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Information
Special Issues
Online Registration and Anomaly Detection of Cyber Security Events
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Online Registration and Anomaly Detection of Cyber Security Events

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Security and Privacy".

Deadline for manuscript submissions: closed (1 November 2024) | Viewed by 3887

Special Issue Editors

Dr. Michael Kiperberg

E-Mail Website
Guest Editor
Department of Software Engineering, Shamoon College of Engineering, Beer-Sheve 8410802, Israel
Interests: security; virtualization; operating systems
Dr. Natalia Vanetik

E-Mail Website
Guest Editor
Software Engineering Department, Sami Shamoon College of Engineering, Beer-Sheve 8410802, Israel
Interests: text analysis; NLP; deep learning, optimization; applications of deep learning in cyber security; integrating security and NLP
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Machine Learning in general and its applications in cyber security have attracted attention from the research community in the last few decades. While classic cyber security techniques can be used to acquire a stream of events, its analysis is usually performed using a machine learning technique. In the past, the analysis was divided into two separate phases. In the first phase, some portion of the stream was used to train a model, which was used for the analysis of the rest of the stream in the second phase. Recently, we have witnessed increased interest in developing online anomaly detection techniques, in which the model is constantly updated with new events.

In this Special Issue, we aim to gather as many perspectives as possible on the problem of online anomaly detection in different contexts. We welcome articles that contribute grand visions, research outcomes, theory development, implementation experiences, and prototype experiments and results. In addition to traditional machine learning applications in cybersecurity, the Special Issue also encourages contributions that explore the integration of Natural Language Processing (NLP) tasks. NLP techniques can play a crucial role in enhancing the analysis of cyber threats by extracting meaningful insights from textual data, such as security logs, incident reports, and communication records.

Key areas of this Special Issue include but are not limited to:

  • Machine learning;
  • Online learning;
  • Information security;
  • Text analysis for security;
  • Network security;
  • Trust management;
  • Security and privacy.

Dr. Michael Kiperberg
Dr. Natalia Vanetik
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. Information 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 1600 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
  • security
  • privacy
  • information leakage
  • NLP

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Published Papers (2 papers)

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Research

25 pages, 7982 KiB  
Article
Aerial Imagery Redefined: Next-Generation Approach to Object Classification
by Eran Dahan, Itzhak Aviv and Tzvi Diskin
Information 2025, 16(2), 134; https://doi.org/10.3390/info16020134 - 11 Feb 2025
Viewed by 748
Abstract
Identifying and classifying objects in aerial images are two significant and complex issues in computer vision. The fine-grained classification of objects in overhead images has become widespread in various real-world applications, due to recent advancements in high-resolution satellite and airborne imaging systems. The [...] Read more.
Identifying and classifying objects in aerial images are two significant and complex issues in computer vision. The fine-grained classification of objects in overhead images has become widespread in various real-world applications, due to recent advancements in high-resolution satellite and airborne imaging systems. The task is challenging, particularly in low-resource cases, due to the minor differences between classes and the significant differences within each class caused by the fine-grained nature. We introduce Classification of Objects for Fine-Grained Analysis (COFGA), a recently developed dataset for accurately categorizing objects in high-resolution aerial images. The COFGA dataset comprises 2104 images and 14,256 annotated objects across 37 distinct labels. This dataset offers superior spatial information compared to other publicly available datasets. The MAFAT Challenge is a task that utilizes COFGA to improve fine-grained classification methods. The baseline model achieved a mAP of 0.6. This cost was 60, whereas the most superior model achieved a score of 0.6271 by utilizing state-of-the-art ensemble techniques and specific preprocessing techniques. We offer solutions to address the difficulties in analyzing aerial images, particularly when annotated and imbalanced class data are scarce. The findings provide valuable insights into the detailed categorization of objects and have practical applications in urban planning, environmental assessment, and agricultural management. We discuss the constraints and potential future endeavors, specifically emphasizing the potential to integrate supplementary modalities and contextual information into aerial imagery analysis. Full article
(This article belongs to the Special Issue Online Registration and Anomaly Detection of Cyber Security Events)
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23 pages, 3496 KiB  
Article
Android Malware Detection Using Support Vector Regression for Dynamic Feature Analysis
by Nahier Aldhafferi
Information 2024, 15(10), 658; https://doi.org/10.3390/info15100658 - 19 Oct 2024
Cited by 3 | Viewed by 2578
Abstract
Mobile devices face significant security challenges due to the increasing proliferation of Android malware. This study introduces an innovative approach to Android malware detection, combining Support Vector Regression (SVR) and dynamic feature analysis to address escalating mobile security challenges. Our research aimed to [...] Read more.
Mobile devices face significant security challenges due to the increasing proliferation of Android malware. This study introduces an innovative approach to Android malware detection, combining Support Vector Regression (SVR) and dynamic feature analysis to address escalating mobile security challenges. Our research aimed to develop a more accurate and reliable malware detection system capable of identifying both known and novel malware variants. We implemented a comprehensive methodology encompassing dynamic feature extraction from Android applications, feature preprocessing and normalization, and the application of SVR with a Radial Basis Function (RBF) kernel for malware classification. Our results demonstrate the SVR-based model’s superior performance, achieving 95.74% accuracy, 94.76% precision, 98.06% recall, and a 96.38% F1-score, outperforming benchmark algorithms including SVM, Random Forest, and CNN. The model exhibited excellent discriminative ability with an Area Under the Curve (AUC) of 0.98 in ROC analysis. The proposed model’s capacity to capture complex, non-linear relationships in the feature space significantly enhanced its effectiveness in distinguishing between benign and malicious applications. This research provides a robust foundation for advancing Android malware detection systems, offering valuable insights for researchers and security practitioners in addressing evolving malware challenges. Full article
(This article belongs to the Special Issue Online Registration and Anomaly Detection of Cyber Security Events)
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