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 ...
Bleeping Computer

New SantaStealer malware steals data from browsers, crypto wallets

A new malware-as-a-service (MaaS) information stealer named SantaStealer is being advertised on Telegram and hacker forums as operating in memory to avoid file-based detection. According to security ...
Dark Reading

Hamas-Linked Hackers Probe Middle Eastern Diplomats

A cyber threat group affiliated with Hamas has been conducting espionage across the Middle East. "Wirte" — tracked by Palo Alto's Unit 42 as "Ashen Lepus" — has been spying on regional government ...
IEEE

A Comparative Study on Malware Detection Using Supervised Machine Learning Models

Abstract: Traditional signature-based systems struggle to detect novel and variably structures threats such as polymorphic and metamorphic malware. These systems rely on predefined rules, which limit ...
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 ...
PC Gamer

Great, now even malware is using LLMs to rewrite its code, says Google, as it documents new phase of 'AI abuse'

I'm sorry Dave, I'm afraid I can't do that. When you purchase through links on our site, we may earn an affiliate commission. Here’s how it works. Google has recently warned "adversaries are no longer ...
Electronic Design

How Multi-Stage Detection Prevents Silent Data Corruption in AI Processors

GenAI and ML workloads are causing a ramp up in silent data corruption. Multi-stage detection with on-chip, AI-based telemetry offers smarter fault prevention. As transistor geometries shrink and ...
IEEE

MiniVHS-22: A Realistic Multi-Source Dataset for Intelligent Malware Detection Using Machine Learning

Abstract: The rapid growth of malware and cyber threats demands intelligent methods for network traffic analysis. While machine learning (ML) techniques have shown high potential, their success is ...
Scientific Research Publishing

Naseer, M., Ullah, F., Ijaz, S., Naeem, H., Alsirhani, A., Alwakid, G. N. et al. (2025). Obfuscated Malware Detection and Classification in Network Traffic Leveraging Hybrid ...

ABSTRACT: Artificial intelligence (AI) is eroding traditional de-identification practices by enabling accurate re-identification of images, text and behavioural traces. A systematic review of 64 ...
Frontiers

Obfuscated malicious traffic detection based on data enhancement

As the proportion of encrypted traffic increases, it becomes increasingly challenging for network attacks to be discovered. Although existing methods combine unencrypted statistical features, e.g., ...