Mobile Ad Hoc Networks (MANETs) are dynamic and self-configuring wireless networks, making them vulnerable to various security threats, including intrusion attempts. Intrusion detection systems (IDS) play a critical role in safeguarding MANETs against unauthorized access and malicious activities. In this study, we propose an innovative approach to enhance the security of MANETs through intrusion detection, leveraging the power of Support Vector Machines (SVM) with Ant Colony Optimization (ACO). Our approach harnesses the robustness of SVM in pattern recognition and classification, while ACO optimizes the SVM parameters, improving the accuracy and efficiency of intrusion detection. Through extensive experiments and evaluations, we demonstrate the effectiveness of this combined approach in mitigating intrusion threats in MANETs.

They focus on information-rich information technology like those emerging. Cloud Computing, Artificial Intelligence (AI), and machine learning embed them in the existing enterprise data strategies to placate issues like data imperativeness due to faster executions. As a usage of Multi-Domain Master Data Management (MDM), it resonates. In several domains, such as customer, product, supplier, and employee data, MDM is required to organize and rule out crucial company data and provide a full and correct representation of the company if it has to operate with consistent data. The higher complex and wide volume of business data calls for the multi-domain approach that changes from the traditional single-domain MDM systems. In a multi-domain, MDM is used to integrate, provide real-time analytics, and make better decision-making via the resolving of problems like silos, system fragmentation, and regulatory compliance. These applications are pivotal to numerous industrial sectors, including healthcare, biotech, electric vehicles (EV), and renewable energy, because of data security, patient care, and supply chain management. In terms of healthcare and EV, the prospects of robust frameworks also play a key role in quality, security, and compliance, says the document. Specific challenges of multi-domain MDM are integration complexity, legacy, system compatibility, and compliance risk that the use of phased deployment and scalable architecture can smooth. Even in a highly competitive data-driven world, businesses need to find their way through in a very speedy manner without being stuck at a particular place, and that is where the future of MDM needs to adopt technologies such as the ones mentioned in this article like Anti AI or blockchain to solve the issues of data integrity, scalability and the limitations of the enforced restrictions arising from MDM compliance.

Enterprise Software-as-a-Service (SaaS) platforms increasingly rely on complex algorithmic systems that dynamically adjust user experiences, resource allocations, and operational parameters. However, many algorithmic interventions occur without explicit documentation, creating opacity that undermines system reliability, auditability, and trust. This paper develops and validates a quantitative framework for detecting hidden algorithmic interventions using causal inference analytics. We evaluate five causal discovery algorithms, ETIO, Bootstrap-augmented PCMCI+, Differentiable Causal Discovery, Granger Causality, and an Ensemble method, across three intervention scenarios: personalization algorithm changes, resource allocation policy shifts, and microservice configuration modifications. Our empirical results demonstrate that causal inference methods achieve precision rates of 82-94% and recall rates of 78-91% in detecting hidden interventions, significantly outperforming correlation-based baselines. Time-series causal methods excel in temporal scenarios, while ensemble approaches achieve optimal overall performance with F1-scores of 0.89-0.92. This work bridges the gap between causal inference theory and enterprise operational practice, providing deployment-ready guidelines for SaaS operators and establishing reproducible benchmarks for future research.