IEEE

Detectability Driven Recommendation of Anomaly Detection Models for Time-Series Data

Abstract: Anomaly detection for time-series data has been viewed widely in many practical applications and caused lots of research interests. A popular solution based on deep learning techniques is ...
Dark Reading

AsyncRAT Malware Infests Orgs via Python & Cloudflare

The phishing campaign shows how attackers continue to weaponize legitimate cloud services and open source tools to evade ...
GitHub

Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection

07.2025: Dinomaly has been integrated in Intel open-edge Anomalib in v2.1.0. Great thanks to the contributors for the nice reproduction and integration. Anomalib is a comprehensive library for ...
IEEE

Anomaly Detection Model in Network Security Situational Awareness Based on Machine Learning: Limitation, Techniques, and Future Trends

Abstract: This study focuses on the anomaly detection problem in Network Security Situational Awareness (NSSA). We systematically review traditional approaches and recent advancements based on Machine ...
InfoWorld

PythoC: A new way to generate C code from Python

PythoC lets you use Python as a C code generator, but with more features and flexibility than Cython provides. Here’s a first look at the new C code generator for Python. Python and C share more than ...
GitHub

Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection

[CVPR 2025] Official Implementation of "Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection". The first multi-class UAD model that can compete with single-class SOTAs - ...
Futurism

Malware Is Now Using AI to Rewrite Its Own Code to Avoid Detection

Researchers at Google’s Threat Intelligence Group (GTIG) have discovered that hackers are creating malware that can harness the power of large language models (LLMs) to rewrite itself on the fly. An ...
Hacker

An Overview of Log-Based Anomaly Detection Techniques

5.1 RQ1: How does our proposed anomaly detection model perform compared to the baselines? 5.2 RQ2: How much does the sequential and temporal information within log sequences affect anomaly detection?
Frontiers

Anomaly detection in netflow traffic: workflow for dataset preparation and analysis

Information and communication technology (ICT) is crucial for maintaining efficient communications, enhancing processes, and enabling digital transformation. As ICT becomes increasingly significant in ...
Scientific Research Publishing

Graph Neural Networks for Anomaly Detection in Cloud Infrastructure ()

Cloud infrastructure anomalies cause significant downtime and financial losses (estimated at $2.5 M/hour for major services). Traditional anomaly detection methods fail to capture complex dependencies ...
maketecheasier.com

Google Colab Allows You to Write and Run Python Code in Your Browser – Getting Started Guide

Google Colab, also known as Colaboratory, is a free online tool from Google that lets you write and run Python code directly in your browser. It works like Jupyter Notebook but without the hassle of ...
Scientific Research Publishing

Goldstein, M. and Uchida, S. (2016) A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data. PLOS ONE, 11, e0152173.

ABSTRACT: Purpose: The purpose of this study is to develop a scalable, risk-aware artificial intelligence (AI) framework capable of detecting financial fraud in high-throughput digital transaction ...