01280nam a2200097Ia 4500008004100000041001200041100001400053245007700067260004400144510099400188231221s9999    xx            000 0 und d  aEnglish  aNisha, D. 0aPredicting and preventing adversaries in machine learningbSivaraman, E.  cJan-May 2019bPES UniversityaBengaluru  aMachine learning is a major area in artificial intelligence, which enables computer to learn itself explicitly without programming. As machine learning is widely used in making decision automatically, attackers have strong intention to manipulate the prediction generated by machine learning model. In this paper we study about the different types of attacks and its countermeasures on machine learning model. By research we found that there are many security threats in various algorithms such as K-Nearest-Neighbors (KNN) classifier, Random Forest, AdaBoost, Support Vector Machine (SVM), Decision Tree, we revisit existing security threads and check what are the possible countermeasures during the training and prediction phase of machine learning model In any machine learning model, there is 2 phase one is the training phase and the other one is the testing phase. So, the attacker tries to attack the training phase that is called as causative or poisoning attack and the attack on