## 2 Related Works  
In this section, we examine and analyze current Android malware detection solutions built using various ML approaches. Malware detection in Android devices and applications using ML algorithms has been extensively investigated in the literature. Many previous efforts in this research domain have used baseline classifiers to detect Android malware. [PERSON], [PERSON], and [PERSON] [11] proposed SafeDroid, which employed a decision tree (DT) based on structural attributes instead of API calls and permissions, as is customary in conventional methods. The research findings showed that the technique based on structural traits detected new malware better than API-based features. However, the research work focused on structural-based features only. That is, the approach may not be effective for other families of Android malware. Moreover, SafeDroid likely inherits the instability and myopic (based on the greedy approach) problems of the DT algorithm [30]. [PERSON] et al. [31] deployed a regularized logistic regression (RLR) for Android malware detection. From their results, it was observed that RLR had an F-1 score of 0.95. However, the problem of parameter tuning (penalty parameter \(\lambda\)) or RLR persists. Moreover, RLR still has issues with the collinearity of features. [PERSON] et al. [32] proposed Android Malware detection using STatic analySIs of Applications (ANASTASIA) for Android malware. ANASTASIA is practically based on selected ML algorithms, such as support vector machine (SVM), logistic regression (LR), naive Bayes (NB), random forest (RF), k-nearest neighbour (kNN), AdaBoost, XGBoost, and deep learning (DL). The suggested method outperformed some studied existing methods, but the huge overhead runtime cost is a drawback. The framework of ANASTASIA is complex and consists of ML methods with diverse computational characteristics, which invariably increase its computational complexity (time and space). In another context, [PERSON] and [PERSON] [33] deployed an unsupervised one-class SVM for Android malware detection. The suggested method was used on benign instances only, which makes it somewhat inadequate for imbalanced classification. Additionally, [PERSON] et al. [34] proposed the use of clustering techniques for Android malware detection. Specifically, K-means, agglomerative, BIRCH, Gaussian mixture model, and DBSCAN clustering methods were utilized to group malware features based on their characteristics. However, finding or selecting an optimal number of clusters is a problem for the selected clustering methods. In addition, some recent research has concentrated on the application of deep learning (DL) methods, such as long short-term memory (LSTM), deep convoluted neural network (DCNN), recurrent neural network (RNN), AlexNet, and Resnet [9; 17; 35; 36; 37; 38]. [PERSON], [PERSON], [PERSON], and [PERSON] [9] developed an automatic Android malware framework (MalDozer) based on an artificial neural network (ANN). MalDozer was trained to detect malware patterns based on collections of raw method calls in the assembly code. The proposed method recorded high-performance detection on the studied malware datasets; however, its performance in comparison with other existing Android malware methods were not included in the study. [PERSON] and [PERSON] [39] successfully hybridized AlexNet and Resnet-152 for Android malware detection. They integrated two broad pre-trained DL methods in an optimized manner. The performance of the proposed hybrid method was reported to be superior to that of its constituents (AlexNet and Resnet-152, as well as some existing Android malware detection methods). [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [35] developed a feature-fusion solution to categorize Android malware. In particular, they augmented malware images to address any class imbalance that may exist and thereafter deployed a DCNN to generate a single-feature vector for Android malware classification.