Deep learning has become a transformative technology for modern weed detection, offering significant advantages over traditional machine vision in robustness, scalability, and recognition accuracy.

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 ...

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 ...

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Traffic prediction is the core of intelligent transportation system, and accurate traffic speed prediction is the key to optimize traffic management. Currently, the traffic speed prediction model ...