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 ...

This project uses deep learning techniques to detect malware by analyzing file characteristics, byte sequences, and behavioral patterns. It employs Convolutional Neural Networks (CNNs) for image-based ...

Threat actors are testing malware that incorporates large language models (LLMs) to create malware that can evade detection by security tools. In an analysis published earlier this month, Google's ...

ABSTRACT: The research aim is to develop an intelligent agent for cybersecurity systems capable of detecting abnormal user behavior using deep learning methods and ensuring interpretability of ...

This repository contains the source code, scripts, and supplementary materials for the paper: "A New Hybrid Model for Improving Outlier Detection Using Combined Autoencoder and Variational Autoencoder ...

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 ...

Deep learning has emerged as a transformative tool for the automated detection and classification of seizure events from intracranial EEG (iEEG) recordings. In this review, we synthesize recent ...

A new Android malware family, Herodotus, uses random delay injection in its input routines to mimic human behavior on mobile devices and evade timing-based detection by security software. Herodotus, ...

1 Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra, Saudi Arabia 2 InnoV'COM Laboratory-Sup'Com, University of Carthage, Ariana, Tunisia ...