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 ...
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 ...
Dark Reading

AI-Powered 'DeepLoad' Malware Steals Credentials, Evades Detection

Researchers have uncovered a new malware strain capable of stealing credentials immediately after gaining a foothold on a victim network, capturing both stored browser passwords and live keystrokes in ...
TechCrunch

FBI says Iranian hackers are using Telegram to steal data in malware attacks

Iranian government hackers are using Telegram as a way to steal data from hacked dissidents, opposition groups, and journalists who oppose the regime around the world, according to an FBI alert ...
Dark Reading

Nation-State Actor Embraces AI Malware Assembly Line

Pakistan-linked state-sponsored threat group APT36 has begun using AI coding tools to bombard targets with mass produced malware that appears designed to overwhelm defenses not through technical ...
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 ...
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 ...