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Applied Sciences
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Signal, Multimedia, and Text Processing in Cybersecurity Context
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Signal, Multimedia, and Text Processing in Cybersecurity Context

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

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 9575

Special Issue Editors

Prof. Dr. Artur Janicki

E-Mail Website
Guest Editor
Faculty of Electronics and Information Technology, Warsaw University of Technology, Nowowiejska, 00-665 Warsaw, Poland
Interests: speech processing: speech synthesis; speech and speaker recognition; emotion recognition; speech transmission in VoIP systems; steganography; machine learning; anomaly detection; data hiding; assistive technologies
Dr. Katarzyna Wasielewska

E-Mail Website
Guest Editor
Institute of Applied Informatics, University of Applied Sciences in Elblag, Wojska Polskiego, 82-300 Elblag, Poland
Interests: cybersecurity; network security; network traffic analysis; dataset quality assessment; multivariate data analysis; machine learning

Special Issue Information

Dear Colleagues,

Cybersecurity is an interdisciplinary field that requires competencies from a variety of fields. Several hot topics in cybersecurity, such as fake detection, privacy protection, security of biometric authentication methods, fraud detection, hidden transmission, and log analysis, involve solving advanced problems from the fields of text, audio, images, and video processing. Some of these probems require advanced methods of natural language processing (NLP), either the newest methods, based on deep neural models, or earlier methods, but used in a new context. The other problems necessitate the use of signal and image processing methods, which can also employ deep learning methods or more traditional approaches. In this Special Issue, we would like to gather examples of the use of signal, multimedia, and text processing methods in the cybersecurity context to allow prospective authors to share their ideas for improving cyberspace security and to inspire the research community to propose new solutions.

Prof. Dr. Artur Janicki
Dr. Katarzyna Wasielewska
Guest Editors

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Keywords

  • adversarial attacks
  • anomaly detection
  • anti-malware security
  • artificial intelligence
  • audio analysis
  • cyber threats
  • cybersecurity
  • dataset quality
  • deep fake
  • deep learning
  • digital forensics
  • disinformation
  • fake detection
  • feature selection
  • fraud detection
  • hate speech
  • image steganography
  • information hiding
  • log analysis
  • machine learning
  • multimedia analysis
  • natural language processing
  • neural models
  • pattern recognition
  • presentation attacks
  • privacy protection
  • social media
  • social networks
  • spectral analysis
  • speech processing
  • traffic analysis
  • video analysis
  • voice biometry

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Published Papers (3 papers)

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Research

12 pages, 1608 KiB  
Article
Source Microphone Identification Using Swin Transformer
by Mustafa Qamhan, Yousef A. Alotaibi and Sid-Ahmed Selouani
Appl. Sci. 2023, 13(12), 7112; https://doi.org/10.3390/app13127112 - 14 Jun 2023
Cited by 1 | Viewed by 1649
Abstract
Microphone identification is a crucial challenge in the field of digital audio forensics. The ability to accurately identify the type of microphone used to record a piece of audio can provide important information for forensic analysis and crime investigations. In recent years, transformer-based [...] Read more.
Microphone identification is a crucial challenge in the field of digital audio forensics. The ability to accurately identify the type of microphone used to record a piece of audio can provide important information for forensic analysis and crime investigations. In recent years, transformer-based deep-learning models have been shown to be effective in many different tasks. This paper proposes a system based on a transformer for microphone identification based on recorded audio. Two types of experiments were conducted: one to identify the model of the microphones and another in which identical microphones were identified within the same model. Furthermore, extensive experiments were performed to study the effects of different input types and sub-band frequencies on system accuracy. The proposed system is evaluated on the Audio Forensic Dataset for Digital Multimedia Forensics (AF-DB). The experimental results demonstrate that our model achieves state-of-the-art accuracy for inter-model and intra-model microphone classification with 5-fold cross-validation. Full article
(This article belongs to the Special Issue Signal, Multimedia, and Text Processing in Cybersecurity Context)
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16 pages, 1576 KiB  
Article
Detecting Fake Reviews in Google Maps—A Case Study
by Paweł Gryka and Artur Janicki
Appl. Sci. 2023, 13(10), 6331; https://doi.org/10.3390/app13106331 - 22 May 2023
Cited by 5 | Viewed by 3870
Abstract
Many customers rely on online reviews to make an informed decision about purchasing products and services. Unfortunately, fake reviews, which can mislead customers, are increasingly common. Therefore, there is a growing need for effective methods of detection. In this article, we present a [...] Read more.
Many customers rely on online reviews to make an informed decision about purchasing products and services. Unfortunately, fake reviews, which can mislead customers, are increasingly common. Therefore, there is a growing need for effective methods of detection. In this article, we present a case study showing research aimed at recognizing fake reviews in Google Maps places in Poland. First, we describe a method of construction and validation of a dataset, named GMR–PL (Google Maps Reviews—Polish), containing a selection of 18 thousand fake and genuine reviews in Polish. Next, we show how we used this dataset to train machine learning models to detect fake reviews and the accounts that published them. We also propose a novel metric for measuring the typicality of an account name and a metric for measuring the geographical dispersion of reviewed places. Initial recognition results were promising: we achieved an F1 score of 0.92 and 0.74 when detecting fake accounts and reviews, respectively. We believe that our experience will help in creating real-life review datasets for other languages and, in turn, will help in research aimed at the detection of fake reviews on the Internet. Full article
(This article belongs to the Special Issue Signal, Multimedia, and Text Processing in Cybersecurity Context)
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15 pages, 590 KiB  
Article
MDFULog: Multi-Feature Deep Fusion of Unstable Log Anomaly Detection Model
by Min Li, Mengjie Sun, Gang Li, Delong Han and Mingle Zhou
Appl. Sci. 2023, 13(4), 2237; https://doi.org/10.3390/app13042237 - 9 Feb 2023
Cited by 2 | Viewed by 3059
Abstract
Effective log anomaly detection can help operators locate and solve problems quickly, ensure the rapid recovery of the system, and reduce economic losses. However, recent log anomaly detection studies have shown some drawbacks, such as concept drift, noise problems, and fuzzy feature relation [...] Read more.
Effective log anomaly detection can help operators locate and solve problems quickly, ensure the rapid recovery of the system, and reduce economic losses. However, recent log anomaly detection studies have shown some drawbacks, such as concept drift, noise problems, and fuzzy feature relation extraction, which cause data instability and abnormal misjudgment, leading to significant performance degradation. This paper proposes a multi-feature deep fusion of an unstable log anomaly detection model (MDFULog) for the above problems. The MDFULog model uses a novel log resolution method to eliminate the dynamic interference caused by noise. This paper proposes a feature enhancement mechanism that fully uses the correlation between semantic information, time information, and sequence features to detect various types of log exceptions. The introduced semantic feature extraction model based on Bert preserves the semantics of log messages and maps them to log vectors, effectively eliminating worker randomness and noise injection caused by log template updates. An Informer anomaly detection classification model is proposed to extract practical information from a global perspective and predict outliers quickly and accurately. Experiments were conducted on HDFS, OpenStack, and unstable datasets, showing that the anomaly detection method in this paper performs significantly better than available algorithms. Full article
(This article belongs to the Special Issue Signal, Multimedia, and Text Processing in Cybersecurity Context)
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