Adversarial Robustness in Multimodal AI-Enabled Cybersecurity Systems: Defenses, Vulnerabilities, and Modality Interactions
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V5I4P117Keywords:
Multimodal Artificial Intelligence, Cybersecurity Optimization, Statistical Validation, P-Value Analysis, Residual Diagnostics, Q–Q Plot, Model Robustness, Machine Learning, Adversarial ResilienceAbstract
The study being reported on is investing in the multimodal artificial intelligence (AI) models for their optimum use and validation to carry out the best possible cyber protection. The mixed-up data sets provided the necessary ground for the statistical analysis—model parameters' significance was evaluated through P-Value testing and various kinds of residual analysis changing from the standardized one to quantile–quantile (Q–Q) plots. All the important features showed their statistical significance under the p < 0.005 threshold. This implied the strength of the model. The Q–Q plot showed that the residuals were very much like a normal distribution; thus, the prediction made was reliable and the error deviation was minor. The extreme P-Value analysis pinpointed the heavily influential and lightly significant variables, thereby indicating the multimodal features that impact the system performance and defense against malevolent threats. Not only does this study highlight the role of artificial intelligence (AI) in advancing cybersecurity but also the necessity of ML and statistical diagnostics in data-driven optimization and model interpretability, making the entire process of result consistency, transparency, and reproducibility reliable. To sum up, the proposed framework points to the use of AI-based statistical optimization as an intelligent, efficient, scalably and, most importantly, secure solution for the future cyberspace (one where AI-based applications will be in use)
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