Published March 30, 2025 | Version CC-BY-NC-ND 4.0
Journal article Open

An Algorithm for Detecting Brute Force Attacks on FTP and SSH Services Utilizing Deep Learning with Probabilistic Neural Networks (PNN)

Authors/Creators

  • 1. Department of Information Technology, College of Computer and Information Technology, Taif University, Saudi Arabia.

Contributors

Contact person:

  • 1. Department of Information Technology, College of Computer and Information Technology, Taif University, Saudi Arabia.
  • 2. Department of Computer Science and Computer Engineering, La Trobe University, Bundoora, Australia.

Description

Abstract: Brute force attacks remain one of the most prevalent and effective methods cybercriminals use to gain unauthorized access to networks and systems. These attacks involve systematically attempting various password or key combinations until the correct one is identified, often targeting critical services such as FTP (File Transfer Protocol) and SSH (Secure Shell). The consequences of these attacks can be severe, including data breaches, financial losses, and reputational damage. Intrusion Detection Systems (IDS) play a crucial role in mitigating these threats by monitoring network traffic and identifying malicious activities. However, traditional IDS methods - such as signaturebased detection and anomaly detection - struggle to detect emerging and evolving threats. To address these challenges, this study presents an advanced detection model utilizing deep learning techniques, specifically a Probabilistic Neural Network (PNN), to identify brute force attacks on FTP and SSH protocols. The model is trained and evaluated using the CICIDS2018 dataset, with the Bat Optimization Algorithm employed to fine-tune parameters and enhance performance. The proposed model achieves remarkable results, with an accuracy of 99.968%, precision of 99.949%, recall of 99.986%, and an F1-score of 99.968%. These findings highlight the model's potential as a highly effective tool for strengthening network security and preventing unauthorized access.

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Additional details

Identifiers

DOI
10.35940/ijrte.E8187.13060325
EISSN
2277-3878

Dates

Accepted
2025-03-15
Manuscript received on 12 November 2024 | Revised Manuscript received on 28 December 2024 | Second Revised Manuscript received on 16 January 2025 | Manuscript Accepted on 15 March 2025 | Manuscript published on 30 March 2025.

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