Applied Machine Learning predictive analytics to SQL Injection Attack detection and prevention

TL;DR: This article proposes a web attack detection system that takes advantage of analyzing URLs, designed to detect web attacks and is deployed on edge devices, and is competitive in detecting web attacks.

TL;DR: This paper provides a survey of prediction, and forecasting methods used in cyber security, and discusses machine learning and data mining approaches, that have gained a lot of attention recently and appears promising for such a constantly changing environment, which is cyber security.

TL;DR: This survey mainly focuses on approaches and technologies to manage the big NTMA data, additionally briefly discussing big data analytics (e.g., machine learning) for the sake of NTMA.

TL;DR: This paper is the first attempt to provide a holistic understanding of the role of ML in the entire cybersecurity domain and highlights the advantages of ML with respect to human-driven detection methods, as well as the additional tasks that can be addressed by ML in cybersecurity.

TL;DR: In this article, a method of over-sampling the minority class involves creating synthetic minority class examples, which is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.

TL;DR: A technique to prevent this kind of manipulation and hence eliminate SQL injection vulnerabilities is described, based on comparing, at run time, the parse tree of the SQL statement before inclusion of user input with that resulting after inclusion of input.

TL;DR: A novel approach to detect injection attacks by modeling SQL queries as graph of tokens and using the centrality measure of nodes to train a Support Vector Machine (SVM) is presented.

TL;DR: This paper proposes an approach that results in an effective n-gram feature extraction from malicious code for classifying executable as malicious or benign with the use of Support Vector Machines (SVM) as the machine learning classifier.