Design and Development of Automated Penetration Testing Model for Network Efficacy

Abstract

The increasing complexity of cyber threats necessitates intelligent and scalable penetration testing frameworks that go beyond manual efforts. Traditional approaches are resource-heavy and often limited by rigid configurations and lack of adaptability. This research introduces a two-stage development of automated penetration testing models integrating reinforcement learning (RL) and deep learning (DL) to enhance detection accuracy and testing efficiency. newlineInitially, a PNS-optimized Q-learning Ensemble Deep CNN (PNS-QCNN) was developed, which combines Convolutional Neural Networks with a reinforcement learning controller and Prairie Natural Swarm optimization for fine-tuning parameters. This model significantly improves classification accuracy and performance in simulated network environments. Building upon this, the Incremental Q-Learning Ensemble Deep CNN (IQLEDN) was proposed to further address the gaps of static learning and prioritization. IQLEDN incorporates fuzzy logic for semantic vulnerability assessment, allowing classification into risk-based categories (None, Partial, Complete) using CVSS and exploitability metrics. It also integrates Canidae-Procyonid Swarm (CPrS) optimization and adopts an incremental learning mechanism to adapt over successive training iterations. newlineBoth models were trained on a structured CVE dataset comprising real-world vulnerabilities. The IQLEDN model achieved superior results with an accuracy of 95.90% on 90% training data and 95.96% with 10-fold cross-validation, outperforming state-of-the-art classifiers like KNN, CatBoost, XGBoost, and LSTM. Performance metrics and cumulative reward analysis confirmed the learning efficiency and adaptability of the models. newlineThis research contributes a scalable, accurate, and intelligent framework for penetration testing, filling key gaps in existing systems. Future work will explore real-time deployment, richer datasets, and further explainability to support critical cybersecurity applications. newline