batea

Batea Batea 是一种由木材或铁制成的大型浅盘，传统上被淘金者用来淘洗沙砾以回收金块。 Batea 是一个基于异常检测机器学习算法的上下文驱动网络设备排名框架。其目标是让安全团队能够使用 nmap 扫描报告 自动筛选出大型网络中有趣的网络资产 。我们将这些资产称为 金块 。 关于金块发现以及 Batea 背后科学的更多信息，请参见我们的白皮书这里。 工作原理 Batea 通过从 nmap 报告（XML）中构建所有设备的数值表示（numpy），然后应用异常检测方法来揭示金块。它可以通过向网络元素的数值表示添加特定特征（即有趣特性）来轻松扩展。...

guardd

guardd 基于机器学习的行为异常检测，适用于 Linux，采用 eBPF + 孤立森林 Guardd 收集底层系统事件（进程执行、网络活动），将其聚合成时间窗口特征向量，并使用机器学习模型检测异常行为。 Guardd 专注于检测未知威胁 。 !WARNING 该项目仍在开发中 功能和检测准确率正在积极改进 欢迎反馈、建议和贡献 工作原理 guardd 作为一个单独的 systemd 服务运行，管理数据收集、训练和检测的完整生命周期。 启动时： 如果模型不存在，guardd 开始收集基线行为数据 默认收集 1 天的数据进行初始训练 训练成功后，自动切换到检测模式 运行期间：...

ThreatDetect

ThreatDetect ThreatDetect 是一个基于 Streamlit 的内部威胁检测原型，用于分析员工活动数据并标记潜在风险行为。它使用训练好的 XGBoost 分类器结合孤立森林异常检测器，生成组织级风险摘要和可解释的员工级洞察。 演示 该应用可以使用 Streamlit 本地运行。之前发布了一个部署演示，地址为： https://threatdetectcos720.streamlit.app/ 主要功能 从上传的 CSV 文件进行批量风险检测 组织威胁摘要，包含数量、概率分布和风险等级 使用 SHAP 值对每位员工进行可解释性分析...

AegisUI: Behavioral Anomaly Detection for Structured User Interface Protocols in AI Agent Systems

AI agents that build user interfaces on the fly assembling buttons, forms, and data displays from structured protocol payloads are becoming common in production systems. The trouble is that a payload can pass every schema check and still trick a user: a button might say "View invoice" while its...

Towards Eco Friendly Cybersecurity: Machine Learning Based Anomaly Detection with Carbon and Energy Metrics

The rising energy footprint of artificial intelligence has become a measurable component of US data center emissions, yet cybersecurity research seldom considers its environmental cost. This study introduces an eco aware anomaly detection framework that unifies machine learning based network...

Unsupervised Anomaly Detection for Smart IoT Devices: Performance and Resource Comparison

The rapid expansion of Internet of Things IoT deployments across diverse sectors has significantly enhanced operational efficiency, yet concurrently elevated cybersecurity vulnerabilities due to increased exposure to cyber threats. Given the limitations of traditional signature-based Anomaly...

NegBLEURT Forest: Leveraging Inconsistencies for Detecting Jailbreak Attacks

Jailbreak attacks designed to bypass safety mechanisms pose a serious threat by prompting LLMs to generate harmful or inappropriate content, despite alignment with ethical guidelines. Crafting universal filtering rules remains difficult due to their inherent dependence on specific contexts. To...

Semi-Supervised Supply Chain Fraud Detection with Unsupervised Pre-Filtering

Detecting fraud in modern supply chains is a growing challenge, driven by the complexity of global networks and the scarcity of labeled data. Traditional detection methods often struggle with class imbalance and limited supervision, reducing their effectiveness in real-world applications. This...

QUIC-Exfil: Exploiting QUIC'S Server Preferred Address Feature to Perform Data Exfiltration Attacks

The QUIC protocol is now widely adopted by major tech companies and accounts for a significant fraction of today's Internet traffic. QUIC's multiplexing capabilities, encrypted headers, dynamic IP address changes, and encrypted parameter negotiations make the protocol not only more efficient,...

Zero-Day Botnet Attack Detection in IoV: a Modular Approach Using Isolation Forests and Particle Swarm Optimization

The Internet of Vehicles IoV is transforming transportation by enhancing connectivity and enabling autonomous driving. However, this increased interconnectivity introduces new security vulnerabilities. Bot malware and cyberattacks pose significant risks to Connected and Autonomous Vehicles CAVs, ...