fibratus

Fibratus 用于实时威胁检测和防护的安全传感器 快速开始 » 文档 • 规则 • Filaments • 下载 • 讨论 Fibratus 通过审视和断言广泛的系统事件，并将其与行为驱动的规则引擎和 YARA 内存扫描器进行比对，从而检测并根除高级攻击者攻击手法、恶意软件和新兴威胁。 事件可以被路由到各种各样的输出接收器，或写入捕获文件，以供本地检查和取证分析。借助 filaments，你可以用自己的工具扩展 Fibratus，并利用 Python 生态系统的全部力量。 简而言之，Fibratus 的信条建立在三大支柱之上：实时行为检测 、内存扫描 和取证分析 。 让 Fibrat...

ThreatHound

ThreatHound ThreatHound es una herramienta avanzada de ciberseguridad diseñada para facilitar la detección y análisis eficiente de amenazas, registros de eventos de Windows. Ofrece una interfaz fácil de usar para gestionar y analizar datos de seguridad. Las características principales incluyen...

reflector

reflector 描述 Burp Suite 扩展能够实时在浏览网站时发现页面上的反射型 XSS，并包含以下特性： 在响应标签页中高亮显示反射内容。 测试该反射中允许哪些符号。 分析反射上下文。 Content-Type 白名单。 如何使用 安装插件后，您只需开始操作被测 Web 应用程序。每当发现反射时，reflector 会定义严重性并生成 Burp issue。 每个 Burp issue 包含有关反射参数的详细信息，例如： 该反射中允许的符号。 在响应中高亮显示反射值。 反射上下文分析。 允许的符号分析 当发现反射并且启用了"Aggressive...

FalconEye

FalconEye: Windows进程注入实时检测软件 FalconEye是一款针对Windows端点的实时进程注入检测软件。它是一个内核模式驱动程序，旨在实时捕获进程注入（实时进行）。由于FalconEye运行在内核模式下，它针对试图逃避各种用户模式挂钩的进程注入技术提供了更强且可靠的防御。 您可以查看我们在2021 Blackhat ASIA Arsenal上的演示以及幻灯片。 项目概览 检测覆盖范围 下表展示了各类进程注入技术的实现状态和检测逻辑。WPM代表WriteProcessMemory。如需测试检测功能，可参考参考文献部分。 技术| 状态| 检测逻辑| 使用的POC...

metlo

Metlo API Security 保护您的API。 Metlo 是一个开源的API安全平台 Metlo 是一个开源API安全工具，您可以在15分钟内完成部署，它能够盘点您的端点、检测恶意行为，并实时拦截恶意流量。 实时检测API攻击。 自动拦截恶意行为者。 创建所有API端点和敏感数据的清单。 在API投入生产前主动进行安全测试。 免费开始使用！ 如果您想部署我们的开源产品，我们提供了面向 AWS、GCP、Azure 和 Docker 的部署说明。 功能特性 端点发现 - Metlo 扫描网络流量，为您API中的每一个端点建立清单。 敏感数据扫描 -...

Detect-CVE-2017-0144-attack

Programa Python para detectar ataques CVE-2017-0144 Programa Python cuya función es rastrear y supervisar el tráfico de red continuamente en tiempo real. Mostrará una alerta en la pantalla de la consola cuando detecte un ataque CVE-2017-0144 EternalBlue 1. Modelo del laboratorio Este laboratorio ...

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...