Machine learning is widely accepted as an accurate statistical approach for malware detection to cope with the rising uncertainty risk and complexity of modern intrusions. Not only has machine learning security been asked, but it has also been challenged in the past. However, it has been identified that machine learning contains intrinsic weaknesses that may be exploited to avoid detection during testing. So, look at it another way, machine learning can become an intelligence system bottleneck. We use the related attack methodology to classify different types of attacks using learning-based malware detection techniques in this research by evaluating attackers with unique abilities and talents. After this, to carefully identify the security of Drebin, Android malware detection has been performed. We implemented and did a set of comparable malware detection using the linear SVM and other relevant techniques, including Sec-SVM, Reduced SVM, Reduced Sec-SVM, Na ̈ıve Bayes, Random Forest Classifier, and some deep neural networks. The main agenda of this paper is the presentation of a scalable and straightforward securelearning methodology that reduces the effect of adversarial attacks. In the presence of an attack, the detection accuracy is only a bit worsened. Finally, we evaluate that our robust technique may be accurately adapted to additional intrusion prevention tasks.