International Journal of Network Security & Its Applications (IJNSA) - ERA Indexed, H Index - 52

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International Journal of Network Security & Its Applications (IJNSA) - ERA Indexed, H Index - 52

ISSN: 0974 - 9330 (Online); 0975 - 2307 (Print)

Webpage URL: https://airccse.org/journal/ijnsa.html

Google Scholar URL: https://scholar.google.co.in/citations?hl=en&user=lhj_6AYAAAAJ

Citations 12233   h-index 52   i10-index 220

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International Journal of Network Security & Its Applications (IJNSA) - ERA Indexed, H Index - 52

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Welcome To IJNSA

International Journal of Network Security & Its Applications (IJNSA) - ERA Indexed, H Index - 52

ISSN: 0974 - 9330 (Online); 0975 - 2307 (Print)

Webpage URL: https://airccse.org/journal/ijnsa.html

Wikicfp URL: http://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=190234

Submission Deadline: February 15, 2026

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Submission URL: https://airccse.com/submissioncs/home.html

International Journal of Network Security & Its Applications (IJNSA)

International Journal of Network Security & Its Applications (IJNSA)

ISSN: 0974 - 9330 (Online); 0975 - 2307 (Print)

Webpage URL: https://airccse.org/journal/ijnsa.html

Optimized Naive Bayes for Phishing Website Detection using Hybrid TF - IDF and Character Level URL Features

Hieu Ngo Van, Tin Trinh Quang, Phuong Nguyen Thi Thanh, Huong Mai Quoc and Dung Nguyen Thi Thuy, Duy Tan University, Vietnam

Abstract

The rapid growth of phishing websites poses significant challenges to users and online systems, particularly as many existing detection approaches rely on webpage content analysis or computationally expensive deep learning models. This paper proposes a lightweight phishing URL detection method that integrates token-level Term Frequency–Inverse Document Frequency (TF–IDF) and character-level ngrams within a Multinomial Naive Bayes classifier. The proposed approach is evaluated on three public datasets, including the UCI Phishing Website dataset, PhishTank, and URLNet. Experimental results show that the model achieves F1-scores ranging from 0.904 to 0.940 when trained and tested on individual datasets, indicating robust detection performance. When the three datasets are merged for training, the model attains an F1-score of 0.931, with Recall improving by 2.0 percent compared to the average singledataset results, reflecting enhanced generalization across diverse data sources. The lightweight nature of the proposed method enables fast URL classification and practical deployment under hardware constraints.

Keywords

Phishing detection, URL analysis, Machine learning, Naive Bayes, TF–IDF

Abstract URL: http://aircconline.com/abstract/ijnsa/v18n1/18126ijnsa02.html

Original Source URL: https://aircconline.com/ijnsa/V18N1/18126ijnsa02.pdf

Volume URL: https://airccse.org/journal/jnsa26_current.html

International Journal of Network Security & Its Applications (IJNSA) - ERA Indexed, H Index - 52

#networksecurity #artificialintelligence #machinelearning #security #blockchain #technology #cybersecurity #cloud #fog #edge #crytography #protocols #datacenter #network #optimization #database #systemsecurity #spam #phishing #email #iot #internetsecurity #intrusion #detection #prevention #mobile #adhoc #sensor #p2p #networkmanagement #virtualmachines #vanet #socialnetwork #ddos #trust #privacy #wirelessnetwork #ubiquitouscomputing #virus #worms #trojan #hacking #ransomware #computer #cloudsecurity #malware #firewall #bigdata #informationsecurity #cloudcomputing #dataprotection #networking #cloudstorage #cybercrime #cyberattack #ethicalhacking #datasecurity #tech #itsecurity #internet #computerscience #server #dataprivacy

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Welcome To IJNSA

International Journal of Network Security & Its Applications (IJNSA) - ERA Indexed, H Index - 52

ISSN: 0974 - 9330 (Online); 0975 - 2307 (Print)

Webpage URL: https://airccse.org/journal/ijnsa.html

Submission Deadline: February 15, 2026

Contact us:

Here's where you can reach us: ijnsa@aircconline.com (or) ijnsa@airccse.org (or) ijnsajournl@gmail.com

Submission URL: https://airccse.com/submissioncs/home.html

International Journal of Network Security & Its Applications (IJNSA) - ERA Indexed, H Index - 52

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International Journal of Network Security & Its Applications (IJNSA) - ERA Indexed, H Index - 52

ISSN: 0974 - 9330 (Online); 0975 - 2307 (Print)

Call for papers

The International Journal of Network Security & Its Applications (IJNSA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the computer Network Security & its applications. The journal focuses on all technical and practical aspects of security and its applications for wired and wireless networks. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding Modern security threats and countermeasures, and establishing new collaborations in these areas.

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13th International Conference on Artificial Intelligence and Applications (AIAPP 2026)

#ArtificialIntelligence #machinelearning #deeplearning #decisionmaking #robotics #naturallanguageprocessing #computervision #optimization #CognitiveComputing #datamining #BigData #informationretrieval #bioinformatics #FuzzyLogic #neuralnetworks #ParallelProcessing #computerscience #engineering #informationtechnology #SemanticWeb #multimedia #patternrecognition #mechatronics #Computing #education #programming #softcomputing #technology #dataanalytics #hardware #deployment #simulation #modeling #computationalintelligence #informatics #reasoning #software #ubiquitouscomputing #recognition

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Welcome To AIAPP 2026

13th International Conference on Artificial Intelligence and Applications (AIAPP 2026)

May 16 ~ 17, 2026, Zurich, Switzerland

Webpage URL: https://aiapp2026.org/index

Submission Deadline: February 07, 2026

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Here's where you can reach us: aiapp@aiapp2026.org (or) aiappconfere@yahoo.com

Submission URL: https://cosit2026.org/submission/index.php

International Journal of Network Security & Its Applications (IJNSA)

International Journal of Network Security & Its Applications (IJNSA)

ISSN: 0974 - 9330 (Online); 0975 - 2307 (Print)

Webpage URL: https://airccse.org/journal/ijnsa.html

Empirical Telemetry-based Metrics for Evaluating Honeypot Realism and Deception Effectiveness

Teresita Noelia Nunez Migliorisi, University of Delaware, USA

Abstract

Honeypots remain critical tools for cyber deception, adversarial observation, and proactive threat intelligence. However, despite decades of development, the field still lacks a standardized and empirically validated framework for assessing deception effectiveness. Existing studies rely heavily on raw connection counts or ad hoc indicators, limiting reproducibility, comparability, and operational relevance. This paper presents a telemetry-driven methodology for evaluating honeypot realism and deception effectiveness across measurable behavioral dimensions. Using both a baseline cloud honeynet and an Enhanced Realism-Driven Honeynet (ERDH) modeled on a healthcare research environment, it's empirically demonstrated that domain-consistent realism significantly increases attacker dwell time, interaction depth, behavioral diversity, and malware family richness.

Keywords

Honeypots, Deception, Engagement, Telemetry, Metrics, Evaluation, Standardization, NIST

Abstract URL: http://aircconline.com/abstract/ijnsa/v18n1/18126ijnsa01.html

Original Source URL: https://aircconline.com/ijnsa/V18N1/18126ijnsa01.pdf

Volume URL: https://airccse.org/journal/jnsa26_current.html