The Design of an Ontology for ATT&amp;CK and its Application to Cybersecurity

Akbar, Khandakar Ashrafi; Halim, Sadaf Md; Singhal, Anoop; Abdeen, Basel; Khan, Latifur; Thuraisingham, Bhavani

doi:10.1145/3577923.3585051

Cited by 3 publications

(1 citation statement)

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“…Olivares-Alarcos et al [17] show how ontologies support artificial intelligence in their discussion of comparing different ontology-based methods for robot autonomy. Various taxonomies and ontologies for cybersecurity have been proposed or are used in expert systems [18]- [23]. Canham et al [24] focused on the causes of unintentional and intentional data breaches, providing an overview of human error research and comparing studies for root causes.…”

Section: Baugher and Qumentioning

confidence: 99%

Create the Taxonomy for Unintentional Insider Threat via Text Mining and Hierarchical Clustering Analysis

Baugher,

2024

EJECE

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The unintentional activities of system users can jeopardize the confidentiality, integrity, and assurance of data on information systems. These activities, known as unintentional insider threat activities, account for a significant percentage of data breaches. A method to mitigate or prevent this threat is using smart systems or artificial intelligence (AI). The construction of an AI requires the development of a taxonomy of activities. The literature review focused on data breach threats, mitigation tools, taxonomy usage in cybersecurity, and taxonomy development using Endnote and Google Scholar. This study aims to develop a taxonomy of unintentional insider threat activities based on narrative descriptions of the breach events in public data breach databases. The public databases were from the California Department of Justice, US Health and Human Services, and Verizon, resulting in 1850 examples of human errors. A taxonomy was constructed to specify the dimensions and characteristics of objects. Text mining and hierarchical cluster analysis were used to create the taxonomy, indicating a quantitative approach. Ward’s agglomeration coefficient was used to ensure the cluster was valid. The resulting top-level taxonomy categories are application errors, communication errors, inappropriate data permissions, lost media, and misconfigurations.

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Section: Baugher and Qumentioning

confidence: 99%

Create the Taxonomy for Unintentional Insider Threat via Text Mining and Hierarchical Clustering Analysis

Baugher,

2024

EJECE

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OntoCPS4PMS: Ontology modeling for collaborative cyber‐physical threat defense in power monitoring system

Teng,

Yang,

Wang

et al. 2024

Systems Engineering

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Given the deep interconnection between information and physical aspects of power systems, escalating cyber‐attacks pose significant threats to both grid information systems and electrical equipment. The context highlights the importance of risk perception and defense mechanisms in information and functional security for Power Monitoring System (PMS). To address this, we introduce an innovative ontology framework designed for comprehensive cyber‐physical security in PMS (OntoCPS4PMS). OntoCPS4PMS identifies risks from cross‐domain attacks and supports a collaborative defense strategy. It offers a multifaceted assessment method for PMS security events, considering vulnerabilities, cyber‐attack impacts, and physical domain risks. Through simulated PMS attack scenarios, we validate the framework's effectiveness in dynamic cyber‐physical security analysis. The results demonstrate OntoCPS4PMS's ability to trace attacks, locate affected equipment, and recommend mitigation measures, confirming its utility in enhancing PMS security and decision‐making.

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