Adaptive Machine Learning Models for Real-Time Anomaly Detection in Dynamic Systems

• Author(s): Alejandro Palacino
• Paper ID: 1707126
• Page: 358-369
• Published Date: 30-11-2021
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 5 Issue 5 November-2021

Abstract

Data integration results in the formation of systems that make society technologically developed than the world as it was 10 years ago. Rapidly, digital, and physical systems that deal with energy comprise significant constructive needs for anomaly detection in real-time to provide a high level of security for operational efficiency; traditionally, even unknown to the other, and nowadays-are faced with a major problem of a buy-and-bye method. The drawback of these methods is that they are usually driven by pre-defined set rules and are static with human-designed mortality rate is used for the detection of anomalies in which they do not conform. Adaptive machine learning (ML) models were specifically sponsored by tax payers and bring about a solution to let real-time anomaly detection happen depending upon self-learning objectives, incremental training, and continuous adjustments to parameter setting. Parameters-generated algorithms are fed through new data by performing an efficiency signal gain applicable to dismissing or maintaining data anomalies mostly in the cybersecurity domain but also in financial fraud detection, predictive maintenance, and industries (Wang & Chen, 2022). This paper will offer an overview of techniques in adaptive machine learning for real-time anomaly detection and will discuss online learning, reinforcement learning, ensemble learning, and deep learning-based detection. Online learning enables parameters for values that will change continuously with every bit of information; for this reason, they must be applied to streaming data (Liu et al., 2023). In reinforcement learning, the method further enhances the accuracy of anomaly detection by learning optimal policies as far as anomaly detection is concerned, with the addition of complex epoch patterns and associating temporal data sets to deep learning techniques such as Long Short-Term Memory (LSTM) networks and autoencoders (Nair & Gono, 2021). The ensemble methods combine multiple models to complement each other, possibly making it most effective at large false positives on diverse and unsafe datasets (Gupta & Kumar, 2021). Real-world data concerning financial crime, cybersecurity intrusion, and preventive maintenance were used for an in-depth analysis and benchmarking among these methods. The deeply learning- based adaptive models consistently provide a higher anomaly detection accuracy, giving rise to F1-Scores greater than 94%, with reinforcement learning models due for the second position, offering a fine adjustment for the trade-off between adaptability and computational efficiency (Chen et al., 2023). The online learning schemes, as efficient as they are, have the shortcomings due to the presence of the data drift that requires the integration of some drift-detecting solutions (Liu et al., 2023). Despite the advantages, numerous challenges arise, like computational complexity, generalization, and interpretability, especially in such implementations requiring deep learning (Smith et al.,2022). For these problems going forward, there must be further research into hybrid learning strategies, federated learning, and explainable AI techniques, which can enhance transparency about decision-making and lower recorded bias. Moreover, privacy-preserving techniques, such as differential privacy and secure multi-party computation, should be seriously considered to safeguard valid data, for application in real-time detection imperatives, particularly pertaining sensitive areas like finance and healthcare (Zhang et al., 2021). The review article partners with considerable ability to take the field of adaptive machine learning foormed into a responsible and satisfactory setting of technology, definitively updating motives, limitations, and exploration of research vistas. Emphasis is laid on the encouragement to develop a large array of models that are acceptable for perfecting proper pace for detection and computational efficiency from the get-go until adaptability ultimately kicks in. The accounts warrant a solid layout regarding the validation scheme for reversing classification of severely overfit entities through enhancing the generalization properties of models for diverse data- representation frames in starting from this margin. Concisely, future research may pursue to fine- tune real-life adaptive models with reinforcement learning combo put over the top of deep neural networks, follow the line of meta-learning, and put the underlying action of edge computing into practice for decentralized anomaly detection in IoT and industrial operation (Wang et al., 2023).

Keywords

Adaptive machine learning, real-time anomaly detection, online learning, reinforcement learning, deep learning, cybersecurity, financial fraud, predictive maintenance.

Citations

IRE Journals:
Alejandro Palacino "Adaptive Machine Learning Models for Real-Time Anomaly Detection in Dynamic Systems" Iconic Research And Engineering Journals Volume 5 Issue 5 2021 Page 358-369

IEEE:
Alejandro Palacino "Adaptive Machine Learning Models for Real-Time Anomaly Detection in Dynamic Systems" Iconic Research And Engineering Journals, 5(5)