An intelligent forecasting system for unauthorized URL identification using deep learning

W. Rose Varuna; M. Harshini; K. T. Baby

doi:10.53730/ijhs.v6nS1.6725

Authors

W. Rose Varuna Assistant Professor, Department of Information Technology, Bharathiar University, Coimbatore, Tamilnadu, India
M. Harshini Student (II M.Sc), Department of Information Technology, Bharathiar University, Coimbatore, Tamilnadu, India
K. T. Baby Student (II M.Sc), Department of Information Technology, Bharathiar University, Coimbatore, Tamilnadu, India

Keywords:

Cyber-attack, URL, classification, neural network, malicious user, PCA, accuracy

Abstract

A cyber-attack is a destructive action carried out in cyberspace by an intruder, a group, an association, or a country that targets computer information systems, infrastructures, personal computers, and servers in order to carry out harmful operations or impair their functioning. Attackers produce and sell a wide range of tools for launching web application attacks and increasing the quantity of destructive attacks. Dynamic classifiers continuously watch the web page's activities, whereas classifiers download the page, extract the attributes, and do classification analysis. This article covers how to identify a fraudulent person using a URL and other pertinent data. The process of selecting features from a URL is accomplished using PCA, and the collected characteristics are used to classify URL types. The categorization is done with 98.5 percent accuracy using the DBN approach, which distinguishes between normal and benign users. Other existing techniques are outperformed by the feature selection technique PCA with DBN classification.

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References

Dixit, P., &Silakari, S. (2021). Deep learning algorithms for cybersecurity applications: A technological and status review. Computer Science Review, 39, 100317.

Ulven, J. B., &Wangen, G. (2021). A systematic review of cybersecurity risks in higher education. Future Internet, 13(2), 39.

Kumar, E. Boopathi, and V. Thiagarasu. "Segmentation using Fuzzy Membership Functions: An Approach." IJCSE, ISSN (2017): 2347-2693.

Kumar, E. Boopathi, and V. Thiagarasu. "Comparison and Evaluation of Edge Detection using Fuzzy Membership Functions." International Journal on Future Revolution in Computer Science & Communication Engineering (IJFRCSCE), ISSN: 2454-4248.

Fosch-Villaronga, E., & Mahler, T. (2021). Cybersecurity, safety and robots: Strengthening the link between cybersecurity and safety in the context of care robots. Computer Law & Security Review, 41, 105528.

Lee, I. (2021). Cybersecurity: Risk management framework and investment cost analysis. Business Horizons, 64(5), 659-671.

Marinos, N. (2021). Cybersecurity: Federal Actions Urgently Needed to Better Protect the Nation's Critical Infrastructure (No. GAO-22-105530).

Sadiq, A., Anwar, M., Butt, R. A., Masud, F., Shahzad, M. K., Naseem, S., &Younas, M. (2021). A review of phishing attacks and countermeasures for internet of things‐based smart business applications in industry 4.0. Human Behavior and Emerging Technologies, 3(5), 854-864.

Butnaru, A., Mylonas, A., &Pitropakis, N. (2021). Towards lightweight url-based phishing detection. Future internet, 13(6), 154.

Gupta, B. B., Yadav, K., Razzak, I., Psannis, K., Castiglione, A., & Chang, X. (2021). A novel approach for phishing URLs detection using lexical based machine learning in a real-time environment. Computer Communications, 175, 47-57.

Chen, D., Wawrzynski, P., &Lv, Z. (2021). Cyber security in smart cities: a review of deep learning-based applications and case studies. Sustainable Cities and Society, 66, 102655.

Rosenberg, I., Shabtai, A., Elovici, Y., &Rokach, L. (2021). Adversarial machine learning attacks and defense methods in the cyber security domain. ACM Computing Surveys (CSUR), 54(5), 1-36.

Raja, A. S., Vinodini, R., &Kavitha, A. (2021). Lexical features based malicious URL detection using machine learning techniques. Materials Today: Proceedings, 47, 163-166.

Biswas, B., Mukhopadhyay, A., Bhattacharjee, S., Kumar, A., &Delen, D. (2022). A text-mining based cyber-risk assessment and mitigation framework for critical analysis of online hacker forums. Decision Support Systems, 152, 113651.

Barraclough, P. A., Fehringer, G., & Woodward, J. (2021). Intelligent cyber-phishing detection for online. Computers & Security, 104, 102123.

RAMESH, K., BENNET, M. A., VEERAPPAN, J., & RENJITH, P. (2021, April). Performance Metric System for Malicious URL Data using Revised Random Forest Algorithm. In 2021 5th International Conference on Computing Methodologies and Communication (ICCMC) (pp. 1188-1191). IEEE.

Bhagyashree, E., &Tanuja, K. (2015). Phishing URL detection: a machine learning and web mining-based approach. International Journal of Computer Applications, 123(13), 46-50.

Jeeva, S. C., &Rajsingh, E. B. (2016). Intelligent phishing url detection using association rule mining. Human-centric Computing and Information Sciences, 6(1), 1-19.

Le, A., Markopoulou, A., &Faloutsos, M. (2011, April). Phishdef: Url names say it all. In 2011 Proceedings IEEE INFOCOM (pp. 191-195). IEEE.

Yu, F. (2015). Malicious url detection algorithm based on bm pattern matching. International Journal of Security and Its Applications, 9(9), 33-44.

Shrivastava, V., Damodaran, S. S., &Kamble, M. (2020). Adalward: a deep-learning framework for multi-class malicious webpage detection. Journal of Cyber Security Technology, 4(3), 153-195.

Yookesh, T. L., et al. "Efficiency of iterative filtering method for solving Volterra fuzzy integral equations with a delay and material investigation." Materials today: Proceedings 47 (2021): 6101-6104.

E. B. Kumar and V. Thiagarasu, "Color channel extraction in RGB images for segmentation," 2017 2nd International Conference on Communication and Electronics Systems (ICCES), 2017, pp. 234-239, doi: 10.1109/CESYS.2017.8321272.