IEEE

Zero-Day Malware Detection Using Autoencoder and Hybrid Deep Learning Model

Abstract: Malware continues to pose a serious threat to cybersecurity, especially with the rise of unknown or zero day attacks that bypass the traditional antivirus tools. This study proposes a hybrid ...
AlphaGalileo

Unmasking Hidden Threats: Adaptive Encrypted Malware Detection via FC-MAE

Sub-headline: BIT researchers introduce Malcom to tackle cross-domain encrypted traffic detection using self-supervised learning. A significant technical pain point in cybersecurity is the heavy ...
Frontiers

Explainable deep learning approaches and clinical insights for cancer biomarker identification

Biomarkers play a pivotal role in contemporary cancer immunotherapy by guiding diagnosis, patient stratification, therapeutic decision-making, and longitudinal assessment of treatment responses.
TechRepublic

New Phishing Attack Turns n8n Into On-Demand Malware Machine

Hackers are abusing n8n workflows to deliver malware and evade detection, according to Cisco Talos, using trusted automation to bypass security defenses. A trusted automation tool has quietly become a ...
Infosecurity-magazine.com

DeepLoad Malware Combines ClickFix With AI-Generated Code to Avoid Detection

A newly uncovered malware campaign is combining ClickFix delivery with AI generated evasion techniques to steal enterprise user accounts and passwords. The attacks are designed to provide intruders ...
SecurityWeek

Cyber Insights 2026: Malware and Cyberattacks in the Age of AI

Security leaders share how artificial intelligence is changing malware, ransomware, and identity-led intrusions, and how defenses must evolve. SecurityWeek’s Cyber Insights 2026 examines expert ...
IEEE

Real-Time Detection of Fileless Malware Using Hybrid Deep Learning with CNN and Autoencoder Anomaly Detection

Abstract: Fileless malware represents a rising and complex challenge in cybersecurity, primarily due to its capability to avoid conventional detection strategies by operating entirely within a ...
Bleeping Computer

New Android malware uses AI to click on hidden browser ads

A new family of Android click-fraud trojans leverages TensorFlow machine learning models to automatically detect and interact with specific advertisement elements. The mechanism relies on visual ...
Nature

Customs fraud detection using a gradient boosting approach for joint classification and risk estimation

Customs revenue represents a major source of funding for most governments. Accordingly, customs administrations worldwide have promoted advanced analytics techniques to detect fraud and enhance ...
Nature

A new hybrid model for improving outlier detection using combined autoencoder and variational autoencoder

In this paper, we propose a new hybrid model, called AVE, that integrates the strengths of Autoencoder (AE) and Variational Autoencoder (VAE) to enhance outlier detection for numerous high-dimensional ...
Infosecurity-magazine.com

Malware Manipulates AI Detection in Latest npm Package Breach

A new attempt to influence AI-driven security scanners has been identified in a malicious npm package. The package, eslint-plugin-unicorn-ts-2 version 1.2.1, appeared to be a TypeScript variant of the ...
Scientific Research Publishing

An, J. and Cho, S. (2015) Variational Autoencoder Based Anomaly Detection Using Reconstruction Probability. Special Lecture on IE, 2, 1-18.

ABSTRACT: Video-based anomaly detection in urban surveillance faces a fundamental challenge: scale-projective ambiguity. This occurs when objects of different physical sizes appear identical in camera ...