Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Veigas, Christine Maria; Sangeetha, G.; Kulkarni, Manasa Title A name given to the resource A Multi-Layer Security Framework for Adversarial SQL Injection in Machine Learning Systems Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 17th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2025;pp.714-720 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/CICN67655.2025.11368126" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/CICN67655.2025.11368126</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105034686990?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105034686990?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Veigas C.M., Christ University, Department of Computer Science, India; Sangeetha G., Christ University, Department of Computer Science, India; Kulkarni M., Christ University, Department of Computer Science, India Description An account of the resource Adversarial machine learning (AML) is a field that works with attacks from hackers that deliberately cause machine learning systems to work incorrectly or identify data wrongly. Modern day machine learning systems grow in a very fast manner. This often introduces new threats and vulnerabilities that are above the capacity of the traditional cyber security measures. These attacks can in turn affect the trustworthiness and security of artificial systems across many domains like healthcare, education, finance, etc. This paper introduces a multi-layer security framework. It focuses on modelling and defending against SQL injection based attacks in machine learning. The paper emphasizes technical defences, governance and collaboration across various domains. By combining the risks with the existing cybersecurity frameworks such as NIST, MITRE ATLAS, and the EU AI Act, the framework provides a way to develop resilient, ethical and secure AI systems. 2025 IEEE. Subject The topic of the resource Adversarial Defenses; Adversarial Machine Learning; AI Governance; Injection Attacks; Mathematical Modelling; Threat Analysis Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833158733-8; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online