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Special Issue "Deep Learning Security and Privacy Defensive Techniques"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: 31 December 2023 | Viewed by 1084

Special Issue Editor

Department of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, 86100 Campobasso, Italy
Interests: software security; software testing; software engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) is facilitated by heterogeneous technologies which contribute to providing innovative and intelligent services in a large number of application domains. The satisfaction of security and privacy requirements in this scenario, are becoming a main challenge for IoT systems and their developers.

Nevertheless, most efforts in IoT security and privacy requirements look at these requirements from a  high-level view. Hence, important aspects of security and privacy functionalities will be disregarded, causing wrong design decisions. Exploiting data from infrastructure, computers, and cyber physical systems, it can be possible to discover useful information from data in order to secure the systems from both administrators and end users.

Decision makers can make more informative and conscious decisions through this kind of emerging analysis, including what actions need to be performed, and improvement recommendations to policies, guidelines, procedures, tools, and other aspects of the security of processes. In this context, fuzzy logic can be properly used to help deal with issues associated with computer security and computer forensics.

Submissions are expected from, but not limited to, the following topics:

  • Securing private data on mobile and wearable devices
  • Security in cyber physical system
  • Security in Smart Grid and in Cloud computing environments
  • Formal methods for security
  • Artificial Intelligence for cybersecurity
  • Cybersecurity in healthcare
  • Fraud detection and forensics
  • Big Data security for complex data analysis (video, sensors, text, etc.)
  • Security issues in complex systems and environments

Prof. Dr. Francesco Mercaldo
Collection Editor

Manuscript Submission Information

Manuscripts should be submitted online at 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. Sensors 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 2600 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.


  • Internet of Things
  • Artificial Intelligence
  • Cybersecurity

Published Papers (1 paper)

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41 pages, 5655 KiB  
Deep Neural Decision Forest (DNDF): A Novel Approach for Enhancing Intrusion Detection Systems in Network Traffic Analysis
Sensors 2023, 23(20), 8362; - 10 Oct 2023
Viewed by 543
Intrusion detection systems, also known as IDSs, are widely regarded as one of the most essential components of an organization’s network security. This is because IDSs serve as the organization’s first line of defense against several cyberattacks and are accountable for accurately detecting [...] Read more.
Intrusion detection systems, also known as IDSs, are widely regarded as one of the most essential components of an organization’s network security. This is because IDSs serve as the organization’s first line of defense against several cyberattacks and are accountable for accurately detecting any possible network intrusions. Several implementations of IDSs accomplish the detection of potential threats throughout flow-based network traffic analysis. Traditional IDSs frequently struggle to provide accurate real-time intrusion detection while keeping up with the changing landscape of threat. Innovative methods used to improve IDSs’ performance in network traffic analysis are urgently needed to overcome these drawbacks. In this study, we introduced a model called a deep neural decision forest (DNDF), which allows the enhancement of classification trees with the power of deep networks to learn data representations. We essentially utilized the CICIDS 2017 dataset for network traffic analysis and extended our experiments to evaluate the DNDF model’s performance on two additional datasets: CICIDS 2018 and a custom network traffic dataset. Our findings showed that DNDF, a combination of deep neural networks and decision forests, outperformed reference approaches with a remarkable precision of 99.96% by using the CICIDS 2017 dataset while creating latent representations in deep layers. This success can be attributed to improved feature representation, model optimization, and resilience to noisy and unbalanced input data, emphasizing DNDF’s capabilities in intrusion detection and network security solutions. Full article
(This article belongs to the Special Issue Deep Learning Security and Privacy Defensive Techniques)
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