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Electronics
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Advances in Cyber-Security and Machine Learning
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Advances in Cyber-Security and Machine Learning

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: 15 October 2025 | Viewed by 2703

Special Issue Editor

Dr. Sana Ullah Jan

E-Mail Website
Guest Editor
School of Computing Engineering and the Built Environment, Edinburgh Napier University, Edinburgh EH10 5DT, UK
Interests: artificial intelligence; machine learning; cyber security; intrusion detection systems; information security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The fields of cyber-security and machine learning (ML) have seen significant advancements over the past few years. These advancements are driven by the increasing complexity of cyber threats and the growing availability of data, computational power, and sophisticated algorithms. The integration of ML into cyber-security has become crucial in enhancing the capability to predict, detect, and respond to cyber threats. Moreover, the rapid evolution of cyber threats necessitates advanced, adaptive solutions, and ML provides the tools to predict, detect, and respond to these threats effectively. This Special Issue is organized to present the key advances in the application of ML within the cyber-security domain.

The main themes of this Special Issue include, but are not limited to, the following:

  • Machine learning advances;
  • Advances in cyber-security;
  • Intrusion detection systems;
  • Cryptography;
  • Encryption and decryption;
  • Image encryption;
  • Adversarial intrusion detection;
  • Secure machine learning;
  • Attacks against machine learning;
  • Anomaly detection;
  • Zero-day exploits;
  • Advanced persistent threats;
  • Behavioral analysis for threat identification;
  • Signature and heuristic analysis;
  • NLP for attack detection;
  • Real-time fraud detection;
  • Predictive analytics;
  • Risk assessment;
  • Adversarial attacks on ML;
  • Explainable AI for transparency and trust enhancement;
  • Centralized and de-centralized security architectures;
  • Transfer learning and federated learning techniques.

Dr. Sana Ullah Jan
Guest Editor

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. Electronics is an international peer-reviewed open access semimonthly 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

  • machine learning
  • cyber security
  • information security
  • intrusion detection

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Published Papers (3 papers)

