In recent years, Android has become the leading smartphone operating system across the world. However, due to their increasing popularity, Android devices have become the primary target to mobile malware. To address the arising security threats, many malware detection approaches have been studied that aim at providing strong defense mechanisms against malware. However, with more such malware detection systems being distributed and deployed, malware authors tend to generate adversarial examples by manipulating mobile applications to avoid being detected by the malware detection systems. For this project, we investigate different types of adversarial example attacks while researching a viable approach to fight against them. We do this by first conducting a literature review on existing malware detection approaches and adversarial attacks on these approaches. We then use that information to develop our own attack models to generate adversarial examples in order to study their behavior. Then, we focus on evasion attack models and data poisoning attack models to generate mutated samples. By working with various app features such as API calls and Permissions, we will generate feature sets. As a result, we used the manipulated dataset to develop and train our classifier to detect both evasion and data poisoning attacks. The goal of our approach is to further enhance the robustness of malware detection approach in the presence of adversarial example attacks.