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

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.