Deep Learning Combined with A Technique for Detecting Viruses in Pdfs and Urls

Authors

  • J. Jayapradha
  • R. Dhinesh
  • R. Balasubramanian
  • D. Madhurakavi
  • R. Swathi
  • S. Tejaswini

Keywords:

PDF malware detection, RNN, BiLSTM, BiGRU, hybrid model, malicious URLs, cybersecurity, sequential data analysis, phishing detection, deep learning

Abstract

PDF malware is becoming a more serious cybersecurity risk as hackers use malicious payloads and embedded URLs to avoid detection. These complex dangers frequently cause traditional machine learning classifiers to fail. For improved PDF virus detection, we suggest a hybrid RNN-BiLSTM model in order to solve this. BiLSTM improves contextual awareness by processing data in both directions, while the RNN component records temporal dependencies. Furthermore, to detect malicious URLs, we incorporate a BiLSTM-BiGRU architecture, in which BiLSTM improves contextual analysis and BiGRU records sequential dependencies. This hybrid technique increases the efficiency and accuracy of detecting hidden linkages and malware.Our system efficiently identifies new threats while cutting down on training time by utilizing sequential modeling capabilities. According to experimental results, the suggested model performs more accurately and efficiently than conventional techniques, making it a reliable and expandable solution for PDF virus detection.

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References

Y. Liu, W. Lin, J. Wang, and Z. Chen, "A novel approach for malicious PDF detection using deep neural networks," Computers & Security, vol. 92, p. 101760, 2020.

S. Tobiyama, Y. Yamaguchi, H. Shimada, T. Ikuse, and T. Yagi, "Malware detection with deep neural network using process behavior," in Proc. IEEE Trustcom/BigDataSE/ISPA, 2016, pp. 261–267.

W. Hardy, L. Chen, S. Hou, Y. Ye, and X. Li, "DL4MD: A deep learning framework for intelligent malware detection," in Proc. Int. Conf. Data Mining Workshops, 2016, pp. 61–68.

Y. David, N. Partush, and E. Yahav, "Statistical similarity of binaries," in Proc. ACM SIGPLAN Notices, vol. 50, no. 6, pp. 266–280, 2015.

Y. Zhang, L. Wang, Y. Wang, and J. Liu, "Malicious PDF detection using convolutional neural network," IEEE Access, vol. 8, pp. 158131–158140, 2020.

E. Raff, J. Barker, J. Sylvester, R. Brandon, B. Catanzaro, and C. K. Nicholas, "Malware detection by eating a whole EXE," in Proc. AAAI Workshops, 2018.

G. Kim, S. Lee, and S. Kim, "A novel hybrid intrusion detection method integrating anomaly detection with misuse detection," Expert Systems with Applications, vol. 41, no. 4, pp. 1690–1700, 2014.

J. Ma, L. K. Saul, S. Savage, and G. M. Voelker, "Beyond blacklists: Learning to detect malicious web sites from suspicious URLs," in Proc. WWW '09, pp. 1245–1254.

L. Bilge, D. Balzarotti, W. Robertson, E. Kirda, and C. Kruegel, "Disclosure: Detecting botnet command and control servers through large-scale DNS graph analysis," in Proc. ACSAC, 2014.

Y. Wang, L. Wang, and Y. Zhang, "PDF malware detection via hierarchical learning model," Computers & Security, vol. 89, p. 101682, 2020.

A. Shabtai, R. Moskovitch, Y. Elovici, and C. Glezer, "Detecting unknown malicious applications using machine learning techniques," Computers & Security, vol. 30, no. 4, pp. 325–337, 2012.

T. Nguyen, T. Hung, and N. Pham, "uitPDF-MalDe: Malicious PDF document detection based on machine learning," Journal of Information Security and Applications, vol. 65, p. 103140, 2022.

Z. Li, X. Zhang, Y. Zhu, and J. Liu, "A PDF malware detection model using CNN and multi-layer features," IEEE Access, vol. 9, pp. 10401–10410, 2021.

W. Zhou, Z. Qin, and J. Zhang, "Ensemble learning for PDF malware detection," Security and Communication Networks, vol. 2021, Article ID 6627631.

A. Kirichenko, A. Skuratovskii, and A. Sychev, "Static analysis-based feature engineering for malicious document classification," in Proc. MMM-ACNS, 2020.

N. Papernot, P. McDaniel, A. Sinha, and M. Wellman, "Sok: Security and privacy in machine learning," in Proc. EuroSP, 2018.

Seetharaman, K., and N. Palanivel. 2013. “Texture Characterization, Representation, Description, and Classification Based on Full Range Gaussian Markov Random Field Model with Bayesian Approach.” International Journal of Image and Data Fusion 4 (4): 342–62. doi:10.1080/19479832.2013.804007.

N. Carlini and D. Wagner, "Towards evaluating the robustness of neural networks," in Proc. IEEE SP, 2017

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Published

2025-05-26

How to Cite

1.
Jayapradha J, Dhinesh R, Balasubramanian R, Madhurakavi D, Swathi R, Tejaswini S. Deep Learning Combined with A Technique for Detecting Viruses in Pdfs and Urls. J Neonatal Surg [Internet]. 2025May26 [cited 2025Sep.21];14(28S):22-31. Available from: https://www.jneonatalsurg.com/index.php/jns/article/view/6560

Issue

Vol. 14 No. 28S (2025): Journal of Neonatal Surgery

Section

Original Article

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