A Comprehensive Review of Chain-of-Custody Applications
Abstract: The integrity of the chain of custody is key to the admissibility of digital evidence in court, as any alteration that goes unnoticed can lead to doubts about an exhibit. Traditional methods of record keeping, that depend heavily on the human touch and procedural order, are becoming overstretched by the volume and speed of the digital evidence in today's world. A hierarchical hashing system that was proposed by Ralph Merkle, …
Published in Journal Of Network security · Vol. 14, Issue 2, 2026 Read article
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Enhancing Explainability in Machine Learning Based Spam Email Detection Using LIME and SHAP
Abstract: Spam emails represent a continual and irritating cybersecurity issue due to the fast expansion of cyberspace communication. These messages are unwanted and, in some cases, dangerous, such as phishing schemes, identity theft, and the propagation of malware. They cause severe financial and data security threats to individuals and organizations. Conventional spam detection techniques, such as rule-based spamming, blacklisting, and keyword classification, are techniques that find it difficult to match adaptive …
Published in Journal Of Network security · Vol. 14, Issue 2, 2026 · pp. 35–48 Read article
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AI-Driven Cyber Threat Detection and Response Framework for Higher Educational Institutions (HEIs)
Abstract: Higher educational institutions (HEIs) are increasingly exposed to complex cyber threats due to their open-access digital ecosystems, diverse user populations, and extensive research infrastructures. Conventional signature-based intrusion detection systems often fail to detect zero-day and evolving attacks, creating an urgent need for intelligent and adaptive security mechanisms. This research introduces an AI-based framework for detecting and responding to cyber threats, specifically designed for HEIs. The framework employs an unsupervised machine …
Published in Journal Of Network security · Vol. 14, Issue 2, 2026 · pp. 28–34 Read article
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An in-Depth Analysis of Cyber Security: Vulnerabilities, Threat Landscapes, Attack Mechanisms, and Defense Solutions
Abstract: In a highly digitalized world today, cyber security has taken over as a necessary component to safeguarding the modern world technology. With the increase in the dependency on the digital platform and online services, the threat of cyber threats has also risen. Such threats are not confined to mere attacks anymore, as they have taken a new form of complexity and magnitude to impact individuals, businesses, government institutions among others. …
Published in Journal Of Network security · Vol. 14, Issue 2, 2026 Read article
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AI-Based Cybersecurity Framework for Protecting Smart Surveillance Infrastructure in Mumbai
Abstract: Smart city infrastructures increasingly rely on interconnected surveillance systems to ensure safety, operational efficiency, and public trust. However, the rapid expansion of IoT-based monitoring technologies has introduced new cyber risks, especially in high-density metropolitan areas. This paper proposes an AI-driven cyber resilience framework targeting smart surveillance infrastructure as a critical smart-living domain, focusing on Mumbai as a case study. Using the CIC-IDS2017 dataset, a machine learning-based intrusion detection model is …
Published in Journal Of Network security · Vol. 14, Issue 1, 2026 Read article
Ahead of print
Accepted and online before they are assigned to an issue.
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Enhancing Data Security and Privacy in Machine Learning Using Federated
Abstract: Federated learning is a way of training machine learning models. It does this across devices or organizations without putting all the raw data in one place. This helps to keep data secure in modern artificial intelligence systems. This paper looks at how federated learning helps to keep data from being seen reduces the chance of something going wrong and lets people keep control over information. This makes the whole security …
Published in Journal Of Network security Read article
Issue highlights
What is in our recent issues, in plain words, with a pointer to each paper.
Archive on its own page →
Volume 14 (2026) 13 articles
Volume 13 (2025) 17 articles
Volume 12 (2024) 16 articles
Volume 11 (2023) 18 articles
- Issue 4 3 articles Highlights
- Issue 3 5 articles
- Issue 2 5 articles
- Issue 1 5 articles