Artificial Intelligence for Cybersecurity: A Data-Driven Approach

A special issue of AI (ISSN 2673-2688).

Deadline for manuscript submissions: closed (15 August 2021) | Viewed by 6876

Special Issue Editor

Department of Computer Science, University of Salerno, 84084 Fisciano, SA, Italy
Interests: soft computing algorithms; data mining and machine learning; deep learning; knowledge discovery; optimization problems; pervasive Computing; trustworthiness modeling; high performance machines; parallel computing; big data analytics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As cyber-attacks grow in volume and complexity, artificial intelligence (AI) is helping under-resourced security operations analysts to stay ahead of threats. AI technologies like machine learning and natural language processing enable analysts to respond to threats with greater confidence and speed. AI is trained by consuming billions of data artifacts from both structured and unstructured sources. Through machine learning and deep learning techniques, AI improves its knowledge to “understand” cybersecurity threats and cyber risk. AI gathers insights and uses reasoning to identify the relationships between threats, such as malicious files, suspicious IP addresses or insiders. This analysis takes seconds or minutes, allowing security analysts to respond to threats up to 60 times faster. AI eliminates time-consuming research tasks and provides curated analysis of risks, reducing the amount of time security analysts take to make the critical decisions and launch an orchestrated response to remediate the threat.

Therefore, the investigation on AI-based security is attracting more and more attention from both industry and academia.

The central theme of this Special Issue is to investigate novel methodologies and theories for cybersecurity and privacy. In particular, this Special Issue focuses on addressing the usage of data mining, machine learning, and novel intelligent techniques for analyzing the data produced by any system and sensor network, and to emphasize the role of the AI in the security world.

Topics of Interest:

This Special Issue aims to present the most important and relevant advances to overcome the new challenges related to the application of AI for cybersecurity, through data mining, machine learning, deep learning, and cognitive computing.

We seek original and high-quality submissions related to, but not limited to, one or more of the following topics:

  • Cybersecurity AI solutions;
  • Intrusion and detection AI-based techniques;
  • Machine learning-based data analytics;
  • Real-time data processing;
  • Nature-inspired evolutionary algorithms and systems for pattern recognition, data analysis, and modeling;
  • Pattern recognition and classification for multivariate time series;
  • Learning from data streams;
  • Learning from networked data;
  • Deep Learning-based solutions;
  • Algorithmic developments and applications of machine learning and data mining for Big Data;
  • Distributed data mining and machine learning systems;
  • Distributed computing and computing framework.

Dr. Gianni D'Angelo
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. AI is an international peer-reviewed open access quarterly 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.

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

25 pages, 1712 KiB  
Article
Detection of Anomalies in Large-Scale Cyberattacks Using Fuzzy Neural Networks
by Paulo Vitor de Campos Souza, Augusto Junio Guimarães, Thiago Silva Rezende, Vinicius Jonathan Silva Araujo and Vanessa Souza Araujo
AI 2020, 1(1), 92-116; https://0-doi-org.brum.beds.ac.uk/10.3390/ai1010005 - 07 Feb 2020
Cited by 12 | Viewed by 4309
Abstract
The fuzzy neural networks are hybrid structures that can act in several contexts of the pattern classification, including the detection of failures and anomalous behaviors. This paper discusses the use of an artificial intelligence model based on the association between fuzzy logic and [...] Read more.
The fuzzy neural networks are hybrid structures that can act in several contexts of the pattern classification, including the detection of failures and anomalous behaviors. This paper discusses the use of an artificial intelligence model based on the association between fuzzy logic and training of artificial neural networks to recognize anomalies in transactions involved in the context of computer networks and cyberattacks. In addition to verifying the accuracy of the model, fuzzy rules were obtained through knowledge from the massive datasets to form expert systems. The acquired rules allow the creation of intelligent systems in high-level languages with a robust level of identification of anomalies in Internet transactions, and the accuracy of the results of the test confirms that the fuzzy neural networks can act in anomaly detection in high-security attacks in computer networks. Full article
(This article belongs to the Special Issue Artificial Intelligence for Cybersecurity: A Data-Driven Approach)
Show Figures

Figure 1

Back to TopTop