The ISC International Journal of Information Security

Abstract Selecting optimal cybersecurity countermeasures requires integration with mission-critical objectives beyond technical risk minimization. This paper presents a mission-centric framework for countermeasure selection in cybersecurity situation awareness systems by extending the RiskMAP methodology with agent-based and discrete-event simulation. The framework employs a multi-criteria decision-making approach based on the Confidentiality, Integrity, and Availability (CIA) triad, weighing mission objectives and mapping vulnerabilities and threats using MITRE ATT&CK and D3FEND taxonomies. Candidate countermeasures are evaluated considering risk reduction, implementation cost, operational impact, and mission alignment. We demonstrate the approach through a case study on a critical infrastructure organization’s network modeled in AnyLogic. Results show improved alignment between security posture and organizational priorities while maintaining effective risk reduction, outperforming traditional methods. This framework enables quantitative visualization and optimization of security investments relative to mission continuity. All simulation models, data, and scripts are openly available to support reproducibility.

Abstract Detecting Advanced Persistent Threats (APTs) is crucial, and a practical approach involves using an intrusion detection system (IDS) integrated with supervised machine learning algorithms. These algorithms require a balanced dataset with ample attack samples to learn and recognize attack patterns effectively. However, widely used APT datasets, such as DAPT2020 and SCVIC-APT-2021, suffer from imbalance issues that limit the performance of machine learning-based intrusion detection systems (IDS). We introduce DSRL-APT-2023, a new balanced synthetic APT dataset generated using CTGAN to address this challenge. The CTGAN model is trained on the DAPT2020 dataset to create this balanced dataset. We evaluate and compare the performance of six standard supervised machine learning algorithms—Decision Tree, Support Vector Machine, K-Nearest Neighbor, Logistic Regression, Random Forest, and Multi-Layer Perceptron— alongside an intrusion detection system (IDS) called Intelligent Intrusion Detection System, which is based on tree-structured machine learning models. Our evaluation focuses on detecting attacks in DSRL-APT-2023 and compares its performance to DAPT2020 and SCVIC-APT-2021. Additionally, we assess the data quality of synthetic datasets generated by two prominent GANs, CopulaGAN, and CTGAN, with CTGAN demonstrating slightly superior performance in generating high-quality tabular data. Our results demonstrate that machine learning algorithms and the Intelligent IDS can accurately detect attacks in the synthetic dataset, as evidenced by the F1-Score metrics.