| Journal of Cybersecurity and Intelligent Systems

Published Online: 30 September 2026

J. Cybersecur. Intell. Syst., 2026, 1(1), 26506 | Volume 1 Issue 1 (September 2026) | DOI: https://doi.org/10.64189/cis.26506

This article is licensed under Creative Commons Attribution NonCommercial 4.0 International (CC-BY-NC 4.0)

Trust by Design: Launching the Journal of

Cybersecurity and Intelligent Systems

Parikshit N. Mahalle

Department of Artificial Intelligence and Data Science, Vishwakarma Institute of Technology, Pune, Maharashtra, 411037, India

*Email : eic.cdis@gr-journals.com (Parikshit N. Mahalle)

As critical infrastructure, from power grids to global finance, becomes intrinsically bound to complex digital

ecosystems, security can no longer exist as a reactive afterthought. With AI, IoT, cloud computing, and

blockchain becoming integral to digital infrastructure, the acceleration of cyber threats continuously matches

the advancement of defensive capabilities.

[1-3]

Cybersecurity can no longer be treated as a final layer added

before deployment. It has to be a design principle that runs from application code to network edge, from data

governance to supply chain.

[4,5]

With this mission in mind, we proudly introduce the inaugural issue of the Journal of Cybersecurity and

Intelligent Systems, a multidisciplinary forum dedicated to advancing research in cybersecurity, cyber defense,

and strategic technology. The journal addresses critical vulnerabilities in digital security, information warfare,

and critical infrastructure protection. Our scope encompasses threat intelligence, ethical hacking, post-quantum

cryptography, secure IoT and edge architectures, blockchain, explainable and ethical AI, and cyber law and

governance. We invite original research, systematic reviews, and case studies that successfully bridge

theoretical innovation with real-world defense applications. This first issue presents five contributions that

follow a natural arc in building trustworthy systems. Defenders must first find and close weaknesses, then

detect attacks, then verify who and what can be trusted, and finally govern how shared data is used. For each

paper, we outline the approach, the findings, and where the work can be applied.

Bidve et al. developed an AI-driven framework that combines OWASP ZAP, Burp Suite Professional, FOCA, and

Vega in a single Python-based interface. It automates reconnaissance, vulnerability scanning, analysis, reporting,

and remediation recommendations, uses multithreading and multiprocessing to run assessments in parallel,

and prioritizes vulnerabilities by severity. Their experiments showed that combining scanners improves the

detection of common weaknesses such as SQL injection, cross-site scripting, broken authentication, cross-site

request forgery, XML external entity attacks, insecure configurations, and API flaws. Compared with standalone

tools, the framework offered broader coverage, better usability, stronger reporting, and greater efficiency. Its

structured reports support compliance and risk management, and it lowers the cost and complexity of

traditional penetration testing for modern web applications.

[6]

H. M. Khan et al. addressed trust in the pharmaceutical supply chain. Their system uses Solidity smart contracts

on Ethereum to record each stage from raw material to retail. Soulbound Tokens act as non-transferable

credentials so that only verified manufacturers and suppliers can participate. Thousands of product identifiers

are batched off-chain into a single Merkle root stored on-chain, with cryptographic inclusion proofs for

verification. For a 10,000-identifier batch, Merkle-based registration used 127,813 gas units against an

estimated 350,000,000 for sequential registration, an estimated reduction of 99.96%. Over 50 local trials,

verification averaged 2.188 ms and lifecycle queries 1.409 ms. These results were obtained in a local

environment. The architecture applies to tamper-evident participant authorization, batch registration, and

product verification against counterfeiting.

[7]

Chaudhari turned to threat detection in connected environments. The paper applies unsupervised learning,

including Gaussian Mixture Models, to detect malware and network anomalies in the IoT-23 dataset, without

the labeled data that supervised methods depend on. The study presents this as an adaptive and proactive

defense that can identify novel and zero-day threats while limiting false positives, where signature-based

systems struggle. The approach suits enterprise, cloud, and critical-infrastructure settings. Integrated with

security information and event management (SIEM) systems, it can reduce manual intervention and response

time.

[8]

M. S. Khan et al. proposed a decentralized data trust system for privacy-preserving federated learning, aimed at

sensitive and regulated domains such as healthcare. It has four layers: federated training, privacy protection

through secure aggregation and differential privacy, blockchain-based governance through smart contracts, and

contribution-aware incentives using an approximate Shapley value. A comparative analysis against conventional

federated learning, blockchain-enabled federated learning, and data marketplaces shows broader coverage of

privacy, auditability, trust, incentives, and regulatory traceability. The work is conceptual, and the authors plan

quantitative validation of model utility, communication overhead, scalability, and incentive cost. Its applications

lie in collaborative AI where data cannot be shared, accountability is required, and participants must be fairly

rewarded.

[9]

S. Mohammed et al. addressed security within the application itself. They enhanced the standard OAuth 2.0

framework with comprehensive JSON Web Token (JWT) encryption in Spring Boot, using Spring Security.

Confidential user data, claims, and user roles stay protected inside the token payload even if a token is breached,

and role-based access control is managed on top. The system was evaluated with unit tests and manual

penetration testing. The results show that encrypted JWTs protect token contents and secure the access-control

mechanism, mitigating token interception, replay attacks, and privilege escalation. The approach applies to any

web application or API that needs secure authentication and authorization.

[10]

Together, these papers show that trust can be engineered through automation, cryptography, and intelligent

analytics, and that defending critical systems means securing every layer. They also remind us that promising

architectures must be validated at scale and under real-world conditions, a challenge we hope future

submissions will take up.

We are committed to a rigorous and efficient peer review process that ensures every published work meets the

highest scientific, methodological, and ethical standards. On behalf of the Editorial Office, we extend a heartfelt

welcome to our readers, authors, and reviewers. We invite you to contribute your work and help shape this

journal into a leading forum for secure and intelligent digital systems.

Conflict of Interest

There is no conflict of interest.

Artificial Intelligence (AI) Use Disclosure

The authors declare that artificial intelligence (AI)-assisted tools were used only for language refinement,

grammar improvement, and manuscript structuring purposes during the preparation of this work. All technical

content, experimental implementation, results, and interpretations were independently developed and verified

by the authors.

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