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21 July 2025
Reseach Article

Temporal-Spatial Deep Learning Framework for Real-Time Intrusion Detection in Cloud Environments

by Neethu B., Sheena Mathew
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 19
Year of Publication: 2025
Authors: Neethu B., Sheena Mathew
10.5120/ijca2025925288

Neethu B., Sheena Mathew . Temporal-Spatial Deep Learning Framework for Real-Time Intrusion Detection in Cloud Environments. International Journal of Computer Applications. 187, 19 ( Jul 2025), 38-42. DOI=10.5120/ijca2025925288

@article{ 10.5120/ijca2025925288,
author = { Neethu B., Sheena Mathew },
title = { Temporal-Spatial Deep Learning Framework for Real-Time Intrusion Detection in Cloud Environments },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2025 },
volume = { 187 },
number = { 19 },
month = { Jul },
year = { 2025 },
issn = { 0975-8887 },
pages = { 38-42 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number19/temporal-spatial-deep-learning-framework-for-real-time-intrusion-detection-in-cloud-environments/ },
doi = { 10.5120/ijca2025925288 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-07-09T01:07:44.303202+05:30
%A Neethu B.
%A Sheena Mathew
%T Temporal-Spatial Deep Learning Framework for Real-Time Intrusion Detection in Cloud Environments
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 19
%P 38-42
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

With the rapid advancement of cloud computing, security breaches and intrusion attempts have become increasingly sophisticated and real-time. Traditional intrusion detection systems often fall short in identifying and detecting the evolving threats within dynamic cloud environments because they lack adaptability and struggle with effective feature representation. To address this, this paper proposes a new Temporal-Spatial Deep Learning (TSDL) framework that combines Convolutional Neural Networks (CNNs) for capturing spatial features with Long Short-Term Memory (LSTM) networks to learn temporal patterns in cloud network traffic. The proposed model pre-processes sequential packet data while keeping track of data flow, which enables early and accurate intrusion detection of the system. The system is evaluated on benchmark datasets such as CICIDS2017 and UNSW-NB15, and it outperforms traditional machine learning models and regular deep learning networks in both detection accuracy and processing latency. This system is designed to operate in real-time, making it suitable for deployment in large-scale cloud infrastructures.

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Index Terms

Computer Science
Information Sciences

Keywords

Cloud Security Convolutional Neural Network (CNN) Recurrent Neural Network (RNN) Temporal-Spatial Features Real-Time Detection Intrusion Detection System (IDS) Deep Learning