# ADAL-NN: Anomaly Detection and Localization Using Deep Relational Learning in Distributed Systems
###### Abstract
This method offers a significant increase in efficiency, as well as robustness and generalization. 2. Rarely has log analysis been used to investigate hidden performance issues. Our method for detecting anomalies involves adding semantic and temporal information to log sequences. This allows us to identify a variety of anomalies, such as sequential log anomalies and latent performance issues. 3. To identify anomalies at the instance level, a heuristic technique is developed. This heuristic technique can be utilized to rapidly detect and localize anomalies in a distributed system. 4. We collect a dataset of real-world Papertrail logs and use this research to identify and locate anomalies. Papertrail collects log data from a variety of sources, such as servers, applications, and devices, and provides a central location for viewing and analyzing these data. The results prove the validity and dependability of this investigation. We evaluate this study using both synthetic and actual datasets. Using neural networks and relation graphs, we propose a method for anomaly detection and localization in distributed systems and name it ADAL-NN, which stands for \"Anomaly Detection and Localization using Neural Networks\". We describe the specifics of our approach and evaluate its efficacy through a series of simulation experiments on a distributed system simulator. Our results indicate that our method for detecting and localizing anomalies in distributed systems is effective. Several different areas are explored in this study, such as log correlation, heuristic-based anomaly localization, and sophisticated time embedding. Figure 1 also depicts the real-time logs we will be using. Anomaly detection and localization in a distributed system has many advantages. 1. Improved system stability: System stability is improved when anomalies are found and corrected before they have a chance to grow into major issues that would threaten the system's integrity. 2. Increased performance: Improved responsiveness and productivity can result from the use of anomaly detection and localization to pinpoint the source of performance degradation and to fix it. 3. Improved resource utilization: Resource optimization and waste reduction can be achieved through the use of anomaly detection and localization by highlighting and resolving potential resource contention issues. 4. Increased reliability: In order to improve the system's reliability, anomaly detection and localization can be used to pinpoint and repair the underlying causes of any unexpected behavior. 5. Better decision making: Better decision making and improvement opportunity identification can be achieved through anomaly detection and localization because of the enhanced understanding of the system's behavior and performance that is made possible. A total of five parts make up this study. Section 1 discusses the historical context and theoretical justifications for the research. There is a discussion of the research process and its results in this section. The second part of this paper is a literature review on log processing, anomaly detection, and anomaly localization. In addition, a study table is provided in this section for the purpose of filling in potential knowledge gaps. In Section 3, we lay out in great detail the ADAL-NN methodology that will be used throughout the various procedures. To better visualize the ADAL-NN proposed technique, an architecture diagram for anomaly detection and localization is provided here as well. In Section 4, we go over the implementation details. Extensive explanations of the dataset and the results of numerous log parsing, anomaly detection, and anomaly localization experiments are provided. Section 5 wraps up the research and discusses what comes next.