7d13987c195caa108f56077 20250326110013002 beie:beie director@blueeyesintelligence.org WEB-FORM International Journal of Recent Technology and Engineering (IJRTE) IJRTE 22773878 10.35940/ijrte.2277-3878 https://www.ijrte.org/ 03 30 2025 13 6 Department of Information Technology, College of Computer and Information Technology, Taif University, Saudi Arabia. Hanadi Alosimy https://orcid.org/0009-0009-1690-8439 Jawaher AlZaidi https://orcid.org/0009-0001-8256-1850 Department of Information Technology, College of Computer and Information Technology, Taif University, Saudi Arabia. Samah H. Alajmani https://orcid.org/0009-0000-7152-9559 Department of Information Technology, College of Computer and Information Technology, Taif University, Saudi Arabia. Ben Soh https://orcid.org/0000-0002-9519-886X Department of Computer Science and Computer Engineering, La Trobe University, Bundoora, Australia. 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 signature based 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. 03 30 2025 03 30 2025 1 9 CC-BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true International Journal of Recent Technology and Engineering (IJRTE) Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) Based on my understanding, this article has no conflicts of interest. This article has not been funded by any organizations or agencies. This independence ensures that the research is conducted with objectivity and without any external influence. The content of this article does not necessitate ethical approval or consent to participate with supporting documentation. The adequate resources of this article are publicly accessible. The authorship of this article is contributed equally to all participating individuals. 10.35940/ijrte.E8187.13060325 https://www.ijrte.org/portfolio-item/E818713050125/