class_struggle

Class Struggle 리포지토리는 "Class Struggle" 기사를 보조합니다. 이 리포지토리는 CERT Polska가 공개한 피싱 도메인의 알려진 하위 집합과 유사한 도메인을 탐지하기 위한 오토인코더 사용을 보여줍니다. 데이터 출처 CERT Polska 경고 목록 이 프로젝트는 CERT Polska NASK가 유지 관리하는 CERT Polska 경고 목록 https://cert.pl/en/warning-list/ 의 데이터를 사용합니다. 데이터는 연구 및 교육 목적으로만 사용됩니다. 도메인의 원시 목록은 이 리포지토...

netsentryx

NetSentryx PRO 🛡️ AI 기반의 고성능 실시간 네트워크 관측 및 위협 탐지 엔진 NetSentryx PRO는 차세대 이벤트 기반 네트워크 모니터링 및 위협 인텔리전스 플랫폼입니다. 규칙 기반 시그니처와 PyTorch 딥 오토인코더를 결합하여 10ms 미만의 패킷 및 넷플로우 이상 탐지, 자동화된 다중 채널 경고Slack, Discord, Email, 그리고 대화형 실시간 SOC 대시보드를 제공합니다. 🏗️ 시스템 아키텍처 다음 흐름 다이어그램은 데이터 수집, 실시간 딥러닝 추론, 경고 파이프라인, 그리고...

A Hybrid Approach to Malware Detection: Integrating Few-Shot Model-Agnostic Meta-Learning with Autoencoders

Ransomware has emerged as a major cybersecurity threat, with incidents increasing in frequency and impact across critical sectors. These attacks are typically launched through phishing emails, malicious downloads, or exploitation of software vulnerabilities to gain system access. Once inside, the...

Cybersecurity in Edge Computing: A Trust-Aware Federated Hybrid Intrusion Detection Framework

Edge computing has emerged as a critical computing paradigm in modern distributed systems by migrating data processing closer to end users and Internet of Things IoT devices. While this paradigm decentralizes processes, minimizes latency, and reduces backhaul bandwidth congestion, it exponentiall...

netsentryx — Updated!

NetSentryx PRO 🛡️ AI-Driven, High-Performance Real-Time Network Observability & Threat Detection Engine NetSentryx PRO is a next-generation, event-driven network monitoring and threat intelligence platform. Combining rule-based signatures with a PyTorch Deep Autoencoder, it provides sub-10ms packe...

Cyber-Electromagnetic Anomaly Detection through Time-Series Analysis

Military operations benefit from the coordination between kinetic and non-kinetic domains. In particular, the coordination of cyber operations and electromagnetic warfare has become increasingly relevant for gaining operational advantage. This coordination is also relevant for Cyber Situational...

Identifying Agentic Automation with Behavioral Telemetry

Learn how Akamai uses Masked Autoencoder Transformer models to detect sparse behavioral telemetry from autonomous AI browser agents, such as Comet...

Benchmarking Cyberattack Detection in Electric Vehicle Charging Infrastructure with Benign User Updates

Cyberattack detection in electric vehicle charging infrastructure is complicated by legitimate post-activation revisions to requested energy and departure time. Charging manipulation attacks can exploit the same interface and variables; therefore, detecting a request change alone does not establi...

F-ACVAE: A Federated Adaptive Conditional Variational Auto-Encoder for Privacy-Preserving Intrusion Detection in IoT Networks

The rapid proliferation of Internet of things IoT devices has significantly expanded the cyber-attack surface, necessitating robust and privacy-preserving intrusion detection systems IDS. However, centralized learning approaches often suffer from severe performance degradation due to...

Detecting Adversarial Evasion Attacks against Autoencoder-Based Network Intrusion Detection Systems

Evasion attacks deliberately manipulate input to an ML-based system to produce an incorrect prediction while the manipulated input still appears benign. The PANDA framework has demonstrated that adversarial examples developed for the vision domain can be transferred to the network domain by...

Learning to Look Benign: Targeted Evasion of Malware Detectors Via API Import Injection

Machine learning-based malware detectors are widely deployed in antivirus and endpoint detection systems, yet their reliance on static features makes them vulnerable to adversarial manipulation. This paper investigates whether a malware sample can be intentionally misclassified as a specific beni...

API Security Based on Automatic OpenAPI Mapping

This paper presents Map Reduce Graph MRG, a novel unsupervised method for modeling and securing HTTP REST APIs. MRG learns API structure from real-world traffic without prior knowledge or labels, automatically generating OpenAPI-compliant documentation by reconstructing routes, methods, and...

Enhancing Network Intrusion Detection Systems: A Multi-Layer Ensemble Approach to Mitigate Adversarial Attacks

Adversarial examples can represent a serious threat to machine learning ML algorithms. If used to manipulate the behaviour of ML-based Network Intrusion Detection Systems NIDS, they can jeopardize network security. In this work, we aim to mitigate such risks by increasing the robustness of NIDS...

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...

CVE-2025-57622

An issue in Step-Video-T2V allows a remote attacker to execute arbitrary code via the /vae-api , /caption-api , feature = pickle.loadsrequest.getdata component...

CVE-2025-57622

An issue in Step-Video-T2V allows a remote attacker to execute arbitrary code via the /vae-api , /caption-api , feature = pickle.loadsrequest.getdata component...

How the Graph Construction Technique Shapes Performance in IoT Botnet Detection

The increasing incidence of IoT-based botnet attacks has driven interest in advanced learning models for detection. Recent efforts have focused on leveraging attention mechanisms to model long-range feature dependencies and Graph Neural Networks GNNs to capture relationships between data instance...

Influence of Autoencoder Latent Space on Classifying IoT CoAP Attacks

The Internet of Things IoT presents a unique cybersecurity challenge due to its vast network of interconnected, resource-constrained devices. These vulnerabilities not only threaten data integrity but also the overall functionality of IoT systems. This study addresses these challenges by explorin...

Automating Agent Hijacking Via Structural Template Injection

Agent hijacking, highlighted by OWASP as a critical threat to the Large Language Model LLM ecosystem, enables adversaries to manipulate execution by injecting malicious instructions into retrieved content. Most existing attacks rely on manually crafted, semantics-driven prompt manipulation, which...

Collaborative Zone-Adaptive Zero-Day Intrusion Detection for IoBT

The Internet of Battlefield Things IoBT relies on heterogeneous, bandwidth-constrained, and intermittently connected tactical networks that face rapidly evolving cyber threats. In this setting, intrusion detection cannot depend on continuous central collection of raw traffic due to disrupted link...