Symmetry and Asymmetry in Cyber Security, IoTs and Privacy

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 2015

Special Issue Editors


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School of Computer Science, Federal University of Uberlândia, Uberlândia 38408-100, Brazil
Interests: intrusion detection systems; security analytics and human aspects of cyber attacks
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Special Issue Information

Dear Colleagues,

In cybersecurity, there is often a symmetrical aspect to defense and attack strategies. Attackers may seek corresponding weaknesses or vulnerabilities to exploit for every security measure implemented to protect computer systems. This ongoing back-and-forth between defenders and attackers resembles the concept of symmetry in adversarial relationships. The vulnerability discovery process exemplifies this dynamic. While security researchers work to identify vulnerabilities and create patches, attackers race to exploit these weaknesses before fixes are released. IoT security also presents an inherent asymmetry, where resource-constrained devices face adversaries with more significant resources and time, creating an imbalanced power dynamic. Additionally, privacy issues, including cyber threats' legal and ethical implications, can be examined through symmetrical and asymmetrical lenses. Understanding these aspects of symmetry and asymmetry in cybersecurity is crucial for researchers and practitioners in the field.

Prof. Dr. Rodrigo Sanches Miani
Dr. Bruno Bogaz Zarpelão
Guest Editors

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Keywords

  • cryptosystems
  • cryptographic protocols
  • communication and privacy
  • Internet of Things
  • attack–defense asymmetry
  • machine learning and cybersecurity
  • legal and ethical aspects of cybersecurity
  • cybersecurity policies

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Published Papers (1 paper)

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Research

19 pages, 1331 KiB  
Article
Hybrid Neural Network-Based Intrusion Detection System: Leveraging LightGBM and MobileNetV2 for IoT Security
by Yi-Min Yang, Ko-Chin Chang and Jia-Ning Luo
Symmetry 2025, 17(3), 314; https://doi.org/10.3390/sym17030314 - 20 Feb 2025
Cited by 1 | Viewed by 1062
Abstract
The rapid expansion of the Internet of Things (IoT) has uncovered a significant asymmetry in cybersecurity, where low-power edge devices must face sophisticated threats from adversaries backed by ample resources. In our study, we employ a symmetry-based approach to rebalance these uneven scenarios. [...] Read more.
The rapid expansion of the Internet of Things (IoT) has uncovered a significant asymmetry in cybersecurity, where low-power edge devices must face sophisticated threats from adversaries backed by ample resources. In our study, we employ a symmetry-based approach to rebalance these uneven scenarios. We propose a Hybrid Neural Network Intrusion Detection System (Hybrid NNIDS) that uses LightGBM to filter anomalies at the traffic level and MobileNetV2 for further detection at the packet level, creating a viable compromise between detection accuracy and computational cost. Additionally, the proposed Hybrid NNIDS model, on the ACI-IoT-2023 dataset, outperformed other intrusion detection models with an accuracy of 94%, an F1-score of 91%, and a precision rate of 93% in attack detection. The results indicate the developed asymmetry algorithm can greatly reduce processing overhead while still being able to be implemented in IoT environments. The focus of future work will be on the real-world deployment of these security infrastructures in the IoT and their adaptation to newer types of attack vectors that may be developed by malware. Full article
(This article belongs to the Special Issue Symmetry and Asymmetry in Cyber Security, IoTs and Privacy)
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