Artificial Intelligence, Security and Safety in Information Systems and Networks

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

Deadline for manuscript submissions: 30 September 2024 | Viewed by 762

Special Issue Editors


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Guest Editor
Department of Computer Science, University of Bradford, Bradford BD7 1DP, UK
Interests: safety; reliability; Internet of Things; autonomous systems; cyber security
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Computer Science, University of Bradford, Bradford BD7 1DP, UK
Interests: cyber security; artificial intelligence; Internet of Things; machine learning; security in cyber physical systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We would like to invite you to contribute to this Special Issue, entitled “Artificial Intelligence, Security and Safety in Information Systems and Networks”.

Applications of information systems and networks are becoming increasingly common across the field of engineered systems, from cars and drones to manufacturing systems and medical devices, addressing prevailing societal changes and, increasingly, consumer demand. While information systems offer enormous economic, societal, and innovation potential, they have opened new avenues for safety, security, and privacy concerns that can adversely affect our lives and the environment. Due to the large number of connected devices and their ability to control critical physical assets, deliberate cyber attacks, and/or random failure events such as the mechanical failure of devices, communication failure, and unforeseen bad interactions between connected information systems, all these factors can cause these systems to enter unsafe and dangerous physical states. The volume of attacks on information systems and networks is constantly increasing, and attack space is evolving frequently. Therefore, there is a pressing need to develop innovative techniques and methods to address safety, security, and privacy concerns in the new generation of information systems, thus assuring that they do not pose an unacceptable level of risk.

This Special Issue aims to publish work on multidisciplinary research for novel approaches, visionary ideas, experiences in tools and technologies, and case studies to address the challenges of security and safety in information systems and networks. Both review articles and novel research papers are solicited.

Topics of interest include, but are not limited to, the following:

  • Safety assurance of information systems and networks;
  • Security, privacy, and communication issues in information systems and networks;
  • Cyber–physical threats and vulnerability analysis;
  • Artificial intelligence for the safety, security, and privacy of information systems;
  • Resilience and disaster recovery;
  • Threat detection and prevention;
  • Threat modelling for information systems;
  • Privacy-enhanced technology in information systems;
  • Model-based safety analysis, design, and assessment;
  • Threat intelligence and information sharing;
  • Blockchain for the safety and security of information systems;
  • Safety/security co-analysis and risk assessment;
  • Reliability analysis of information systems.

Dr. Sohag Kabir
Dr. Ibrahim Ghafir
Guest Editors

Manuscript Submission Information

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Keywords

  • security
  • privacy
  • safety
  • reliability
  • information systems
  • cyber security
  • Internet of Things
  • intrusion detection and prevention
  • artificial intelligence
  • machine learning
  • blockchain

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Published Papers (1 paper)

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Research

15 pages, 1202 KiB  
Article
Semantic Hierarchical Classification Applied to Anomaly Detection Using System Logs with a BERT Model
by Clara Corbelle, Victor Carneiro and Fidel Cacheda
Appl. Sci. 2024, 14(13), 5388; https://doi.org/10.3390/app14135388 - 21 Jun 2024
Viewed by 466
Abstract
The compaction and structuring of system logs facilitate and expedite anomaly and cyberattack detection processes using machine-learning techniques, while simultaneously reducing alert fatigue caused by false positives. In this work, we implemented an innovative algorithm that employs hierarchical codes based on the semantics [...] Read more.
The compaction and structuring of system logs facilitate and expedite anomaly and cyberattack detection processes using machine-learning techniques, while simultaneously reducing alert fatigue caused by false positives. In this work, we implemented an innovative algorithm that employs hierarchical codes based on the semantics of natural language, enabling the generation of a significantly reduced log that preserves the semantics of the original. This method uses codes that reflect the specificity of the topic and its position within a higher hierarchical structure. By applying this catalog to the analysis of logs from the Hadoop Distributed File System (HDFS), we achieved a concise summary with non-repetitive themes, significantly speeding up log analysis and resulting in a substantial reduction in log size while maintaining high semantic similarity. The resulting log has been validated for anomaly detection using the “bert-base-uncased” model and compared with six other methods: PCA, IM, LogCluster, SVM, DeepLog, and LogRobust. The reduced log achieved very similar values in precision, recall, and F1-score metrics, but drastically reduced processing time. Full article
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