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Data Security in IoT Networks

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 31 October 2024 | Viewed by 779

Special Issue Editors


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Guest Editor
Department of Computer Science, University of Bari, 70125 Bari, Italy
Interests: data privacy in big data; privacy-preserving data analytics; privacy-preserving social network data; improving privacy awareness in data sharing

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Guest Editor
Department of Computer Science, University of Salerno, 84084 Fiscino, Italy
Interests: Artificial Intelligence; Data Profiling; Data Integration and Data Warehousing; Knowledge Representation and Management; Data Mining; Big Data; Data Science; Intelligent Systems; Data Streams; Data Privacy; Digital Health; Human-Computer Interaction; Data Visualization; IoT Data Analytics; Distributed and Parallel Computing; Social Network Analysis
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Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) has rapidly gained popularity in recent years and has become an integral part of our daily lives. IoT has revolutionized how we interact with technology, from smartphones connected to fitness bands and smartwatches to software applications (apps) that control various household appliances such as thermostats, lights, and security systems. As the IoT continues to grow, data security has become a significant concern. However, deploying effective data security solutions for IoT environments is a complex task due to the anonymization and privatization aspects to be managed.

This Special Issue will publish high-quality and original research articles addressing data security in IoT networks. In particular, research topics will include the definition of data anonymization strategies for anonymizing sensitive data in IoT environments, privatization strategies to privatize data gathered from IoT devices, privacy-preserving strategies for social network data accessed through IoT devices, strategies and methodologies to help users be aware of privacy threats linked to the mismanagement of sensitive data over IoT devices, and prevention/protection policies in IoT devices.

Dr. Domenico Desiato
Dr. Stefano Cirillo
Guest Editors

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Keywords

  • IoT data security
  • IoT anonymization
  • IoT privatization
  • privacy-preserving social network data in IoT environments
  • IoT security prevention
  • IoT data analysis
  • IoT protection policies
  • privacy awareness in IoT devices

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Published Papers (1 paper)

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Research

13 pages, 433 KiB  
Article
Developing a Hybrid Detection Approach to Mitigating Black Hole and Gray Hole Attacks in Mobile Ad Hoc Networks
by Mohammad Yazdanypoor, Stefano Cirillo and Giandomenico Solimando
Appl. Sci. 2024, 14(17), 7982; https://doi.org/10.3390/app14177982 - 6 Sep 2024
Viewed by 491
Abstract
Mobile ad hoc networks (MANETs) have revolutionized wireless communications by enabling dynamic, infrastructure-free connectivity across various applications, from disaster recovery to military operations. However, these networks are highly vulnerable to security threats, particularly black hole and gray hole attacks, which can severely disrupt [...] Read more.
Mobile ad hoc networks (MANETs) have revolutionized wireless communications by enabling dynamic, infrastructure-free connectivity across various applications, from disaster recovery to military operations. However, these networks are highly vulnerable to security threats, particularly black hole and gray hole attacks, which can severely disrupt network performance and reliability. This study addresses the critical challenge of detecting and mitigating these attacks within the framework of the dynamic source routing (DSR) protocol. To tackle this issue, we propose a robust hybrid detection method that significantly enhances the identification and mitigation of black hole and gray hole attacks. Our approach integrates anomaly detection, advanced data mining techniques, and cryptographic verification to establish a multi-layered defense mechanism. Extensive simulations demonstrate that the proposed hybrid method achieves superior detection accuracy, reduces false positives, and maintains high packet delivery ratios even under attack conditions. Compared to existing solutions, this method provides more reliable and resilient network performance, dynamically adapting to evolving threats. This research represents a significant advancement in MANET security, offering a scalable and effective solution for safeguarding critical MANET applications against sophisticated cyber-attacks. Full article
(This article belongs to the Special Issue Data Security in IoT Networks)
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