GitHub

aadvaitav/cube-sat-anomaly-detection

This project implements an Bidirectional LSTM Autoencoder-based anomaly detection system for CubeSat telemetry monitoring. It processes multi-channel satellite telemetry data in real-time, detects ...
IEEE

TransCNN: Deep Hybrid Model for Effective Intermittent Fault Diagnosis in Wireless Sensor Networks

Abstract: Intermittent fault diagnosis (IFD) is a vital subject in modern communication and computing networks. Unlike permanent problems, which are simpler to discover and localize, intermittent ...
Scientific Research Publishing

Graph Neural Networks for Anomaly Detection in Cloud Infrastructure ()

Cloud infrastructure anomalies cause significant downtime and financial losses (estimated at $2.5 M/hour for major services). Traditional anomaly detection methods fail to capture complex dependencies ...
Frontiers

Image steganalysis using LSTM fused convolutional neural networks for secure telemedicine

Deep learning-based image steganalysis has progressed in recent times, with efforts more concerted toward prioritizing detection accuracy over lightweight frameworks. In the context of AI-driven ...
Scientific Research Publishing

Alrawais, A., Alhothaily, A., Hu, C. and Cheng, X. (2021) LSTM-Based Intrusion Detection for IoT Networks. IEEE Transactions on Network and Service Management, 18, 1712-1725.

ABSTRACT: The rapid proliferation of the Internet of Things (IoT) and Industrial IoT (IIoT) has revolutionized industries through enhanced connectivity and automation. However, this expansion has ...
IEEE

Video Anomaly Detection using Variational Convolutional LSTM Autoencoder

Abstract: Unintentional, inadvertent, unanticipated, or unplanned events are referred to as anomalies or abnormal events. Anomaly detection in surveiiance video has been a topic of active research for ...