AlphaGalileo

An approach for detecting encrypted malware traffic via fully convolutional masked autoencoders

Cybersecurity has always been the focus of Internet research. Malware refers to software intentionally designed to harm computer systems, networks, ...
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 ...
IEEE

Conv4Rec: A 1-by-1 Convolutional Autoencoder for User Profiling Through Joint Analysis of Implicit and Explicit Feedback

Abstract: We introduce a new convolutional autoencoder architecture for user modeling and recommendation tasks with several improvements over the state of the art. First, our model has the flexibility ...
GitHub

Self-Learning-VAE-for-EEG-Artifact-Removal

This repository contains the implementation, benchmarks, and supporting tools for my MSc dissertation project: Self-learning Variational Autoencoder for EEG Artifact Removal (Key code only). Benchmark ...
Scientific Research Publishing

Alghawazi, M., Alghazzawi, D. and Alarifi, S. (2023) Deep Learning Architecture for Detecting SQL Injection Attacks Based on RNN Autoencoder Model. Mathematics, 11, Article No ...

ABSTRACT: SQL injection attacks pose a critical threat to web application security, exploiting vulnerabilities to gain access, or modify sensitive data. Traditional rule-based and machine learning ...