# DroidDetectMW: A Hybrid Intelligent Model for Android Malware Detection
## 2 Related Work  
Academics have published many ways to detect and categorize Android malware with ML algorithms. This paper will review the tasks involved in malware detection and classification using ML and deep learning techniques, including static and dynamic malware analysis. In [34], Significant Permission Identification SigPID is a malware detection approach proposed by [PERSON] et al. They construct a three-tiered pruning system based on extracted permission data to identify malicious apps from legitimate ones. The Android apps were categorized by the authors using ML methods. According to the experiments, SigPID has a 93.62% higher efficiency than the best existing approaches. In [35], the authors delved into the threats posed by Android apps' need for permissions. \(T\)-tests, mutual information, and correlation coefficients were used to sort the risks associated with each permit. The subgroups of potentially malicious permissions are identified using sequential forward selection and principal component analysis (PCA). A decision Tree, Support Vector Machine, and Random Forest were used to evaluate how well risky permission could identify malicious apps. From what we can see, this detector has a 94.62% detection accuracy and a 0.6 False Positive Rate (FPR). Regarding enhancing accuracy, the innovative fusion technology (DroidFusion) introduced by [PERSON] and [PERSON] [36] is hard to beat. DroidFusion trains classifiers to construct a model, which is then used with a feature importance ranking method based on the prediction precisions to obtain a classifier. The experiments highlight DroidFusion's superiority over the stacking ensemble technique. The methodology suggested by [PERSON] et al. [37], frequency centric for feature creation utilizing system calls, can accurately identify malware through only those system calls. The authors create an MLP on FPGA-based ML methodology for classifier training. The suggested method was determined to achieve high precision, rapid detection, and low power usage. Automatically classifying the app as malicious or benign, the authors in [23] built an engine called DroidDetector. Using dynamic and static analysis, the authors extracted the features. Finally, the experimental results show that DroidDetector has the highest accuracy of 96.76% compared to traditional ML methods. The Android Application Sandbox (AASandbox) was introduced by the authors of [38], which can perform both static and dynamic analysis to aid in detecting malicious apps. They used cloud deployment of the detection algorithm and sandbox to provide widespread and fast detection. The outcomes show that AASandbox performs better than Android antiviral apps. In [39], the authors create a hybrid sequential network architecture using stacked denoising auto-encoders (SDAEs) and stacked hybrid learning merged sparse auto-encoders (MSAEs). Feature extraction from this hybrid model is fed into classification techniques like the SVM and K-NN. Using two datasets, the proposed model is compared to various previous detection methods using evaluation metrics such as precision, accuracy, f1-score, specificity, and sensitivity. Malware classification is proposed in [40], which presents a DeepMal model for malware detection based on a combination of LSTM and CNN deep learning models. The data for this model was derived from a similar source--a sequence of API calls. This hybrid CNN-LSTM design features a variety of layers, including a nonlinearity layer, a convolution layer, and a whole layer. There is a significant probability of malware assaults due to the massive volumes of data uploaded to the cloud. Metrics for assessing the efficacy of a model for detecting malware in the cloud are presented in [41]. A two-dimensional convolutional neural network (CNN) model supplied with memory, CPU, and network information yielded 90% accuracy. Furthermore, the SMOTE method and standardization with hyper-parameter adjustment can be used to achieve excellent detection accuracy.