Due to the fast growing market in Android smartphone operating systems to date cyber criminalsrnhave naturally extended their target towards Googleâ€˜s Android mobile operating system. Threatrnresearchers are reporting an alarming increase of detected malware for Android from 2012 torn2013. Static analysis techniques for malware detection are based on signatures of knownrnmalicious applications. It cannot detect new malware applications and the attacker will getrnwindow of opportunities until the threat databases are updated for the new malware. Malwarerndetection techniques based on dynamic analysis are mostly designed as a cloud based servicesrnwhere the user must submit the application to know whether the application is malware or not.rnAs a solution to these problems, in this work we design and implement a host based lightweightrnsecurity auditing tool that suits resource-constrained mobile devices in terms of low storage andrncomputational requirements. Our proposed solution utilizes the open nature of the Androidrnoperating system and uses the public APIs provided by the Android SDK to collect features ofrnknown-benign and known-malicious applications. The collected features are then provided tornmachine learning algorithm to develop a baseline classification model. This classification modelrnis then used to classify new or unknown applications either as malware or goodware and if it isrnmalware it alerts the user about the infection.rnOur proposed solution has been tested by analyzing both malicious and benign applicationsrncollected from different websites. The technique used is shown to be an effective means ofrndetecting malware and alerting users about detection of malware, which suggests that it has therncapability to stop the spread of the attack since once the user is aware of the maliciousrnapplication he can take measures by uninstalling the application. Experimental results show thatrnthe proposed solution has detection rate of 96.73% in RandomForest machine learning model which isrnused during the final development of our proposed solution as an Android application and low rate ofrnfalse positive rate(0.01). Performance impact on the Android system can also be ignored which isrnonly 3.7-5.6% CPU overhead, 3-4% of RAM overhead and the battery exhaustion is only 2%.rnKeywords: Smartphones, Android, Malware detection, Machine Learning, Classification