Enhancing the Efficiency of a Cybersecurity Operations Center Using Biomimetic Algorithms Empowered by Deep Q-Learning.

In the complex and dynamic landscape of cyber threats, organizations require sophisticated strategies for managing Cybersecurity Operations Centers and deploying Security Information and Event Management systems. Our study enhances these strategies by integrating the precision of well-known biomimetic optimization algorithms-namely Particle Swarm Optimization, the Bat Algorithm, the Gray Wolf Optimizer, and the Orca Predator Algorithm-with the adaptability of Deep Q-Learning, a reinforcement learning technique that leverages deep neural networks to teach algorithms optimal actions through trial and error in complex environments. This hybrid methodology targets the efficient allocation and deployment of network intrusion detection sensors while balancing cost-effectiveness with essential network security imperatives. Comprehensive computational tests show that versions enhanced with Deep Q-Learning significantly outperform their native counterparts, especially in complex infrastructures. These results highlight the efficacy of integrating metaheuristics with reinforcement learning to tackle complex optimization challenges, underscoring Deep Q-Learning's potential to boost cybersecurity measures in rapidly evolving threat environments.

Binarization of Metaheuristics: Is the Transfer Function Really Important?

In this work, an approach is proposed to solve binary combinatorial problems using continuous metaheuristics. It focuses on the importance of binarization in the optimization process, as it can have a significant impact on the performance of the algorithm. Different binarization schemes are presented and a set of actions, which combine different transfer functions and binarization rules, under a selector based on reinforcement learning is proposed. The experimental results show that the binarization rules have a greater impact than transfer functions on the performance of the algorithms and that some sets of actions are statistically better than others. In particular, it was found that sets that incorporate the elite or elite roulette binarization rule are the best. Furthermore, exploration and exploitation were analyzed through percentage graphs and a statistical test was performed to determine the best set of actions. Overall, this work provides a practical approach for the selection of binarization schemes in binary combinatorial problems and offers guidance for future research in this field.

Optimal evaluation of re-opening policies for COVID-19 through the use of metaheuristic schemes.

A new contagious disease or unidentified COVID-19 variants could provoke a new collapse in the global economy. Under such conditions, companies, factories, and organizations must adopt reopening policies that allow their operations to reduce economic effects. Effective reopening policies should be designed using mathematical models that emulate infection chains through individual interactions. In contrast to other modeling approaches, agent-based schemes represent a computational paradigm used to characterize the person-to-person interactions of individuals inside a system, providing accurate simulation results. To evaluate the optimal conditions for a reopening policy, authorities and decision-makers need to conduct an extensive number of simulations manually, with a high possibility of losing information and important details. For this reason, the integration of optimization and simulation of reopening policies could automatically find the realistic scenario under which the lowest risk of infection was attained. In this paper, the metaheuristic technique of the Whale Optimization Algorithm is used to find the solution with the minimal transmission risk produced by an agent-based model that emulates a hypothetical re-opening context. Our scheme finds the optimal results of different generical activation scenarios. The experimental results indicate that our approach delivers practical knowledge and essential estimations for identifying optimal re-opening strategies with the lowest transmission risk.

An Optimized Self-Compensated Solution for Temperature and Strain Cross-Sensitivity in FBG Interrogators Based on Edge Filter.

Optical fiber sensors based on fiber Bragg gratings (FBGs) are prone to measurement errors if the cross-sensitivity between temperature and strain is not properly considered. This paper describes a self-compensated technique for canceling the undesired influence of temperature in strain measurement. An edge-filter-based interrogator is proposed and the central peaks of two FBGs (sensor and reference) are matched with the positive and negative slopes of a Fabry-Perot interferometer that acts as an optical filter. A tuning process performed by the grey wolf optimizer (GWO) algorithm is required to determine the optimal spectral characteristics of each FBG. The interrogation range is not compromised by the proposed technique, being determined by the spectral characteristics of the optical filter in accordance with the traditional edge-filtering interrogation. Simulations show that, by employing FBGs with optimal characteristics, temperature variations of 30 °C led to an average relative error of 3.4% for strain measurements up to 700µÏµ. The proposed technique was experimentally tested under non-ideal conditions: two FBGs with spectral characteristics different from the optimized results were used. The temperature sensibility decreased by 50.8% as compared to a temperature uncompensated interrogation system based on an edge filter. The non-ideal experimental conditions were simulated and the maximum error between theoretical and experimental data was 5.79%, proving that the results from simulation and experimentation are compatible.

Performance Evaluation of Bundle Adjustment with Population Based Optimization Algorithms Applied to Panoramic Image Stitching.

