The future of web application security: Opportunities and challenges for machine learning-based techniques

Source abstract

Abstract In this paper, the future prospects and potential for machine learning-based solutions in web application security are examined. These include enhancing accuracy and efficiency, real-time detection, scaling, explainable Artificial Intelligence (AI), adversarial machine learning, and automated response. The article also gives a general overview of Machine Learning (ML) methods that are frequently applied to web application security, including supervised learning methods like decision trees, Support Vector Machines (SVMs), and neural networks, unsupervised learning methods like k-means clustering and Principal Component Analysis (PCA), and deep learning methods such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). The incorporation of machine learning-based approaches into security measures will be more necessary as online applications continue to develop in order to meet new threats.