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With the fast development of smartphone technology and mobile applications, the mobile phone has become the most powerful tool to access the Internet and get various services with one click. Meanwhile, susceptibilities of the application are the primary hazard to the security of Android devices. Due to these weaknesses, an attacker can easily hack the confidential data of the mobile phone. The malware application automatically performs fraudulent activities on mobile phones without the user's knowledge. Thus, these attacks are the major threats to the security of mobile phones. To detect malicious applications installed on Android smartphones, we have conducted a study that focuses on permissions and intent-based mechanisms. The study was done in three phases: in the first phase, the dataset was created by extracting intents and permissions from APK files; in the second phase, correlation-based feature selection (CFS) and best first search (BFS) were combined to select the most representative features from the feature space of the extracted dataset; and in the third phase, machine learning (ML) techniques were trained and tested against the preprocessed dataset obtained in the second phase. The accuracy, precision, recall, F1 score, and error metrics of seven machine learning techniques (REPTree, Rule PART, RF, SMO, SGD, MCC, and LMT) were demonstrated over the Android dataset.

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