# Fast, Lightweight, and Efficient Cybersecurity Optimization for Tactical-Operational Management
## 4 Conclusions and Future Work  
In this research work, we address the potential of evolutionary computation for solving an optimization problem related to cybersecurity management. To this end, we have developed FLECO, a multi-objective, constrained, and adaptive genetic algorithm that assists the cybersecurity workforce in selecting the set of actions that must be implemented  
Figure 3: Approximation achieved by FLECO of the constrained solutions space. Each green dot is a feasible, high-quality solution found by FLECO. to comply with the cybersecurity restrictions required from the strategic sphere. The multidisciplinary cybersecurity teams, consisting of 3-5 and 15-20 members, respectively, from the collaborating companies in this study encountered difficulties in finding feasible combinations of cybersecurity actions without the aid of FLECO. These combinations were essential for their discussions and the achievement of comprehensive agreements as mandated by the CyberTOMP framework. Despite numerous attempts over the course of a month while the design of FLECO was in progress, the teams were unsuccessful in identifying a feasible set of cybersecurity actions that met the requirements within the specified timeframe of less than 5 min, as stipulated by the decision-makers of these companies. In fact, they were unable to find a suitable set even after dedicating significant additional time. Addressing this need, FLECO provides feasible sets of cybersecurity actions that fulfill the multiple established objectives in a significantly shorter time than what is required by the decision-makers of the participating companies. This capability has enabled them to conduct tactical-operational management meetings, explore different combinations, and achieve holistic cybersecurity starting from the lower levels of the organization. The specific contributions of our work to this scenario are as follows:  
1. An effective mechanism, as it discovers solutions that comply with all business-level constraints. 2. A rapid mechanism, as it achieves this within a timeframe of less than 5 min, facilitating the smooth implementation of the CyberTOMP framework. 3. An efficient mechanism, as it operates using general-purpose hardware similar to the workstations commonly found in contemporary companies. 4. A predictable mechanism, as it exhibits stable behavior regardless of search conditions, consistently delivering solutions of comparable quality. 5. The practical demonstration of the application of evolutionary computing to decision-making in cybersecurity management. The algorithm has been designed based on the specifications of the CyberTOMP framework, which makes it useful and directly applicable to organizations that are using this framework for tactical and operational cybersecurity management. However, it is easily modifiable to adapt to similar frameworks, the NIST framework being particularly well-suited. Furthermore, the set of test cases designed to validate FLECO has also aimed to minimize bias and the influence that the participation of two specific organizations may have on the results. FLECO has demonstrated its speed, efficiency, and effectiveness in finding solutions in a wide variety of contexts, meeting the expectations set by decision-makers in the participating organizations regarding the quality of the solutions provided, the speed with which those solutions are generated, and the positive effect this has on the holistic, tactical-operational cybersecurity management process and meetings that CyberTOMP foresees to discuss and jointly agree on cybersecurity actions to execute. In summary, we can say that evolutionary computation in general, and genetic algorithms such as FLECO in particular, can positively make a difference in decision-making in a poorly explored area such as tactical-operational cybersecurity management. As part of our research we have also identified some lines for future work we deem necessary to expose.