## 1 Introduction  
The explosive increase in the number of mobile devices operating on the Android platform has received significant intention among malware creators, since a massive quantity of private information (e.g., contacts, short messages, and e-mails) is typically stored on these devices. The availability of this information on many mass-market mobile devices renders them a desirable target for malware creators, making the security of mobile devices one of the significant, yet challenging, areas of research. Currently, Android remains one of the most prominent operating systems among mobile devices. An increasing number of devices with the Android operating system are prone to attacks [1]. This is particularly significant as smartphones authenticate critical activities (e.g., e-identity and e-banking) [2]. Several applications may have malware; thus, it is important to protect devices from that malware. The increasing number of mobile devices and their lack of safety have driven application developers to initiate higher levels of protection for the users' devices [3]. Android malware can simply refer to a malicious application that takes sensitive data without the knowledge of the users or executes any action which is not authorized by the user. Simultaneously, Android malware detection mechanisms are continuously developing. Publications in the field of machine learning (ML) have presented a vast number of mobile malware detection solutions that are related to some kind of ML-driven technique [4]. Conventionally, ML is used in anomaly-based identification techniques. Anomaly-based identification involves two fundamental stages: the detection/testing stage and the training stage. It can be further classified based on the analysis type: hybrid, static, or dynamic. Static analysis can be executed in a non-runtime atmosphere [5] and analyzes the internal structure of the applications. The dynamic analysis adopted the opposite technique, carried out during the normal operation of the applications. By adding more features derived using dynamic analysis to malware detection methods, they can manage better the more challenging and newer types of malware [6]. However, hybrid analysis mechanisms are more complicated, because of the numerous extra elements mandated by the dynamic analysis, such as real or virtual platforms. Conventional detection techniques have many disadvantages. With the wide applicability of deep learning (DL) in recent times, a method for identifying Android malware utilizing DL technologies has emerged, and the efficiency of malware detection has improved dramatically [7]. DL is a subdivision of machine learning (ML) and Artificial Neural Networks (ANNs). DL is at a high point of development and can be broadly utilized in natural language processing (NLP), computer vision (CV), and speech recognition [8]. The implementation of DL for Android malware detection has now become a trend. A common DL method is utilizing a deep neural network (DNN) that may use several hidden layers of multiple interconnected neurons for data processing. All layers in a DNN contain distinct neurons, each having varied weights and possibly distinct activation functions [9]. If the data were enforced to a neural network (NN), the loss function would compute the prediction error. The most prominent value of DL lies in the abstraction of features and their automatic extraction, abolishing the d'Hearness of manual feature extraction and automatically finding useful and sophisticated high-order features [10]. By summarizing the DL methods utilized in Android malware detection, it is concluded that the current detection methods are mainly recurrent neural networks (RNNs), deep belief networks (DBNs), deep autoencoders (DAEs), and convolutional neural networks (CNNs). This work presents a Rock Hyrax Swarm Optimization with deep learning-based Android malware detection (RHSODL-AMD) model.