Endpoint Security Agent: A Comprehensive Approach to Real-time System Monitoring and Threat Detection
- URL: http://arxiv.org/abs/2511.08352v1
- Date: Wed, 12 Nov 2025 01:54:56 GMT
- Title: Endpoint Security Agent: A Comprehensive Approach to Real-time System Monitoring and Threat Detection
- Authors: Srihari R, Ayesha Taranum, Karthik, Mohammed Usman Hussain,
- Abstract summary: This paper presents "Endpoint Security Agent: A Comprehensive Approach to Real-time System Monitoring and Threat Detection"<n>A machine learning-based detection engine, trained on labelled datasets of benign and malicious activity, enables accurate threat identification with minimal false positives.<n>The system includes a centralized interface for alerting and forensic analysis.
- Score: 0.3266916057202441
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: As cyber threats continue to evolve in complexity and frequency, robust endpoint protection is essential for organizational security. This paper presents "Endpoint Security Agent: A Comprehensive Approach to Real-time System Monitoring and Threat Detection" a modular, real-time security solution for Windows endpoints. The agent leverages native tools like WMI and ETW for lowlevel monitoring of system activities such as process execution, registry modifications, and network behaviour. A machine learning-based detection engine, trained on labelled datasets of benign and malicious activity, enables accurate threat identification with minimal false positives. Detection techniques are mapped to the MITRE ATT&CK framework for standardized threat classification. Designed for extensibility, the system includes a centralized interface for alerting and forensic analysis. Preliminary evaluation shows promising results in detecting diverse attack vectors with high accuracy and efficiency.
Related papers
- Detecting Object Tracking Failure via Sequential Hypothesis Testing [80.7891291021747]
Real-time online object tracking in videos constitutes a core task in computer vision.<n>We propose interpreting object tracking as a sequential hypothesis test, wherein evidence for or against tracking failures is gradually accumulated over time.<n>We propose both supervised and unsupervised variants by leveraging either ground-truth or solely internal tracking information.
arXiv Detail & Related papers (2026-02-13T14:57:15Z) - ORCA -- An Automated Threat Analysis Pipeline for O-RAN Continuous Development [57.61878484176942]
Open-Radio Access Network (O-RAN) integrates numerous software components in a cloud-like deployment, opening the radio access network to previously unconsidered security threats.<n>Current vulnerability assessment practices often rely on manual, labor-intensive, and subjective investigations, leading to inconsistencies in the threat analysis.<n>We propose an automated pipeline that leverages Natural Language Processing (NLP) to minimize human intervention and associated biases.
arXiv Detail & Related papers (2026-01-20T07:31:59Z) - CaMeLs Can Use Computers Too: System-level Security for Computer Use Agents [60.98294016925157]
AI agents are vulnerable to prompt injection attacks, where malicious content hijacks agent behavior to steal credentials or cause financial loss.<n>We introduce Single-Shot Planning for CUAs, where a trusted planner generates a complete execution graph with conditional branches before any observation of potentially malicious content.<n>Although this architectural isolation successfully prevents instruction injections, we show that additional measures are needed to prevent Branch Steering attacks.
arXiv Detail & Related papers (2026-01-14T23:06:35Z) - OS-Sentinel: Towards Safety-Enhanced Mobile GUI Agents via Hybrid Validation in Realistic Workflows [77.95511352806261]
Computer-using agents powered by Vision-Language Models (VLMs) have demonstrated human-like capabilities in operating digital environments like mobile platforms.<n>We propose OS-Sentinel, a novel hybrid safety detection framework that combines a Formal Verifier for detecting explicit system-level violations with a Contextual Judge for assessing contextual risks and agent actions.
arXiv Detail & Related papers (2025-10-28T13:22:39Z) - MCPGuard : Automatically Detecting Vulnerabilities in MCP Servers [16.620755774987774]
The Model Context Protocol (MCP) has emerged as a standardized interface enabling seamless integration between Large Language Models (LLMs) and external data sources and tools.<n>This paper systematically analyzes the security landscape of MCP-based systems, identifying three principal threat categories.
