## 1 Introduction
Metaheuristic algorithms are a kind of random and global search algorithms that evolved to tackle hard-optimization problems in a feasible amount of time. For example, feature selection in classification problems is a typical optimization problem where the objective is to find the minimal best set of features as well as to maximize the classification performance. In other words, given a dataset with (\(n\)) number of features yields a large search space of \(2^{n}\) potential features subsets, which increases the complexity of the search process, yet becomes more challenging. Remarkably, metaheuristics had shown prominent capacity in approaching hard-optimization problems due to the capability of performing stochastically local and global searches, interchangeably [8; 9; 10]. Owing to their random natural behavior, they can traverse various areas in the search space, which promotes solutions diversity and quality. An efficient metaheuristic strategy can regularize evenly between searching globally which is known as exploration or diversification, and locally that is referred to as exploitation or intensification. The Exploration and exploitation search schemes are two principal components of metaheuristic algorithms. Exploration corresponds to producing more diversified solutions that reside in less-visited regions in the search space, as well as it avoids the trapping in a local best solution during the search of global optima. Whereas, exploitation implies to search at the local regions that potentially can create good solutions [7]. Nature-inspired algorithms are a type of metaheuristics that are inspired by several natural phenomena including the evolution and natural selection, the cooperative foraging behaviors, or physical and chemical observations in ecological systems. Typically, nature-inspired algorithms are widely used to solve optimization, modeling, and simulation problems in various fields of science. Generally, they are grouped into two taxonomies; the population-based or the trajectory-based. Population-based evolutionary algorithms are more common for performing exploration and search at a global scale, which is contrary to the trajectory-based algorithms that are more suitable for exploiting the current region and search at a local scale. In consequence, the Salp Swarm Algorithm (SSA) is a relatively new population-based algorithm that is inspired by the foraging behavior of salps in deep oceans [11]. SSA algorithm has proved efficacy in dealing with feature selection problems and hyperparameter tuning [12; 13]. In this work, we propose a hybrid machine learning model that combines a modified recent swarm intelligent algorithm which is the SSA algorithm with Kernel Extreme Learning Machine (KELM) for Android ransomware detection. The SSA is modified at the level of the swarm structure and the binarization level. The SSA is used to optimize the hyperparameters of the KELM and feature selection, simultaneously. The proposed model is developed for the task of Android ransomware detection based on features that are extracted from 1000 Android applications' permissions and API calls. The goal is to detect Android ransomware with high prediction power with the minimum possible number of features. This paper is structured into six major sections, as follows: the next Section 2 reviews recent works in the area of ransomware detection methods and techniques. Section 3 briefly describes the algorithms used in the proposed detection model. Section 4 presents the proposed classification framework covering an architectural overview, dataset description, the proposed SSA-KELM classification system, and the evaluation measures. The experiments and results are discussed in Section 5. Finally, the main findings and conclusion of this work are given in Section 6.