The image stitching process is based on the alignment and composition of multiple images that represent parts of a 3D scene. The automatic construction of panoramas from multiple digital images is a technique of great importance, finding applications in different areas such as remote sensing and inspection and maintenance in many work environments. In traditional automatic image stitching, image alignment is generally performed by the Levenberg-Marquardt numerical-based method. Although these traditional approaches only present minor flaws in the final reconstruction, the final result is not appropriate for industrial grade applications. To improve the final stitching quality, this work uses a RGBD robot capable of precise image positing. To optimize the final adjustment, this paper proposes the use of bio-inspired algorithms such as Bat Algorithm, Grey Wolf Optimizer, Arithmetic Optimization Algorithm, Salp Swarm Algorithm and Particle Swarm Optimization in order verify the efficiency and competitiveness of metaheuristics against the classical Levenberg-Marquardt method. The obtained results showed that metaheuristcs have found better solutions than the traditional approach.

Real Power Loss Reduction by Melon Fly Optimization and Spontaneous Process Algorithm's

Abstract In this work Melon Fly Optimization (MFO) Algorithm and Spontaneous Process Algorithm (SPA) is designed to reduce the Real power loss, voltage stability enhancement and reducing the Voltage deviation. In this work real power loss measured and how much loss has been reduced is also identified by suitable comparison with standard algorithms. In this society from common consumer to industry needs better quality of power continuously and constantly without much variation. One way to improve the quality of the power is to reduce the power loss. Also reduction of power loss will improve the economic conditions of the nation indirectly and it improves the productivity of the nation with any hurdles. Around the world all nations sequentially identifying the method to reduce the power loss in the transmission and subsequently it improve the quality of power. MFO algorithm has been formed based on the innate events of Melon fly. Due their very excellent eyesight and mutual supportive behaviour Melon fly will find the food without difficulty. By smell and vision the Melon fly will move to the best location form the current location. In the preliminary level Melon flies will search the food in multiple directions and they may be far away from the food source, it like scattering in the plane. Then Spontaneous Process Algorithm (SPA) is designed to solve the optimal reactive power problem Formulation of the projected algorithm is done by imitating the process done during nuclear fission and fusion. Every item of a nucleus attribute symbolizes each solution variable. Sequence of operators directs the nucleus and in order to avoid the local optimum it will imitate the dissimilar condition of reaction. In the exploration space nucleus symbolizes the variables and potential solution. Levy flight has been intermingled in the procedure to enhance the diversification and intensification in the search. Evaluation of validity of the Melon Fly Optimization (MFO) Algorithm and Spontaneous Process Algorithm (SPA) is done in IEEE 30-bus system by considering voltage stability (L-index) and also devoid of L-index criterion. Minimization of voltage deviation, voltage stability enhancement and power loss minimization has been achieved.

A Novel Deep Learning-based Whale Optimization Algorithm for Prediction of Breast Cancer

HIGHLIGHTS Novel whale optimization algorithm is proposed for prediction of breast cancer. Deep learning-based WOA adjusts the CNN structure as per maximum detection accuracy. Proposed method achieves 92.4% accuracy in comparison to 90.3%. Validity of method is evaluated with magnifying factors like 40x, 100 x, 200x, 400x.

Abstract Breast cancer is one of the most common cancers among women that cause billions of deaths worldwide. Identification of breast cancer often depends on the examination of digital biomedical photography such as the histopathological images of various health professionals, and clinicians. Analyzing histopathological images is a unique task and always requires special knowledge to conclude investigating these types of images. In this paper, a novel efficient technique has been proposed for the detection and prediction of breast cancer at its early stage. Initially, the dataset of images is used to carry out the pre-processing phase, which helps to transform a human pictorial image into a computer photographic image and adjust the parameters appropriate to the Convolutional neural network (CNN) classifier. Afterward, all the transformed images are assigned to the CNN classifier for the training process. CNN classifies incoming breast cancer clinical images as malignant and benign without prior information about the occurrence of cancer. For parameter optimization of CNN, a deep learning-based whale optimization algorithm (WOA) has been proposed which proficiently and automatically adjusts the CNN network structure by maximizing the detection accuracy. We have also compared the obtained accuracy of the proposed algorithm with a standard CNN and other existing classifiers and it is found that the proposed algorithm supersedes the other existing algorithms.

Fitting of experimental data using a fractional Kalman-like observer.

In this paper, a fractional order Kalman filter (FOKF) is presented, this is based on a system expressed by fractional differential equations according to the Riemann-Liouville definition. In order to get the best fitting of the FOKF, the cuckoo search optimization algorithm (CS) was used. The purpose of using the CS algorithm is to optimize the order of the observer, the fractional Riccati equation and the FOKF tuning parameters. The Grünwald-Letnikov approximation was used to compute the numerical solution of the FOKF. To show the effectiveness of the proposed FOKF, four examples are presented, the brain activity, the cutaneous potential recordings of a pregnant woman, the earthquake acceleration, and the Chua's circuit response.