01330nam a2200121Ia 4500 231221s9999 xx 000 0 und d English Nisha, D. Predicting and preventing adversaries in machine learning Sivaraman, E. Jan-May 2019 PES University Bengaluru Machine 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 CD 237113 237113 2024-01-26 00:00:00 0 CD7240 2024-01-26 CD 0 0 0 0 ML ML 0000-00-00 CS/PG RS 1 0