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Symmetry
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Symmetry and Asymmetry in Cyber Security, IoTs and Privacy
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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 4559

Special Issue Editors

Prof. Dr. Rodrigo Sanches Miani

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Guest Editor
School of Computer Science, Federal University of Uberlândia, Uberlândia 38408-100, Brazil
Interests: intrusion detection systems; security analytics and human aspects of cyber attacks
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Bruno Bogaz Zarpelão

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Guest Editor
Computer Science Department, State University of Londrina, Londrina, Brazil
Interests: cybersecurity; attack detection; applied machine learning
Special Issues, Collections and Topics in MDPI journals

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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Symmetry is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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 (2 papers)

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Research

25 pages, 3201 KiB  
Article
Semi-Supervised Learning with Entropy Filtering for Intrusion Detection in Asymmetrical IoT Systems
by Badraddin Alturki and Abdulaziz A. Alsulami
Symmetry 2025, 17(6), 973; https://doi.org/10.3390/sym17060973 - 19 Jun 2025
Viewed by 1173
Abstract
The growth of Internet of Things (IoT) systems has brought serious security concerns, especially in asymmetrical environments where device capabilities and communication flows vary widely. Many machine-learning-based intrusion detection systems struggle to address noise, uncertainty, and class imbalance. For that reason, intensive data [...] Read more.
The growth of Internet of Things (IoT) systems has brought serious security concerns, especially in asymmetrical environments where device capabilities and communication flows vary widely. Many machine-learning-based intrusion detection systems struggle to address noise, uncertainty, and class imbalance. For that reason, intensive data preprocessing procedures were required. These challenges are in real-world data. In this work, we introduce a semi-supervised learning approach that uses entropy-based uncertainty filtering to improve intrusion detection in IoT environments. By dynamically identifying uncertain predictions from tree-based classifiers, we retain only high-confidence results during training. Later, confident samples from the uncertain set are used to retrain the model through a self-training loop. We evaluate this method using three diverse and benchmark datasets named RT-IoT2022, CICIoT2023, and CICIoMT2024, which include up to 34 different attack types. The experimental results reveal that XGBoost and Random Forest outperformed other tree-based models while maintaining their robustness when predicting attacks in the IoT environment. In addition, our proposed model was compared with other models proposed by researchers in the field, and the findings confirmed that our model presented promising results. Full article
(This article belongs to the Special Issue Symmetry and Asymmetry in Cyber Security, IoTs and Privacy)
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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 4 | Viewed by 2304
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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