batea

Batea Batea（バテア）とは、金の採掘者が砂や砂利を洗って金塊を回収するために伝統的に使用する、木製または鉄製の浅い大きなパンのことです。 Batea は、異常検知アルゴリズムファミリーに基づく文脈駆動型ネットワークデバイスランキングフレームワークです。Batea の目標は、セキュリティチームが nmap スキャンレポートを使用して大規模ネットワーク内の 重要なネットワークアセットを自動的にフィルタリング できるようにすることです。私たちはこれらを Gold Nuggets（金塊） と呼んでいます。 Gold Nuggeting と Batea...

deep-loglizer

Deep-loglizer Deep-loglizer es un conjunto de herramientas de análisis de registros logs basado en aprendizaje profundo para la detección automatizada de anomalías. Si utilizas deep-loglizer en tu investigación para publicación, por favor cita el siguiente artículo: Zhuangbin Chen, Jinyang Liu,...

syzkaller

syzkaller - fuzzizzatore del kernel syzkaller siːzˈkɔːlə è un fuzzizzatore del kernel non supervisionato e guidato dalla copertura del codice. Sistemi operativi supportati: FreeBSD, Fuchsia, gVisor, Linux, NetBSD, OpenBSD, Windows. Mailing list: [email protected] iscriviti tramite web o emai...

The Quantum Ad-List

The Quantum Ad-List Blocco Pubblicità e Tracker di Nuova Generazione basato su AI 📜 Indice 1. Avviso Legale ed Etico 2. Panoramica del Progetto Manutentori Architettura Tecnica Motivazione Sicurezza e Privacy 3. Installazione Integrazione File Hosts Compatibilità Ad-Blocker Integrazione Pi-hole 4...

A Robust Framework for Sybil Attack Detection in Vehicular Ad Hoc Networks

Sybil attacks create an illusion of traffic congestion by utilizing fake identities, which undermines the reliable and safe operation of vehicular ad hoc networks VANETs. Existing detection mechanisms struggle to effectively handle Sybil attacks as they are i susceptible to high false positive...

[SECURITY] Fedora 44 Update: sentencepiece-0.2.1-1.fc44

The SentencePiece is an unsupervised text tokenizer for Neural Network-based text generation. It is an unsupervised text tokenizer and detokenizer mainly for Neural Network-based text generation systems where the vocabulary size is predetermined prior to the neural model training. SentencePiece...

CLIF: Cross-Layer LEO-ISL Fingerprinting for Physical and Network Attack Detection in Dense LEO Constellations

Low-Earth Orbit LEO mega-constellations such as Starlink by SpaceX and Kuiper by Amazon rely on optical Inter-Satellite Links ISLs for autonomous mesh routing to provide low-latency telecommunication, Internet of Things IoT, and security services globally. As commercial operators and governments...

Evaluating Tabular Representation Learning for Network Intrusion Detection

Classic Network Intrusion Detection Systems NIDS often rely on manual feature engineering to extract meaningful patterns from network traffic data. However, this approach requires domain expertise and runs counter to the widely adopted principle of modern machine learning and neural networks: tha...

System-Aware Contextual Digital Twin for ICS Anomaly Diagnosis

Industrial Control Systems ICS integrate computing, physical processes, and communication to operate critical infrastructures such as power grids, water treatment plants, and oil and gas facilities. As ICS become increasingly targeted by cyberattacks, timely and reliable anomaly diagnosis is...

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...

Reference-Free EM Validation Flow for Detecting Triggered Hardware Trojans

Hardware Trojans HTs threaten the trust and reliability of integrated circuits ICs, particularly when triggered HTs remain dormant during standard testing and activate only under rare conditions. Existing electromagnetic EM side-channel-based detection techniques often rely on golden references o...

Engineering Attack Vectors and Detecting Anomalies in Additive Manufacturing

Additive manufacturing AM is rapidly integrating into critical sectors such as aerospace, automotive, and healthcare. However, this cyber-physical convergence introduces new attack surfaces, especially at the interface between computer-aided design CAD and machine execution layers. In this work, ...

Beyond Detection: A Comprehensive Benchmark and Study on Representation Learning for Fine-Grained Webshell Family Classification

Malicious WebShells pose a significant and evolving threat by compromising critical digital infrastructures and endangering public services in sectors such as healthcare and finance. While the research community has made significant progress in WebShell detection i.e., distinguishing malicious...

Hybrid Quantum-Classical Autoencoders for Unsupervised Network Intrusion Detection

Unsupervised anomaly-based intrusion detection requires models that can generalize to attack patterns not observed during training. This work presents the first large-scale evaluation of hybrid quantum-classical HQC autoencoders for this task. We construct a unified experimental framework that...

AutoGraphAD: A Novel Approach Using Variational Graph Autoencoders for Anomalous Network Flow Detection

Network Intrusion Detection Systems NIDS are essential tools for detecting network attacks and intrusions. While extensive research has explored the use of supervised Machine Learning for attack detection and characterisation, these methods require accurately labelled datasets, which are very...

LFreeDA: Label-Free Drift Adaptation for Windows Malware Detection

Machine learning ML-based malware detectors degrade over time as concept drift introduces new and evolving families unseen during training. Retraining is limited by the cost and time of manual labeling or sandbox analysis. Existing approaches mitigate this via drift detection and selective...

HYDRA: A Hybrid Heuristic-Guided Deep Representation Architecture for Predicting Latent Zero-Day Vulnerabilities in Patched Functions

Software security testing, particularly when enhanced with deep learning models, has become a powerful approach for improving software quality, enabling faster detection of known flaws in source code. However, many approaches miss post-fix latent vulnerabilities that remain even after patches...

BLADE: Behavior-Level Anomaly Detection Using Network Traffic in Web Services

With their widespread popularity, web services have become the main targets of various cyberattacks. Existing traffic anomaly detection approaches focus on flow-level attacks, yet fail to recognize behavior-level attacks, which appear benign in individual flows but reveal malicious purpose using...

Applying Graph Analysis for Unsupervised Fast Malware Fingerprinting

Malware proliferation is increasing at a tremendous rate, with hundreds of thousands of new samples identified daily. Manual investigation of such a vast amount of malware is an unrealistic, time-consuming, and overwhelming task. To cope with this volume, there is a clear need to develop...

Characterizing Event-Themed Malicious Web Campaigns: A Case Study on War-Themed Websites

Cybercrimes such as online scams and fraud have become prevalent. Cybercriminals often abuse various global or regional events as themes of their fraudulent activities to breach user trust and attain a higher attack success rate. These attacks attempt to manipulate and deceive innocent people int...