367 matches found
ThreatDetect
ThreatDetect ThreatDetect is a Streamlit-based insider threat detection prototype that analyzes employee activity data and flags potentially risky behavior. It uses a trained XGBoost classifier together with an Isolation Forest anomaly detector to produce organisation-level risk summaries and...
ranger
ranger A tool to support security professionals access and interact with remote Microsoft Windows based systems. This project was conceptualized with the thought process, we did not invent the bow or the arrow, just a more efficient way of using it. Ranger is a command-line driven attack and...
CVE-2020-0665
CVE-2020-0665 - SID Filter Bypass Proof of Concept for CVE-2020-0665, which allows bypassing SID Filtering in Active Directory Forests Cross Forest Attack. Requirements for the attack Target Domain Controller machine must not be patched after February, 2020. Having previously compromised a Domain...
AI-driven-MITRE-Attack
POC : AI-driven MITRE Attack Detection and Alert Enrichment This repository demonstrates a machine learning pipeline for detecting MITRE ATT&CK techniques from logs and enriching the output using a local LLM. Overview The project is divided into two main components : 1. Machine Learning Model ML ...
guardd
Guardd Behavioral anomaly detection for Linux, powered by eBPF and machine learning. Guardd learns how a Linux host normally behaves and flags activity that differs from that baseline. Kernel sensors collect process executions and outbound TCP connection attempts; an Isolation Forest scores the...
SysTrace
SysTrace 2.0 ptrace-based Linux syscall monitor and sandbox for analyzing ELF binaries. SysTrace runs a target inside an isolated Linux namespace environment, traces its system calls and child processes, applies rule-based detection and weighted risk scoring, then uses a Random Forest classifier ...
Log4Shell-vulnerability-CVE-2021-44228-
Log4Shell Threat Detection CVE-2021-44228 Overview This repository provides an in-depth analysis and implementation of a Machine Learning-based Log4Shell CVE-2021-44228 Threat Detection System. It includes: Understanding Log4Shell : What it is and why it is dangerous Dataset Collection : Sources...
Trigger Value Selection in Clean-Label Malware Backdoors: An Effectiveness-Detectability Analysis
Clean-label backdoor poisoning is a threat to machine learning-based malware classifiers because poisoned samples can be inserted into training data without changing their labels. Previous explanation-guided attacks mainly focus on selecting important trigger features, but the values assigned to...
Empirical Evaluation of Machine Learning for Cloud Cryptojacking Detection: Effects of Preprocessing and Evaluation Protocols
Artificial intelligence AI has been widely applied to cloud cryptojacking detection. Classical machine learning models remain among the most widely used approaches for cloud cryptojacking detection. This paper presents an empirical evaluation of six widely used classical machine learning models f...
CVE-2026-85947
Progress MarkLogic Server 12.x releases before 12.1.0 and 11.x releases before 11.3.7 contain an authorization flaw in certain forest-management operations. A remote attacker with forest-management privileges could exploit this flaw to create a persistent unauthorized administrator account,...
CVE-2026-85947 Improper privilege management in Progress MarkLogic Server forest operations
Progress MarkLogic Server 12.x releases before 12.1.0 and 11.x releases before 11.3.7 contain an authorization flaw in certain forest-management operations. A remote attacker with forest-management privileges could exploit this flaw to create a persistent unauthorized administrator account,...
CVE-2026-85947 Improper privilege management in Progress MarkLogic Server forest operations
Progress MarkLogic Server 12.x releases before 12.1.0 and 11.x releases before 11.3.7 contain an authorization flaw in certain forest-management operations. A remote attacker with forest-management privileges could exploit this flaw to create a persistent unauthorized administrator account,...
CVE-2026-85947
Progress MarkLogic Server versions 12.x before 12.1.0 and 11.x before 11.3.7 are vulnerable to an authorization flaw within specific forest-management operations . A remote attacker possessing forest-management privileges can exploit this flaw to create a persistent unauthorized administrator acc...
EUVD-2026-97505
Progress MarkLogic Server 12.x releases before 12.1.0 and 11.x releases before 11.3.7 contain an authorization flaw in certain forest-management operations. A remote attacker with forest-management privileges could exploit this flaw to create a persistent unauthorized administrator account,...
PT-2026-110640
Name of the Vulnerable Software and Affected Versions MarkLogic Server versions 11.0.0 through 11.3.6 MarkLogic Server versions 12.0.0 through 12.0.9 Description An authorization flaw exists in certain forest-management operations. A remote attacker who already possesses forest-management...
CVE-2026-85947: Improper Privilege Management
Progress MarkLogic Server 12.x releases before 12.1.0 and 11.x releases before 11.3.7 contain an authorization flaw in certain forest-management operations. A remote attacker with forest-management privileges could exploit this flaw to create a persistent unauthorized administrator account,...
batea
Batea Una batea es una bandeja grande y poco profunda de madera o hierro utilizada tradicionalmente por los buscadores de oro para lavar arena y grava y recuperar pepitas de oro. Batea es un marco de clasificación de dispositivos de red basado en contexto que utiliza la familia de algoritmos de...
Adversarial Debiasing of Machine Learning Models for Enhanced Network Security against DDoS Attacks
Distributed Denial of Service attacks are a growing threat to network infrastructure, and new techniques, including the use of generative AI, make them harder to detect. Traditional detection systems, such as rule based firewalls, often fail to identify these evolving attack patterns. In this...
Unmasking Shortcut Learning in IoT Intrusion Detection: A Forensic, Multi-Paradigm Evaluation of Feature Dependence and Data Leakage
Machine learning-based Network Intrusion Detection Systems often report near-perfect performance on IoT benchmarks. However, whether these models learn generalizable attack behavior or exploit spurious dataset shortcuts- such as static testbed IP/MAC addresses and chronological recording...
