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APCI: A Complexity-Aware Framework for Computational Software Effort Estimation in Machine Learning-Based Intrusion Detection Systems
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DOI：10.3390/fi18080424.png)
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Abstract
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The increasing adoption of machine learning and deep learning techniques in intrusion detection systems (IDSs) has substantially increased the computational complexity of model development due to large-scale datasets, sophisticated model architectures, extensive hyperparameter optimization, and repeated experimentation. Despite these demands, existing IDS research primarily emphasizes detection performance while providing limited support for estimating the computational effort required during model development and evaluation. This study proposes the Adaptive Project Complexity Index (APCI), a complexity-aware framework for estimating Computational Software Effort (CSE), a computational resource-based effort metric derived from model training and execution characteristics to support planning and resource estimation in machine learning-based IDS development. To support this objective, a complexity- and effort-oriented benchmark comprising 1040 IDS project instances was constructed using diverse datasets, model architectures, feature configurations, and hyperparameter settings. Statistical analysis demonstrated a strong positive relationship between APCI and CSE, with a Pearson correlation coefficient of 0.834. Building upon this benchmark, multiple machine learning models were evaluated to predict CSE from project characteristics available before implementation, with LightGBM achieving the best predictive performance (R2 = 0.963). Furthermore, explainability analysis identified the dominant computational effort drivers and enabled the development of APCI-Adaptive, improving the APCI–CSE correlation from 0.834 to 0.904 (8.4%). By integrating dataset complexity, model complexity, and computational resource requirements within a unified framework, APCI enables computational software effort estimation prior to IDS implementation, supporting resource planning, experimental design, and complexity-aware project management for machine learning-based IDS development. Consequently, APCI provides a practical decision-support framework for resource planning, experimental design, and complexity-aware project management in modern machine learning-based cybersecurity applications.
Keywords:
intrusion detection systems
computational software effort
adaptive project complexity index (APCI)
software effort prediction
explainable artificial intelligence
machine learning
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