An Ontological Knowledge Base of Poisoning Attacks on Deep Neural Networks

Altoub, Majed; Alqurashi, Fahad; Yiğitcanlar, Tan; Corchado, Juan M.; Mehmood, Rashid

doi:10.20944/preprints202208.0197.v1

Cited by 3 publications

(2 citation statements)

References 80 publications

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“…The third query type is a query that retrieves all the nodes in the graph database to test the system load. We have provided complete details of the queries and their results on our project GitHub page [100].…”

Section: Knowledge Base Constructionmentioning

confidence: 99%

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An Ontological Knowledge Base of Poisoning Attacks on Deep Neural Networks

et al. 2022

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Deep neural networks (DNNs) have successfully delivered cutting-edge performance in several fields. With the broader deployment of DNN models on critical applications, the security of DNNs has become an active and yet nascent area. Attacks against DNNs can have catastrophic results, according to recent studies. Poisoning attacks, including backdoor attacks and Trojan attacks, are one of the growing threats against DNNs. Having a wide-angle view of these evolving threats is essential to better understand the security issues. In this regard, creating a semantic model and a knowledge graph for poisoning attacks can reveal the relationships between attacks across intricate data to enhance the security knowledge landscape. In this paper, we propose a DNN poisoning attack ontology (DNNPAO) that would enhance knowledge sharing and enable further advancements in the field. To do so, we have performed a systematic review of the relevant literature to identify the current state. We collected 28,469 papers from the IEEE, ScienceDirect, Web of Science, and Scopus databases, and from these papers, 712 research papers were screened in a rigorous process, and 55 poisoning attacks in DNNs were identified and classified. We extracted a taxonomy of the poisoning attacks as a scheme to develop DNNPAO. Subsequently, we used DNNPAO as a framework by which to create a knowledge base. Our findings open new lines of research within the field of AI security.

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Section: Knowledge Base Constructionmentioning

confidence: 99%

“…We have provided access to the ontology on our GitHub page at [100] that a user can employ to generate interactive ontology graphs and investigate the viability of DNNPAO with additional queries.…”

Section: Utilizationmentioning

confidence: 99%

An Ontological Knowledge Base of Poisoning Attacks on Deep Neural Networks

et al. 2022

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Resilience and Resilient Systems of Artificial Intelligence: Taxonomy, Models and Methods

et al. 2023

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Artificial intelligence systems are increasingly being used in industrial applications, security and military contexts, disaster response complexes, policing and justice practices, finance, and healthcare systems. However, disruptions to these systems can have negative impacts on health, mortality, human rights, and asset values. The protection of such systems from various types of destructive influences is thus a relevant area of research. The vast majority of previously published works are aimed at reducing vulnerability to certain types of disturbances or implementing certain resilience properties. At the same time, the authors either do not consider the concept of resilience as such, or their understanding varies greatly. The aim of this study is to present a systematic approach to analyzing the resilience of artificial intelligence systems, along with an analysis of relevant scientific publications. Our methodology involves the formation of a set of resilience factors, organizing and defining taxonomic and ontological relationships for resilience factors of artificial intelligence systems, and analyzing relevant resilience solutions and challenges. This study analyzes the sources of threats and methods to ensure each resilience properties for artificial intelligence systems. As a result, the potential to create a resilient artificial intelligence system by configuring the architecture and learning scenarios is confirmed. The results can serve as a roadmap for establishing technical requirements for forthcoming artificial intelligence systems, as well as a framework for assessing the resilience of already developed artificial intelligence systems.

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Understanding Local Government Cybersecurity Policy: A Concept Map and Framework

Hossain,

Yigitcanlar,

Nguyen

et al. 2024

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Cybersecurity is a crucial concern for local governments as they serve as the primary interface between public and government services, managing sensitive data and critical infrastructure. While technical safeguards are integral to cybersecurity, the role of a well-structured policy is equally important as it provides structured guidance to translate technical requirements into actionable protocols. This study reviews local governments’ cybersecurity policies to provide a comprehensive assessment of how these policies align with the National Institute of Standards and Technology’s Cybersecurity Framework 2.0, which is a widely adopted and commonly used cybersecurity assessment framework. This review offers local governments a mirror to reflect on their cybersecurity stance, identifying potential vulnerabilities and areas needing urgent attention. This study further extends the development of a cybersecurity policy framework, which local governments can use as a strategic tool. It provides valuable information on crucial cybersecurity elements that local governments must incorporate into their policies to protect confidential data and critical infrastructure.

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An Ontological Knowledge Base of Poisoning Attacks on Deep Neural Networks

Cited by 3 publications

References 80 publications

An Ontological Knowledge Base of Poisoning Attacks on Deep Neural Networks

An Ontological Knowledge Base of Poisoning Attacks on Deep Neural Networks

Resilience and Resilient Systems of Artificial Intelligence: Taxonomy, Models and Methods

Understanding Local Government Cybersecurity Policy: A Concept Map and Framework

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