AI-Driving Anomaly Detention in Distributed Data Engineering Frameworks

Abstract

Due to the overwhelming number of solutions for Big Data processing during the last few years, distributed data engineering frameworks have become one of the most effective tools for real-time analyses and decision-making in numerous domains. However, these frameworks are becoming more expansive due to the sheer complexity that is driving the scale of these systems, and this is where the anomalies – or variations from the norm or anticipated patterns – are proving to be very hard to identify. Originally applied anomaly detection techniques are not effective when dealing with dynamic high dimensional and real time-based distributed environments. Thus, the subject of this paper is the improvement of detecting anomalies in distributed data engineering frameworks using Artificial Intelligence (AI) approaches. This study aims at developing a rich framework for real-time anomaly detection with the help of the advanced machine learning techniques including deep learning-based auto encoder, generative adversarial network –GAN and recurrent neural network –RNN. It proposed solves the problem of unavailable labeled data by using unsupervised as well as semi supervised learning and targets scalability by integrating it to operate in big data ecosystems such as Apache Spark, Hadoop and Flink. Overall performance analysis of the proposed framework is carried out by employing both synthetic and real-life datasets for accuracy, recall, F1-measure and time complexity analysis. The obtained results confirm the superiority of the proposed system over basic approaches and indicate its flexibility under changing loads and the possibility of detecting different forms of anomalous behavior such as point anomalies, contextual anomalies, collective anomalies, etc. In the same way, it clarifies how model interpretability, data privacy, and efficient resource use in a distributed environment are critical issues in this study. These results also highlight the improvement prospects of AI-based anomaly detection in increasing the dependability, extensibility, and security of distributed data engineering platforms. It will open up new possibilities for further development of smart anomaly detection solutions, and provides the basis for further extension, possible in the form of federated learning or other combinations of AI approaches.