Securing IoT networks in cloud computing environments: a real-time IDS

The term “Internet of Things” (IoT) encompasses an entire group of gadgets that are capable of connecting to the Internet in order to gather and share data. The IoT paradigm is being pushed into computer networks by numerous highly advanced intrusions. Cloud computing greatly enhances the success of...

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Vydané v:The Journal of supercomputing Ročník 80; číslo 10; s. 14489 - 14519
Hlavní autori: Biswas, Soham, Ansari, Md. Sarfaraj Alam
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Springer US 01.07.2024
Springer Nature B.V
Predmet:
Accuracy
Algorithms
Artificial intelligence
Cloud computing
Communication
Compilers
Computer Science
Cybersecurity
Datasets
Deep learning
Denial of service attacks
Digital signatures
Energy consumption
Feature selection
Infrastructure
Internet of Things
Interpreters
Intrusion detection systems
Machine learning
Processor Architectures
Programming Languages
Random variables
Real time
Smart cities
Deep learning
Feature selection
Intrusion detection system
Machine learning
ISSN:0920-8542, 1573-0484
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Shrnutí:The term “Internet of Things” (IoT) encompasses an entire group of gadgets that are capable of connecting to the Internet in order to gather and share data. The IoT paradigm is being pushed into computer networks by numerous highly advanced intrusions. Cloud computing greatly enhances the success of the IoT by enabling users to perform computing tasks using Internet-based services accessed through connected devices. This seamless integration of cloud technology and the IoT has become a powerful catalyst, revolutionizing the way we operate. The adoption of a distributed architecture, such as cloud computing, exposes the system to potential threats like Distributed Denial of Service (DDoS) and Denial of Service (DoS) attacks. To mitigate these risks, the concept of an intrusion detection system (IDS) has been introduced within the cloud environment. Various machine learning (ML) and deep learning (DL) algorithms have been proposed and implemented to effectively detect and respond to such malicious traffic in the cloud system. For dimension reduction during the training process of those algorithms, multiple independent and hybrid techniques have been proposed. This study presents an efficient ML-based real-time IDS framework with proposed hybrid feature selection techniques. Additionally, in this study, a concise comparative analysis has been conducted using five well-known public datasets. The findings presented in this paper reveal that our proposed IDS achieved a maximum accuracy of 99.98% in identifying malicious traffic.
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ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-024-06021-z