anomaly-flow

Anomaly-Flow 用于实现入侵检测系统（NIDS）的框架，旨在利用机器学习模型识别网络流中的异常。 参考论文：Anomaly-Flow: A Multi-domain Federated Generative Adversarial Network for Distributed Denial-of-Service Detection 创建实验环境 要创建实验环境，请执行以下命令： root@kitploit: python -m venv .env 注意：虚拟环境必须使用此名称，因为脚本会使用该名称来加载所需的库。...

Multimodal-web-attack-Dataset

MWAD：面向AI驱动的SQL注入检测的多模态Web攻击数据集 概述 多模态Web攻击数据集（MWAD） 是一个新颖的带标签网络安全数据集，是作者在澳大利亚拉筹伯大学 进行网络安全研究期间开发的。 数据集的生成方法、设计和描述发表于同行评审期刊论文： Yeboah, P. N., et al. 2026. SQL injection detection using self-supervised pre-training and multimodal techniques. Intelligent Systems with Applications , 31 , 200702...

Important: Red Hat Security Advisory: Network Observability 1.12.0 for OpenShift

Network Observability 1.12 for Red Hat OpenShift. Network flows collector and monitoring solution...

Important: Red Hat Security Advisory: Network Observability 1.11.2 for OpenShift

Network Observability 1.11 for Red Hat OpenShift. Network flows collector and monitoring solution...

A Novel Byte-Level Flow-To-Image Encoding Method for Network Intrusion Detection Systems

Network-based Intrusion Detection Systems IDS are predominantly trained on tabular flow records, whose one-dimensional representations limit convolutional architectures from exploiting inter-feature spatial correlations. This paper presents a novel byte-level flow-to-image encoding method that...

Important: Red Hat Security Advisory: Network Observability 1.11.1 for OpenShift

Network Observability 1.11 for Red Hat OpenShift. Network flows collector and monitoring solution...

Important: Red Hat Security Advisory: Network Observability 1.11.0 for OpenShift

Network Observability 1.11 for Red Hat OpenShift. Network flows collector and monitoring solution...

AutoGraphAD: A Novel Approach Using Variational Graph Autoencoders for Anomalous Network Flow Detection

Network Intrusion Detection Systems NIDS are essential tools for detecting network attacks and intrusions. While extensive research has explored the use of supervised Machine Learning for attack detection and characterisation, these methods require accurately labelled datasets, which are very...

Anomaly Detection in Network Flows Using Unsupervised Online Machine Learning

Nowadays, the volume of network traffic continues to grow, along with the frequency and sophistication of attacks. This scenario highlights the need for solutions capable of continuously adapting, since network behavior is dynamic and changes over time. This work presents an anomaly detection mod...

Important: Red Hat Security Advisory: Network observability 1.3.0 for Openshift

Network Observability 1.3.0 for OpenShift Red Hat Product Security has rated this update as having a security impact of Important. A Common Vulnerability Scoring System CVSS base score, which gives a detailed severity rating, is available for each vulnerability from the CVE links in the Reference...

Moderate: Red Hat Security Advisory: Network observability 1.2.0 for Openshift

Network Observability 1.2.0 for OpenShift Red Hat Product Security has rated this update as having a security impact of Moderate. A Common Vulnerability Scoring System CVSS base score, which gives a detailed severity rating, is available for each vulnerability from the CVE links in the References...

Research & Academic

We introduce a novel machine learning approach that uses network flows to generate application-level representation of public and private cloud networks. This will greatly simplify the journey to a micro-segmented network...

This Week in Security News: Holiday Cybercriminals & Cryptomining Malware

Welcome to our weekly roundup, where we share what you need to know about the cybersecurity news and events that happened over the past few days. This week, learn the common threats and the best practices for defending against cybercriminals during November’s online shopping season. Also, see the...