metlo

Metlo API 보안 API를 보호하세요. Metlo는 오픈소스 API 보안 플랫폼입니다 Metlo는 15분 안에 설정할 수 있는 오픈소스 API 보안 도구로, 엔드포인트를 인벤토리화하고 악성 행위자를 탐지하며 실시간으로 악성 트래픽을 차단합니다. 실시간 API 공격 탐지 악성 행위자 자동 차단 모든 API 엔드포인트 및 민감 데이터 인벤토리 생성 프로덕션 배포 전 API 사전 테스트 지금 무료로 시작하세요! 오픈소스 제품을 배포하려면 AWS, GCP, Azure 및 Docker에 대한 설명서를 참조하세요. 기능 엔드포인트 ...

fibratus

Fibratus リアルタイムの脅威検出と保護のためのセキュリティセンサー はじめよう » ドキュメント • ルール • Filaments • ダウンロード • ディスカッション Fibratusは、幅広いシステムイベントを挙動駆動型のルールエンジンとYARAメモリスキャナーに対して精査・照合することで、高度な攻撃者の手口、マルウェア、新興脅威を検出・根絶します。...

ThreatHound

ThreatHound ThreatHound는 Windows 이벤트 로그에 대한 효율적인 위협 탐지 및 분석을 용이하게 하도록 설계된 고급 사이버 보안 도구입니다. 보안 데이터를 관리하고 분석하기 위한 사용자 친화적인 인터페이스를 제공합니다. 주요 기능으로는 로그 분석, Sigma 규칙 통합, 실시간 위협 탐지가 포함됩니다. 주요 기능: Windows 이벤트 로그에 대한 위협 헌팅, 침해 평가, 침해 대응 자동화 소스에서 Sigma 규칙을 매일 다운로드 및 업데이트 포함된 50개 이상의 탐지 규칙 Sigma용 2300개 이상의 ...

reflector

reflector 説明 Burp Suite拡張機能は、ウェブサイトを閲覧中にリアルタイムでページ上のリフレクション型XSSを検出し、以下のような機能を備えています。 レスポンストラブでのリフレクションのハイライト。 このリフレクションで許可されているシンボルのテスト。 リフレクションコンテキストの分析。 Content-Typeホワイトリスト。 使い方 プラグインをインストールした後は、テスト対象のウェブアプリケーションでの作業を開始するだけです。リフレクションが見つかるたびに、reflectorが重大度を定義し、Burp Issueを生成します。 各Burp...

FalconEye

FalconEye: 윈도우 프로세스 인젝션 실시간 탐지 소프트웨어 FalconEye는 실시간 프로세스 인젝션을 위한 윈도우 엔드포인트 탐지 소프트웨어입니다. 커널 모드 드라이버로서, 프로세스 인젝션이 발생하는 즉시실시간으로 탐지하는 것을 목표로 합니다. FalconEye는 커널 모드에서 실행되므로, 다양한 사용자 모드 후크를 회피하려는 프로세스 인젝션 기술에 대해 더 강력하고 신뢰할 수 있는 방어를 제공합니다. 발표 자료는 2021 Blackhat ASIA Arsenal과 슬라이드에서 확인할 수 있습니다. 프로젝트 개요 탐지 ...

Detect-CVE-2017-0144-attack

Python 程序检测 CVE-2017-0144 攻击 该 Python 程序负责实时持续跟踪、监控网络流量。当检测到 CVE-2017-0144（永恒之蓝）攻击时，它会在控制台屏幕上发出警报。 1. 实验环境模型 本次实验在 VMWare 中的 3 台虚拟机上完成：1 台攻击机（Kali Linux），1 台受害者机（Windows 7 - 64bit SP1），1 台监控机（Windows 10）。各机器使用相同的网卡（NAT），并确保它们可以互相 ping 通。一些注意事项： Kali 机器需要注意更新到最新版本 sudo apt update Windows 7 机器关闭防火墙...

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...