Indeed, current research focuses on unauthorized video sites, academic plagiarism, and unlicensed software, marking a new frontier in proactive exploration for the detection of sites already engaged in infringement. Even so, preventive, active, or automated strategies are not yet standardized or uniformly structured, thus creating a significant gap for authors who seek to protect their digital multimedia works or to ascertain if these have been disseminated without their authorization. From this consideration, two research inquiries of crucial importance are raised:  
**First Research Inquiry**: In the context of the diverse and varied existing methodologies for safeguarding and discerning rights-infringing content within the realm of the IoE, how could a meticulously structured taxonomy aid authors in making informed and pertinent decisions within their specific contextual framework? **Second Research Inquiry**: Within the evolving sphere of IT and acknowledging the scarcity of preceding research in the domain of ML, is it feasible to develop an advanced methodology in ML, employing tracking techniques in the IoE and sophisticated search algorithms, capable of analyzing, processing, and categorizing sites containing potentially infringing content? The present study addresses the previously posed research questions, focusing on two fundamental axes: firstly, the design of a detailed taxonomy in the domains of SafeGuarding and Active, aimed at managing infringing sites in the IoE, and secondly, the proposal of an innovative methodology for identifying websites that host multimedia content with potential copyright infringement. This research delves into a field that, so far, has received limited attention in the scientific literature [28; 29; 30; 31; 32; 33; 34; 35]. The main contributions of this work are as follows:  
**Point 1**: This research introduces a novel taxonomy focused on the dual objectives of protecting multimedia works in the IoE and detecting potentially infringing content, developed through a thorough examination of a wide range of scholarly papers and white papers across various digital libraries. **Point 2**: This research introduces an advanced automated search methodology, merging web navigation analysis with multi-engine search capabilities, focusing on key content areas like movies and series, music, software, and books [16]. By harnessing HTML data from websites, the system detects potential copyright violations, ranging from redirects to JavaScript anomalies. It employs BERT (Bidirectional Encoder Representations from Transformers) [36], a state-of-the-art pre-trained encoder, for an innovative synthesis of textual and numerical data. Subsequently, the data are processed through a fine-tuned Dense Neural Network (DNN), marking a significant advancement in information retrieval in the IoE domain. Hereafter, this methodology will be referred to as BERT + DNN. The remainder of this document is structured as follows: Section 2 outlines the construction of a taxonomy of domains, subdomains, and categories that surround the subject of study--multimedia content and copyright. In Section 3, related work concerning the detection of copyright-infringing material using AI/ML is discussed, delving into their processes and performance. Section 4 introduces the methodological BERT + DNN framework, detailing the stages of data collection, preprocessing, transformation, fine-tuning, and classification of websites with infringing multimedia content over the IoE. Section 5 presents, compares, and discusses the results using quantitative performance metrics. Finally, Section 6 concludes the paper by listing its significant contributions and emphasizing areas that warrant further exploration in future research endeavors.