Expedition ML4SEC Part – 1
There is no doubt that state-of-the-art systems can be built using machine learning algorithms but at the same time these algorithm poses serious security flaws. An attacker can take advantage of these flaws by creating adversarial inputs resulting in misbehaviour of Machine Learning systems. In this series we will explore these flaws. But to understand about the vulnerabilities we first need to understand how machine learning models work. Hence, I will dedicate this post to understand the basics of machine learning and finish with building a basic machine learning model. Introduction: Training a machine learning model means increasing the performance of that model in a particular task. Model “Learns” from a dataset. Based on this learning process, machine learning algorithms are classified into following major types. Supervised learning: In supervised learning, we feed the algorithm with features and labels. Consider a problem of classifying network packets into malicious or non malicious. Here features could be the attributes of packet such as source IP, destination IP, port, protocol, payload length, flags,etc. And the labels could be 0 or 1 based based on whether the packet is malicious or not. Classification algorithms like Neural Nets and SVM. Unsupervised learning: This type of learning is used when we do not have a labeled samples. Algorithms learns to differentiate the samples based on the features. Suppose we have a huge set of images of 2 persons and want to classify them. Then we feed these images to an unsupervised algorithm. The algorithm will then create two or more clusters of these images(based on features), which can be labelled as person A and person B. Hence these algorithms are sometimes called as “clustering” algorithms. Semi-supervised learning: Semi-supervised learning is used when we have a mixture of labeled and unlabeled data in dataset....
