An Intelligent Hybrid Learning Model for Real Time Cloud Security Threat Detection in Smart Cities

1. R Ashokkumar, ,2. Navinkumar K, ,3. Srikanth E, ,4. S.Sabaridharan, ,5. Ragul R, ,rajagopal

Publisher: BR Publications

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Pages: 31-36

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Date of Conference: may 2020DOI: http://https://ieeexplore.ieee.org/document/730709ISBN: 979-8-3315-7814-

Abstract

The risk of sophisticated cyberattacks against

real-time services has dramatically grown due to the quick

adoption of cloud computing in smart city infrastructures.

Due to their high false alarm rates and limited flexibility,

traditional security measures frequently fail to identify clever

and changing crimes. An intelligent hybrid learning approach

for real-time cloud security threat detection in smart cities is

proposed in this research. The suggested method combines

deep learning and machine learning approaches to improve

classification

accuracy,

identification,

temporal

attack

pattern

and feature representation. Standard

performance indicators, including as accuracy, precision,

recall, F1-score, false alarm rate, and detection time, are used

in extensive tests to assess the model's efficiency. According to

experimental data, the suggested hybrid model performs

better, achieving an accuracy of 96.3% and drastically

lowering the false alarm rate to 2.1%. Its viability for real

time deployment in cloud systems for smart cities is further

confirmed by the decreased detection time. The suggested

methodology provides a scalable and dependable way to

improve cloud security tolerance.

Published in: TECHNEXA-2020

Published on: July 23, 2026

Introduction

In order to facilitate massive data processing, real

time service delivery, and intelligent decision-making in

fields like public safety, healthcare, transportation, and

energy management, smart cities are depending more and

more on cloud computing infrastructures. The digital

foundation of smart urban ecosystems is formed by the

constant data collecting and analytics made possible by the

integration of Internet of Things (IoT)[1] devices, edge

computing, and cloud platforms. However, a variety of

cyber security risks, such as distributed denial-of-service

(DDoS) attacks, data breaches, malware injection, insider

threats, and advanced persistent threats, are also made

possible by smart cities' heavy reliance on cloud-based

services. These attack have the potential to seriously[2]

impair vital services, jeopardize private citizen

Authors & Affiliations

1. R Ashokkumar,

Department of Artificial Intelligence and Data Science, Paavai College of Engineering, Namakkal, Tamil Nadu, India,

2. Navinkumar K,

Department of Artificial Intelligence and Data Science, Paavai College of Engineering Namakkal, Tamil Nadu, India,

3. Srikanth E,

Department of Artificial Intelligence and Data Science, Paavai College of Engineering, Namakkal, Tamil Nadu, India,

4. S.Sabaridharan,

Department of Artificial Intelligence & Data Science, Paavai College of Engineering, Namakkal, Tamil Nadu, India,

5. Ragul R,

Department of Artificial Intelligence and Data Science, Paavai College of Engineering, Namakkal, Tamil Nadu, India,

rajagopal

anna univ

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The risk of sophisticated cyberattacks against

real-time services has dramatically grown due to the quick

adoption of cloud computing in smart city infrastructures.

Due to their high false alarm rates and limited flexibility,

traditional security measures frequently fail to identify clever

and changing crimes. An intelligent hybrid learning approach

for real-time cloud security threat detection in smart cities is

proposed in this research. The suggested method combines

deep learning and machine learning approaches to improve

classification

accuracy,

identification,

temporal

attack

pattern

and feature representation. Standard

performance indicators, including as accuracy, precision,

recall, F1-score, false alarm rate, and detection time, are used

in extensive tests to assess the model's efficiency. According to

experimental data, the suggested hybrid model performs

better, achieving an accuracy of 96.3% and drastically

lowering the false alarm rate to 2.1%. Its viability for real

time deployment in cloud systems for smart cities is further

confirmed by the decreased detection time. The suggested

methodology provides a scalable and dependable way to

improve cloud security tolerance.