Enhanced Web Security Using Cross-Feature Analysis of Visual Design, Live Logs and Code Structures DOI
Rahul Kumar,

Tanvi Sharma,

G. Saranya

et al.

2022 7th International Conference on Communication and Electronics Systems (ICCES), Journal Year: 2024, Volume and Issue: unknown, P. 860 - 866

Published: Dec. 16, 2024

Language: Английский

LLM-AE-MP: Web attack detection using a large language model with autoencoder and multilayer perceptron DOI
Jing Yang, Yifan Wu,

Yuping Yuan

et al.

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 126982 - 126982

Published: Feb. 1, 2025

Language: Английский

Citations

1
Industrial IoT intrusion attack detection based on composite attention-driven multi-layer pyramid features DOI

Jiqiang Zhai,

Xinyu Wang, Zhengli Zhai

et al.

Computer Networks, Journal Year: 2025, Volume and Issue: unknown, P. 111207 - 111207

Published: March 1, 2025

Language: Английский

Citations

0
Enhancing Online Security: A Novel Machine Learning Framework for Robust Detection of Known and Unknown Malicious URLs DOI Creative Commons

S. P. Li,

Omar Dib

Journal of theoretical and applied electronic commerce research, Journal Year: 2024, Volume and Issue: 19(4), P. 2919 - 2960

Published: Oct. 26, 2024

The rapid expansion of the internet has led to a corresponding surge in malicious online activities, posing significant threats users and organizations. Cybercriminals exploit uniform resource locators (URLs) disseminate harmful content, execute phishing schemes, orchestrate various cyber attacks. As these evolve, detecting URLs (MURLs) become crucial for safeguarding ensuring secure environment. In response this urgent need, we propose novel machine learning-driven framework designed identify known unknown MURLs effectively. Our approach leverages comprehensive dataset encompassing labels—including benign, phishing, defacement, malware—to engineer robust set features validated through extensive statistical analyses. resulting URL detection system (MUDS) combines supervised learning techniques, tree-based algorithms, advanced data preprocessing, achieving high accuracy 96.83% MURLs. For MURLs, proposed utilizes CL_K-means, modified k-means clustering algorithm, alongside two additional biased classifiers, 92.54% on simulated zero-day datasets. With an average processing time under 14 milliseconds per instance, MUDS is optimized real-time integration into network endpoint systems. These outcomes highlight efficacy efficiency fortifying security by identifying mitigating thereby reinforcing digital landscape against threats.

Language: Английский

Citations

2
Biologically inspired oscillating activation functions can bridge the performance gap between biological and artificial neurons DOI
Mathew Mithra Noel, Shubham Bharadwaj, Venkataraman Muthiah-Nakarajan

et al.

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: unknown, P. 126036 - 126036

Published: Dec. 1, 2024

Language: Английский

Citations

1
Enhanced Web Security Using Cross-Feature Analysis of Visual Design, Live Logs and Code Structures DOI
Rahul Kumar,

Tanvi Sharma,

G. Saranya

et al.

2022 7th International Conference on Communication and Electronics Systems (ICCES), Journal Year: 2024, Volume and Issue: unknown, P. 860 - 866

Published: Dec. 16, 2024

Language: Английский

Citations

0