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Research

21 pages, 1159 KiB  
Article
StatePre: A Large Language Model-Based State-Handling Method for Network Protocol Fuzzing
by Yifan Zhang, Kailong Zhu, Jie Peng, Yuliang Lu, Qian Chen and Zixiong Li
Electronics 2025, 14(10), 1931; https://doi.org/10.3390/electronics14101931 - 9 May 2025
Viewed by 279
Abstract
As essential components for communication, network protocol programs are highly security-critical, making it crucial to identify their vulnerabilities. Fuzzing is one of the most popular software vulnerability discovery techniques, being highly efficient and having low false-positive rates. However, current network protocol fuzzing is [...] Read more.
As essential components for communication, network protocol programs are highly security-critical, making it crucial to identify their vulnerabilities. Fuzzing is one of the most popular software vulnerability discovery techniques, being highly efficient and having low false-positive rates. However, current network protocol fuzzing is hindered by the coarse-grained and missing state annotations in programs. The current solutions primarily rely on the manual modification of programs, which is inefficient and prone to omissions. In this paper, we propose StatePre, a novel state-handling method for stateful network protocol programs, which leverages large language model (LLM) code- and text-understanding capabilities to analyze request for comments (RFC)-defined state knowledge and optimize the state handling of programs for fuzzing. StatePre automatically refines coarse-grained state annotations and complements missing state annotations in programs to ensure precise state tracking and fuzzing effectiveness. We implement a prototype of StatePre. The evaluation shows that programs modified with StatePre, with fine-grained and comprehensive state annotations, achieve better fuzzing efficiency, higher code coverage, and improved crash detection compared to those not modified with StatePre. Moreover, StatePre demonstrates good scalability, thus is applicable to various network protocol programs. Full article
(This article belongs to the Special Issue Advances in Cyber-Security and Machine Learning)
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36 pages, 21603 KiB  
Article
Forensic Joint Photographic Experts Group (JPEG) Watermarking for Disk Image Leak Attribution: An Adaptive Discrete Cosine Transform–Discrete Wavelet Transform (DCT-DWT) Approach
by Belinda I. Onyeashie, Petra Leimich, Sean McKeown and Gordon Russell
Electronics 2025, 14(9), 1800; https://doi.org/10.3390/electronics14091800 - 28 Apr 2025
Viewed by 339
Abstract
This paper presents a novel forensic watermarking method for digital evidence distribution in non-cloud environments. The approach addresses the critical need for the secure sharing of Joint Photographic Experts Group (JPEG) images in forensic investigations. The method utilises an adaptive Discrete Cosine Transform–Discrete [...] Read more.
This paper presents a novel forensic watermarking method for digital evidence distribution in non-cloud environments. The approach addresses the critical need for the secure sharing of Joint Photographic Experts Group (JPEG) images in forensic investigations. The method utilises an adaptive Discrete Cosine Transform–Discrete Wavelet Transform (DCT-DWT) domain technique to embed a 64-bit watermark in both stand-alone JPEGs and those within forensic disk images. This occurs without alterations to disk structure or complications to the chain of custody. The system implements uniform secure randomisation and recipient-specific watermarks to balance security with forensic workflow efficiency. This work presents the first implementation of forensic watermarking at the disk image level that preserves structural integrity and enables precise leak source attribution. It addresses a critical gap in secure evidence distribution methodologies. The evaluation occurred on extensive datasets: 1124 JPEGs in a forensic disk image, 10,000 each of BOSSBase 256 × 256 and 512 × 512 greyscale images, and 10,000 COCO2017 coloured images. The results demonstrate high imperceptibility with average Peak Signal-to-Noise Ratio (PSNR) values ranging from 46.13 dB to 49.37 dB across datasets. The method exhibits robust performance against geometric attacks with perfect watermark recovery (Bit Error Rate (BER) = 0) for rotations up to 90° and scaling factors between 0.6 and 1.5. The approach maintains compatibility with forensic tools like Forensic Toolkit FTK and Autopsy. It performs effectively under attacks including JPEG compression (QF ≥ 60), filtering, and noise addition. The technique achieves high feature match ratios between 0.684 and 0.690 for a threshold of 0.70, with efficient processing times (embedding: 0.0347 s to 0.1187 s; extraction: 0.0077 s to 0.0366 s). This watermarking technique improves forensic investigation processes, particularly those that involve sensitive JPEG files. It supports leak source attribution, preserves evidence integrity, and provides traceability throughout forensic procedures. Full article
(This article belongs to the Special Issue Advances in Cyber-Security and Machine Learning)
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20 pages, 4577 KiB  
Article
FedLSTM: A Federated Learning Framework for Sensor Fault Detection in Wireless Sensor Networks
by Rehan Khan, Umer Saeed and Insoo Koo
Electronics 2024, 13(24), 4907; https://doi.org/10.3390/electronics13244907 - 12 Dec 2024
Cited by 3 | Viewed by 1299
Abstract
The rapid growth of Internet of Things (IoT) devices has significantly increased reliance on sensor-generated data, which are essential to a wide range of systems and services. Wireless sensor networks (WSNs), crucial to this ecosystem, are often deployed in diverse and challenging environments, [...] Read more.
The rapid growth of Internet of Things (IoT) devices has significantly increased reliance on sensor-generated data, which are essential to a wide range of systems and services. Wireless sensor networks (WSNs), crucial to this ecosystem, are often deployed in diverse and challenging environments, making them susceptible to faults such as software bugs, communication breakdowns, and hardware malfunctions. These issues can compromise data accuracy, stability, and reliability, ultimately jeopardizing system security. While advanced sensor fault detection methods in WSNs leverage a machine learning approach to achieve high accuracy, they typically rely on centralized learning, and face scalability and privacy challenges, especially when transferring large volumes of data. In our experimental setup, we employ a decentralized approach using federated learning with long short-term memory (FedLSTM) for sensor fault detection in WSNs, thereby preserving client privacy. This study utilizes temperature data enhanced with synthetic sensor data to simulate various common sensor faults: bias, drift, spike, erratic, stuck, and data-loss. We evaluate the performance of FedLSTM against the centralized approach based on accuracy, precision, sensitivity, and F1-score. Additionally, we analyze the impacts of varying the client participation rates and the number of local training epochs. In federated learning environments, comparative analysis with established models like the one-dimensional convolutional neural network and multilayer perceptron demonstrate the promising results of FedLSTM in maintaining client privacy while reducing communication overheads and the server load. Full article
(This article belongs to the Special Issue Advances in Cyber-Security and Machine Learning)
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