anomaly-flow

Anomaly-Flow Framework for implementing Intrusion Detection Systems NIDS aimed at identifying anomalies in network flows using Machine Learning models. Reference paper: Anomaly-Flow: A Multi-domain Federated Generative Adversarial Network for Distributed Denial-of-Service Detection Create the...

Multimodal-web-attack-Dataset

MWAD: AI 기반 SQL 인젝션 탐지를 위한 다중 모달 웹 공격 데이터셋 개요 다중 모달 웹 공격 데이터셋Multimodal Web Attack Dataset, MWAD은 저자가 호주 라트로브 대학교La Trobe University 에서 수행한 사이버보안 연구의 일환으로 개발된 새로운 라벨링 사이버보안 데이터셋입니다. 데이터셋 생성 방법론, 설계 및 설명은 동료 심사 저널 논문에 제시되어 있습니다: Yeboah, P. N., et al. 2026. SQL injection detection using...

Important: Red Hat Security Advisory: Network Observability 1.12.3 for OpenShift

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

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...