fibratus

Fibratus Capteur de sécurité pour la détection des menaces et la protection en temps réel Commencer » Documentation • Règles • Filaments • Téléchargement • Discussions Fibratus détecte et éradique les techniques avancées des attaquants, les logiciels malveillants et les menaces émergentes en...

ThreatHound

ThreatHound ThreatHound est un outil de cybersécurité avancé conçu pour faciliter la détection et l'analyse efficaces des menaces, des journaux d'événements Windows. Il offre une interface conviviale pour gérer et analyser les données de sécurité. Les fonctionnalités clés incluent l'analyse des...

reflector

reflector Description L'extension Burp Suite permet de trouver des XSS réfléchies sur une page en temps réel lors de la navigation sur un site web et inclut certaines fonctionnalités telles que : Mise en évidence de la réflexion dans l'onglet de réponse. Test des symboles autorisés dans cette...

FalconEye

FalconEye : Logiciel de détection en temps réel des injections de processus Windows FalconEye est un logiciel de détection pour terminaux Windows destiné aux injections de processus en temps réel. C'est un pilote en mode noyau qui vise à détecter les injections de processus au moment où elles se...

metlo

Metlo API Security Sécurisez votre API. Metlo est une plateforme open-source de sécurité pour API Metlo est un outil de sécurité API open-source que vous pouvez configurer en moins de 15 minutes. Il inventorie vos endpoints, détecte les acteurs malveillants et bloque le trafic malveillant en temp...

Detect-CVE-2017-0144-attack

Программа на Python для обнаружения атаки CVE-2017-0144 Программа на Python предназначена для непрерывного мониторинга сетевого трафика в реальном времени. Она выводит предупреждение на консоль при обнаружении атаки CVE-2017-0144 EternalBlue 1. Схема лабораторного стенда Лабораторный стенд состои...

Introducing Continuous Vulnerability Assessment: Real-Time Defense for the AI Threat Era

Detect exposure to new vulnerabilities the moment they are published with Wiz CVA...

ShielDroid: A Hybrid Approach Integrating Machine and Deep Learning for Android Malware Detection

The rapid advancement of modern technology has led to a significant increase in the use of smart devices, such as smartphones and tablets, resulting in the widespread adoption of mobile applications. Although applications are required to undergo malware screening before being published on officia...

Intelligent Detection and Mitigation of Carpet-Bombing DDoS Attacks in SDN Using Retrieval-Augmented Generation and Large Language Models

Software-Defined Networking SDN provides flexible and programmable network management; however, its centralized control architecture remains highly vulnerable to Distributed Denial-of-Service DDoS attacks, particularly Carpet-Bombing DDoS attacks that distribute malicious traffic across multiple...

Smart Contract Security beyond Detection

Smart contract security has progressed from vulnerability detection toward a broader research agenda that includes semantic reasoning, automated repair, adversarial robustness, and real-time exploit detection. This paper develops a capstone-oriented research narrative around four directions:...

A-THENA: Early Intrusion Detection for IoT with Time-Aware Hybrid Encoding and Network-Specific Augmentation

The proliferation of Internet of Things IoT devices has significantly expanded attack surfaces, making IoT ecosystems particularly susceptible to sophisticated cyber threats. To address this challenge, this work introduces A-THENA, a lightweight early intrusion detection system EIDS that...

RansomTrack: A Hybrid Behavioral Analysis Framework for Ransomware Detection

Ransomware poses a serious and fast-acting threat to critical systems, often encrypting files within seconds of execution. Research indicates that ransomware is the most reported cybercrime in terms of financial damage, highlighting the urgent need for early-stage detection before encryption is...

Next-Generation Cyberattack Detection with Large Language Models: Anomaly Analysis across Heterogeneous Logs

This project explores large language models LLMs for anomaly detection across heterogeneous log sources. Traditional intrusion detection systems suffer from high false positive rates, semantic blindness, and data scarcity, as logs are inherently sensitive, making clean datasets rare. We address...

AI-Powered Algorithms for the Prevention and Detection of Computer Malware Infections

The rise in frequency and complexity of malware attacks are viewed as a major threat to modern digital infrastructure, which means that traditional signature-based detection methods are becoming less effective. As cyber threats continue to evolve, there is a growing need for intelligent systems t...

Detecting Malicious Entra OAuth Apps with LLM-Based Permission Risk Scoring

This project presents a unified detection framework that constructs a complete corpus of Microsoft Graph permissions, generates consistent LLM-based risk scores, and integrates them into a real-time detection engine to identify malicious OAuth consent activity...

Rapid7: 7 years of recognition in Gartner® Magic Quadrant™ for SIEM

We’re proud to share that Rapid7 has been recognized in the 2025 Gartner Magic Quadrant for Security Information and Event Management SIEM. This is the seventh year we have been positioned in this report, which means we’ve been recognized in every report following the launch of our SIEM offering,...

A Statistical Method for Attack-Agnostic Adversarial Attack Detection with Compressive Sensing Comparison

Adversarial attacks present a significant threat to modern machine learning systems. Yet, existing detection methods often lack the ability to detect unseen attacks or detect different attack types with a high level of accuracy. In this work, we propose a statistical approach that establishes a...

CST-AFNet: A Dual Attention-Based Deep Learning Framework for Intrusion Detection in IoT Networks

The rapid expansion of the Internet of Things IoT has revolutionized modern industries by enabling smart automation and real time connectivity. However, this evolution has also introduced complex cybersecurity challenges due to the heterogeneous, resource constrained, and distributed nature of...

Collaborative P4-SDN DDoS Detection and Mitigation with Early-Exit Neural Networks

Distributed Denial of Service DDoS attacks pose a persistent threat to network security, requiring timely and scalable mitigation strategies. In this paper, we propose a novel collaborative architecture that integrates a P4-programmable data plane with an SDN control plane to enable real-time DDo...

Enhancing GraphQL Security by Detecting Malicious Queries Using Large Language Models, Sentence Transformers, and Convolutional Neural Networks

GraphQL's flexibility, while beneficial for efficient data fetching, introduces unique security vulnerabilities that traditional API security mechanisms often fail to address. Malicious GraphQL queries can exploit the language's dynamic nature, leading to denial-of-service attacks, data...