A Survey on Explainable Artificial Intelligence for Cybersecurity

Rjoub, Gaith; Wahab, Omar Abdel; Mizouni, Rabeb; Song, Alyssa; Cohen, Robin; Otrok, Hadi; Mourad, Azzam

doi:10.1109/tnsm.2023.3282740

Cited by 12 publications

(3 citation statements)

References 94 publications

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“…Multi-tasking AI has led to the development of numerous solutions using multiple algorithms [54,69,70]. This has translated into the use of XAI, which can contribute to greater clarity and understanding of machine learning models in these numerous issues [30,33,34,71,72]. One can still see the need for further research on the explainability of parameters in fruit powders to achieve, among others, an understanding of the factors affecting their performance.…”

Section: Interpretability Of Machine Learningmentioning

confidence: 99%

Explainable AI: Machine Learning Interpretation in Blackcurrant Powders

Przybył

2024

Sensors

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Recently, explainability in machine and deep learning has become an important area in the field of research as well as interest, both due to the increasing use of artificial intelligence (AI) methods and understanding of the decisions made by models. The explainability of artificial intelligence (XAI) is due to the increasing consciousness in, among other things, data mining, error elimination, and learning performance by various AI algorithms. Moreover, XAI will allow the decisions made by models in problems to be more transparent as well as effective. In this study, models from the ‘glass box’ group of Decision Tree, among others, and the ‘black box’ group of Random Forest, among others, were proposed to understand the identification of selected types of currant powders. The learning process of these models was carried out to determine accuracy indicators such as accuracy, precision, recall, and F1-score. It was visualized using Local Interpretable Model Agnostic Explanations (LIMEs) to predict the effectiveness of identifying specific types of blackcurrant powders based on texture descriptors such as entropy, contrast, correlation, dissimilarity, and homogeneity. Bagging (Bagging_100), Decision Tree (DT0), and Random Forest (RF7_gini) proved to be the most effective models in the framework of currant powder interpretability. The measures of classifier performance in terms of accuracy, precision, recall, and F1-score for Bagging_100, respectively, reached values of approximately 0.979. In comparison, DT0 reached values of 0.968, 0.972, 0.968, and 0.969, and RF7_gini reached values of 0.963, 0.964, 0.963, and 0.963. These models achieved classifier performance measures of greater than 96%. In the future, XAI using agnostic models can be an additional important tool to help analyze data, including food products, even online.

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Section: Interpretability Of Machine Learningmentioning

confidence: 99%

Explainable AI: Machine Learning Interpretation in Blackcurrant Powders

Przybył

2024

Sensors

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“…XAI has also been extensively studied and applied in the field of cybersecurity. Rjoub et al [23] highlight the transformative potential of XAI in network cybersecurity, emphasizing the importance of clear and interpretable explanations for AI models' decisions and actions. Their survey reviews the current state of XAI in cybersecurity, addressing challenges and suggesting future research directions.…”

Section: Xai In Cybersecuritymentioning

confidence: 99%

Interpretability and Transparency of Machine Learning in File Fragment Analysis with Explainable Artificial Intelligence

Jinad,

Islam,

Shashidhar

2024

Electronics

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Machine learning models are increasingly being used across diverse fields, including file fragment classification. As these models become more prevalent, it is crucial to understand and interpret their decision-making processes to ensure accountability, transparency, and trust. This research investigates the interpretability of four machine learning models used for file fragment classification through the lens of Explainable Artificial Intelligence (XAI) techniques. Specifically, we employ two prominent XAI methods, Shapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME), to shed light on the black-box nature of four machine learning models used for file fragment classification. By conducting a detailed analysis of the SHAP and LIME explanations, we demonstrate the effectiveness of these techniques in improving the interpretability of the models’ decision-making processes. Our analysis reveals that these XAI techniques effectively identify key features influencing each model’s predictions. The results also showed features that were critical to predicting specific classes. The ability to interpret and validate the decisions made by machine learning models in file fragment classification can enhance trust in these models and inform improvements for better accuracy and reliability. Our research highlights the importance of XAI techniques in promoting transparency and accountability in the application of machine learning models across diverse domains.

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“…Also, like the application of AGI, the concept of Explainable Artificial Intelligence (EAI) seems to possess the potential to revolutionise cybersecurity in IOMV systems by better understanding the behaviour of various types of cyberthreats and allowing the development of effective countermeasures. The EAI method can become the most suitable for IOMV systems, where peer-to-peer communication takes place between healthcare devices, and anomaly-based intrusion detection in the IOMV networks [114,115]. Also, brain-inspired neuromorphic computing techniques are promising biologically inspired methods by using brain cognition mechanism to address diversified technologies for IOV system to develop intelligent and fault-tolerant transport systems [116].…”

Section: Artificial Intelligence and Machine Learning For Strengtheni...mentioning

confidence: 99%

Cybersecurity in Internet of Medical Vehicles: State-of-the-Art Analysis, Research Challenges and Future Perspectives

Bhukya,

Thakur,

Mudhivarthi

et al. 2023

Sensors

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The “Internet-of-Medical-Vehicles (IOMV)” is one of the special applications of the Internet of Things resulting from combining connected healthcare and connected vehicles. As the IOMV communicates with a variety of networks along its travel path, it incurs various security risks due to sophisticated cyber-attacks. This can endanger the onboard patient’s life. So, it is critical to understand subjects related to “cybersecurity” in the IOMV to develop robust cybersecurity measures. In this paper, the goal is to evaluate recent trends and state-of-the-art publications, gaps, and future outlooks related to this research area. With this aim, a variety of publications between 2016 and 2023 from “Web-of-Science” and “Scopus” databases were analysed. Our analysis revealed that the IOMV is a niche and unexplored research area with few defined standards and frameworks, and there is a great need to implement robust cybersecurity measures. This paper will help researchers to gain a comprehensive idea of this niche research topic, as it presents an analysis of top journals and highly cited papers, their challenges and limitations, the system model and architecture of the IOMV, related applicable standards, potential cyber-attacks, factors causing cybersecurity risks, various artificial intelligence techniques for developing potential countermeasures, the assessment and parameterisation of cybersecurity risks, constraints and challenges, and future outlooks for implementing cybersecurity measures in the IOMV.

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A Survey on Explainable Artificial Intelligence for Cybersecurity

Cited by 12 publications

References 94 publications

Explainable AI: Machine Learning Interpretation in Blackcurrant Powders

Explainable AI: Machine Learning Interpretation in Blackcurrant Powders

Interpretability and Transparency of Machine Learning in File Fragment Analysis with Explainable Artificial Intelligence

Cybersecurity in Internet of Medical Vehicles: State-of-the-Art Analysis, Research Challenges and Future Perspectives

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