ZTMP: Zero Touch Management Provisioning Algorithm for the On-boarding of Cloud-native Virtual Network Functions

Arulappan, Arunkumar; Raja, Gunasekaran; Bashir, Ali Kashif; Mahanti, Aniket; Omar, Marwan

doi:10.1007/s11036-023-02260-1

Mobile Netw Appl

2023

DOI: 10.1007/s11036-023-02260-1

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ZTMP: Zero Touch Management Provisioning Algorithm for the On-boarding of Cloud-native Virtual Network Functions

Arunkumar Arulappan,

Gunasekaran Raja,

Ali Kashif Bashir

et al.

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2024

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Cited by 2 publications

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This chapter presents an in-depth study on the application of decision tree-based classifiers for the detection of malware in internet of things (IoT) environments. With the burgeoning expansion of IoT devices, the threat landscape has grown increasingly complex, making traditional security measures insufficient. This study proposes an innovative approach using decision tree algorithms to address the growing concern of IoT malware. The research methodology encompasses a comprehensive analysis of IoT vulnerabilities, focusing on malware threats and the development of a decision tree-based classifier. The classifier is empirically validated using the MaleVis dataset, a rich source of real-world IoT malware data. Performance metrics such as precision, recall, specificity, F1-score, accuracy, and processing time are meticulously evaluated to determine the efficacy of the model.

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Enhancing IoT Security

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Redefining Security With Cyber AI

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This chapter explores the application of advanced deep learning architectures, namely convolutional neural networks (CNNs) and recurrent neural networks (RNNs), for the detection of botnet activities in internet of things (IoT) networks. Addressing the growing concern of IoT security, the study develops and evaluates deep learning models to identify complex patterns of botnet behavior. The models demonstrate high accuracy and precision, outperforming traditional machine learning methods in botnet detection. However, challenges related to the computational demands of these models and the evolving nature of cyber threats are also acknowledged. Future research directions include optimizing these models for diverse IoT environments and enhancing their adaptability to new cyber threats. This research provides valuable insights into the application of neural networks in cybersecurity, offering a promising approach to enhancing IoT security.

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ZTMP: Zero Touch Management Provisioning Algorithm for the On-boarding of Cloud-native Virtual Network Functions

Cited by 2 publications

References 25 publications

Decision Trees Unleashed

Decision Trees Unleashed

Enhancing IoT Security

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