arXiv Detail & Related papers (2025-10-27T05:12:51Z) - VeriGuard: Enhancing LLM Agent Safety via Verified Code Generation [40.594947933580464]
The deployment of autonomous AI agents in sensitive domains, such as healthcare, introduces critical risks to safety, security, and privacy.<n>We introduce VeriGuard, a novel framework that provides formal safety guarantees for LLM-based agents.
arXiv Detail & Related papers (2025-10-03T04:11:43Z) - BlindGuard: Safeguarding LLM-based Multi-Agent Systems under Unknown Attacks [58.959622170433725]
BlindGuard is an unsupervised defense method that learns without requiring any attack-specific labels or prior knowledge of malicious behaviors.<n>We show that BlindGuard effectively detects diverse attack types (i.e., prompt injection, memory poisoning, and tool attack) across multi-agent systems.
arXiv Detail & Related papers (2025-08-11T16:04:47Z) - CANDoSA: A Hardware Performance Counter-Based Intrusion Detection System for DoS Attacks on Automotive CAN bus [45.24207460381396]
This paper presents a novel Intrusion Detection System (IDS) designed for the Controller Area Network (CAN) environment.<n>A RISC-V-based CAN receiver is simulated using the gem5 simulator, processing CAN frame payloads with AES-128 encryption as FreeRTOS tasks.<n>Results indicate that this approach could significantly improve CAN security and address emerging challenges in automotive cybersecurity.
arXiv Detail & Related papers (2025-07-19T20:09:52Z) - CANTXSec: A Deterministic Intrusion Detection and Prevention System for CAN Bus Monitoring ECU Activations [53.036288487863786]
We propose CANTXSec, the first deterministic Intrusion Detection and Prevention system based on physical ECU activations.<n>It detects and prevents classical attacks in the CAN bus, while detecting advanced attacks that have been less investigated in the literature.<n>We prove the effectiveness of our solution on a physical testbed, where we achieve 100% detection accuracy in both classes of attacks while preventing 100% of FIAs.
arXiv Detail & Related papers (2025-05-14T13:37:07Z) - Exposing the Ghost in the Transformer: Abnormal Detection for Large Language Models via Hidden State Forensics [5.384257830522198]
Large Language Models (LLMs) in critical applications have introduced severe reliability and security risks.<n>These vulnerabilities have been weaponized by malicious actors, leading to unauthorized access, widespread misinformation, and compromised system integrity.<n>We introduce a novel approach to detecting abnormal behaviors in LLMs via hidden state forensics.
arXiv Detail & Related papers (2025-04-01T05:58:14Z) - Autonomous Identity-Based Threat Segmentation in Zero Trust Architectures [4.169915659794567]
Zero Trust Architectures (ZTA) fundamentally redefine network security by adopting a "trust nothing, verify everything" approach.<n>This research applies the proposed AI-driven, autonomous, identity-based threat segmentation in ZTA.
arXiv Detail & Related papers (2025-01-10T15:35:02Z) - CryptoFormalEval: Integrating LLMs and Formal Verification for Automated Cryptographic Protocol Vulnerability Detection [41.94295877935867]
We introduce a benchmark to assess the ability of Large Language Models to autonomously identify vulnerabilities in new cryptographic protocols.
We created a dataset of novel, flawed, communication protocols and designed a method to automatically verify the vulnerabilities found by the AI agents.
arXiv Detail & Related papers (2024-11-20T14:16:55Z) - IDU-Detector: A Synergistic Framework for Robust Masquerader Attack Detection [3.3821216642235608]
In the digital age, users store personal data in corporate databases, making data security central to enterprise management.
Given the extensive attack surface, assets face challenges like weak authentication, vulnerabilities, and malware.
We introduce the IDU-Detector, integrating Intrusion Detection Systems (IDS) with User and Entity Behavior Analytics (UEBA)
This integration monitors unauthorized access, bridges system gaps, ensures continuous monitoring, and enhances threat identification.
arXiv Detail & Related papers (2024-11-09T13:03:29Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.