Leveraging the capabilities of machine learning (ML) has emerged as a pivotal strategy for bolstering cybersecurity defenses. This paper provides an in-depth exploration of the application of ML techniques in the realm of cyber attack detection.

This project focuses on detecting web application attacks using a combination of classical machine learning models and deep learning techniques. The dataset used is the CSIC 2010 Dataset, which contains both normal and anomalous HTTP traffic logs.

In this work we investigate the use of machine learning techniques to leverage the performance of Web Application Firewalls (WAFs), systems that are used to detect and prevent attacks. We propose a characterization of the problem by defining different scenarios depending if we have valid and/or attack data available for training.

Feb 27, 2024 · Several previous studies have utilized machine learning classification methods to create an efficient IDS with various datasets for different types of attacks. This paper utilizes the Canadian...

Jan 6, 2021 · In this blog, we will focus on Machine Learning techniques to detect web attack in network communication flows using continuous learning algorithm that learns the normal pattern of...

The objective is to detect cyber attacks using algorithms like artificial neural networks, convolutional neural networks, and random forests. The document outlines the structure of the project, including requirements gathering, system design, implementation, testing, and …

In this article, we focus on countering emerging application layer cyber attacks since those are listed as top threats and the main challenge for network and cyber security. The major contribution of the article is the proposition of machine learning approach to model normal behaviour of application and to detect cyber attacks.

Abstract—The exploit of vulnerabilities present in Web appli-cations has been the main attack vector in the last decade biggest data breaches. In this work we put forward a framework to leverage the performance of Web Application Firewalls (WAFs) using …

Global computer security issues including virus detection, ransom ware recognition, fraud detection, and spoofing identification were addressed using machine learning techniques.

Nov 6, 2020 · According to the OWASP, the 80 percent of the web attacks are done through HTTP/HTTPS requests queries. In this paper, we present a deep learning based web application attacks detection model. The model uses auto-encoder that can learn from the sequences of word and weight each word or character according to them.