Bridging Number-Theoretic Primality Testing and Algorithmic Optimization: An Adaptive Cryptographic Framework

The security of modern public-key cryptographic systems, such as RSA, Diffie-Hellman, and ECC, depends entirely on the computational intractability of the integer factorization and discrete logarithm problems. For these asymmetric algorithms to operate, a core requirement is that the system must ...

AIRO: A Streaming-Based Machine Learning Framework for Threat Detection and Security in Industrial Internet of Things

Modern Industrial Internet of Things IIoT im- plementations now connect edge gateways, cloud dashboards and business applications. This improves visibility and remote monitoring. However it simultaneously exposes industrial traffic to various cyber threats including scanning, web attacks and...

cve2risk

CVE2Risk: Explainable Machine Learning for In-the-Wild Exploit...

Adversarial Benchmarks and BenchDoor Attacks: Exploiting Benchmark Incentives for Backdoor Attacks

Machine learning benchmarks act as guideposts for model development and spur the creation of training datasets intended to improve benchmark scores. We introduce BenchDoor, a family of data poisoning attacks that exploits this incentive. Beyond preserving benign performance, BenchDoor requires...

Q-MALDetect: Quantum Image-Based Malware Detection and Adversarial Robustness Analysis

Quantum machine-learning QML models are increasingly considered for security applications, yet their adversarial robustness in malware analysis remains insufficiently understood. This paper presents Q-MALDetect, a hybrid convolutional and variational-quantum detector for byte-image representation...

Mind Your Boundaries: Boundary-Aware Model Obfuscation against Cross-Node Attack

With the advancement of powerful AI chips, on-device machine learning is gaining traction for its low latency and privacy protection. However, when edge devices are compromised, attackers can obtain white-box access to the deployed models by reversing the apps directly to obtain the knowledge of...

SemPurify: Semantics-Aware Data Purification for Black-Box Backdoor Defense

Backdoor attacks have emerged as a critical security threat in machine learning, where adversaries embed triggers within poisoned training samples to manipulate a model's predictions on targeted inputs while leaving its general behavior unaffected. This stealthy nature makes backdoor attacks...

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...

secretflow

简体中文｜English SecretFlow es un marco unificado para la inteligencia de datos y el aprendizaje automático con preservación de la privacidad. Para lograr este objetivo, proporciona: Una capa abstracta de dispositivos que consta de dispositivos simples y dispositivos secretos que encapsulan varios...

columbo

Columbo Columbo es una herramienta de análisis forense informático utilizada para simplificar e identificar patrones específicos en conjuntos de datos comprometidos. Descompone los datos en secciones pequeñas y utiliza reconocimiento de patrones y modelos de aprendizaje automático para identifica...

pulsar

PULSAR Aprendizaje de Protocolos, Simulación y Fuzzer con Estado Pulsar es un fuzzer de red con capacidades automáticas de aprendizaje y simulación de protocolos. La herramienta permite modelar un protocolo mediante técnicas de aprendizaje automático, como clustering y modelos de Markov. Estos...

twebit

Análisis de Bitcoin con aprendizaje automático ¿Cómo funciona? 1- Obtener tweets de Twitter 2- Filtrar tweets 3- Clasificación de tweets con algoritmo Naive Bayes Positivo, negativo y neutro Instalación git clone https://github.com/omergunal/twebit cd twebit pip3 install -r requirements.txt...

logparser

Logparser Logparser fornisce un toolkit di machine learning e benchmark per il parsing automatico dei log, un passaggio fondamentale per l'analisi strutturata dei log. Applicando logparser, gli utenti possono estrarre automaticamente modelli di eventi da log non strutturati e convertire i messagg...

pcfg_cracker

PCFG = Probabilistic Context Free Grammar PCFG = Pretty Cool Fuzzy Guesser En resumen: Una colección de herramientas para investigar cómo los humanos generan contraseñas. Se pueden usar para descifrar hashes de contraseñas, pero también para crear contraseñas sintéticas honeywords o ayudar a...

Hardware-Assisted Multi-Stage Ransomware Detection with Adaptive Machine Learning

Ransomware is a fast-escalating privacy threat causing significant financial and data losses worldwide. As attacks grow more sophisticated, traditional static signatures and dynamic behavior analysis struggle to defend. In this paper, we propose an novel framework that couples software-level...

deep-pwning

Deep-pwning è un framework leggero per sperimentare con modelli di machine learning, con l'obiettivo di valutarne la robustezza contro un avversario motivato. Si noti che deep-pwning nel suo stato attuale è lontano dall'essere maturo o completo. È pensato per essere sperimentato, ampliato ed este...

foolbox

home: true heroImage: /logo.png heroText: Foolbox tagline: "Foolbox: attacchi adversarial veloci per valutare la robustezza dei modelli di machine learning in PyTorch, TensorFlow e JAX" actionText: Inizia → actionLink: /guide/ features: title: Prestazioni native details: Foolbox 3 è basata su...

WebAttackDetector

WebAttackDetector Static machine-learning triage for suspicio...

K000163580: Elasticsearch vulnerability CVE-2026-56149

Security Advisory Description Allocation of Resources Without Limits or Throttling CWE-770 in Elasticsearch can lead to a denial of service via Excessive Allocation CAPEC-130. A user with elevated privileges can submit a specially crafted machine learning request that causes excessive memory...

CLEANSTART-2026-CF09659 Deserialization of Untrusted Data (CWE-502) in the Elasticsearch machine learning component can lead to remote code execution via Object Injection (CAPEC-586)

Security vulnerability affects the sonarqube package. Deserialization of Untrusted Data CWE-502 in the Elasticsearch machine learning component can lead to remote code execution via Object Injection CAPEC-586...