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Intelligent Defenses: The Role of AI in Strengthening Information Security
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Intelligent Defenses: The Role of AI in Strengthening Information Security

A special issue of AI (ISSN 2673-2688). This special issue belongs to the section "AI in Autonomous Systems".

Deadline for manuscript submissions: 26 June 2026 | Viewed by 3886

Special Issue Editor

Dr. Kashif Naseer Qureshi

E-Mail Website
Guest Editor
1. Department of Electronic & Computer Engineering, University of Limerick, V94 T9PX Limerick, Ireland
2. Centre for Robotics and Intelligent Systems (CRIS), Mobile & Marine Robotics Research Centre, University of Limerick, V94 T9PX Limerick, Ireland
3. Lero—the SFI Research Centre for Software, Tierney Building, University of Limerick, V94 NYD3 Limerick, Ireland
Interests: vehicular ad hoc networks; wireless sensor networks; computer network
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

The Special Issue, "Intelligent Defenses: The Role of AI in Strengthening Information Security", will explore how artificial intelligence (AI) is revolutionizing cybersecurity by providing intelligent, adaptive, and proactive defense mechanisms against evolving cyber threats. As the complexity and frequency of attacks increase, traditional security tools often struggle to keep pace. AI introduces advanced capabilities, such as machine learning, deep learning, and natural language processing, to detect anomalies, predict threats, and automate response strategies with greater accuracy and speed.

This Special Issue will emphasize the integration of AI in areas like intrusion detection systems (IDS), threat intelligence, behavioral analytics, and malware detection. It will also highlight the use of AI for real-time decision-making, reducing false-positives, and strengthening endpoint security. Furthermore, it will address challenges such as adversarial AI, data privacy concerns, and the ethical implications of automated defenses.

We invite contributions on innovative AI-based frameworks, novel algorithms, case studies, and practical implementations that enhance security posture across various sectors. By bridging research and practice, this Special Issue aims to shape the next generation of intelligent cybersecurity solutions and foster discussions on securing digital infrastructures in an AI-driven world. It will serve as a platform for academics and professionals to collaborate on advancing resilient and intelligent defense systems.

Dr. Kashif Naseer Qureshi
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 250 words) can be sent to the Editorial Office for assessment.

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 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 1800 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

  • artificial intelligence (AI)
  • cybersecurity
  • intelligent defense systems
  • machine learning
  • deep learning
  • intrusion detection systems (IDS)
  • threat intelligence
  • anomaly detection
  • malware detection
  • behavioral analytics 
  • automated incident response
  • adversarial AI
  • cyber threat prediction
  • security automation
  • data privacy
  • ethical AI
  • real-time threat detection
  • AI-driven cyber defense
  • security analytics
  • digital infrastructure protection

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Published Papers (2 papers)

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17 pages, 1099 KB  
Article
LLM Security and Safety: Insights from Homotopy-Inspired Prompt Obfuscation
by Luis Eduardo Lazo Vera, Hamed Jelodar and Roozbeh Razavi-Far
AI 2026, 7(3), 83; https://doi.org/10.3390/ai7030083 - 1 Mar 2026
Viewed by 1172
Abstract
In this study, we propose a homotopy-inspired prompt obfuscation framework to enhance understanding of security and safety vulnerabilities in Large Language Models (LLMs). By systematically applying carefully engineered prompts, we demonstrate how latent model behaviors can be influenced in unexpected ways. Our experiments [...] Read more.
In this study, we propose a homotopy-inspired prompt obfuscation framework to enhance understanding of security and safety vulnerabilities in Large Language Models (LLMs). By systematically applying carefully engineered prompts, we demonstrate how latent model behaviors can be influenced in unexpected ways. Our experiments encompassed 15,732 prompts, including 10,000 high-priority cases, across LLama, Deepseek, KIMI for code generation, and Claude to verify. The results reveal critical insights into current LLM safeguards, highlighting the need for more robust defense mechanisms, reliable detection strategies, and improved resilience. Importantly, this work provides a principled framework for analyzing and mitigating potential weaknesses, with the goal of advancing safe, responsible, and trustworthy AI technologies. Full article
(This article belongs to the Special Issue Intelligent Defenses: The Role of AI in Strengthening Information Security)
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22 pages, 3725 KB  
Article
An Enhanced Machine Learning Framework for Network Anomaly Detection
by Oumaima Chentoufi, Mouad Choukhairi and Khalid Chougdali
AI 2025, 6(11), 299; https://doi.org/10.3390/ai6110299 - 20 Nov 2025
Cited by 3 | Viewed by 1840
Abstract
Given the increasing volume and sophistication of cyber-attacks, there has always been a need for improved and adaptive real-time intrusion detection systems. Machine learning algorithms have presented a promising approach for enhancing their capabilities. This research has focused on investigating the impact of [...] Read more.
Given the increasing volume and sophistication of cyber-attacks, there has always been a need for improved and adaptive real-time intrusion detection systems. Machine learning algorithms have presented a promising approach for enhancing their capabilities. This research has focused on investigating the impact of different dimensionality reduction approaches on performance, and we have chosen to work with both Batch PCA and Incremental PCA alongside Logistic Regression, SVM, and Decision Tree classifiers. We started this work by applying machine learning algorithms directly on pre-processed data, then applied the same algorithms on the reduced data. Our results have yielded an accuracy of 98.61% and an F1-score of 98.64% with a prediction time of only 0.09 s using Incremental PCA with Decision Tree. We also have obtained an accuracy of 98.44% and an F1-score of 98.47% with a prediction time of 0.04 s from Batch PCA with SVM, and an accuracy of 98.47% and an F1-score of 98.51% with a prediction time of 0.05 s from Incremental PCA with Logistic Regression. The findings demonstrate that Incremental PCA offers near real-time IDS deployment in large networks. Full article
(This article belongs to the Special Issue Intelligent Defenses: The Role of AI in Strengthening Information Security)
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