Over the past two decades, the IT environment has seen a tremendous transition as a result of technological innovation. In order to keep up with the demands, modern systems, including infrastructure and applications, are growing tremendously. They are scaling up to high-performance computing with lots of data centers on one end and scaling out to numerous distributed systems on the other. Log analytics can be used as a powerful security monitoring tool by utilizing unsupervised machine learning techniques.
Modern systems are also increasingly essential for supporting diverse services like social media platforms, search engines, and developing alternative decision-making processes, with the emphasis on client centricity. The importance of having information accessible to everyone, around the clock, seven days a week, rises as a result. For a successful IT Team to enable the system, availability and dependability become crucial KPIs to be assessed. Any interruptions, such as service or system failures or poor QoS, may cause the application to malfunction, which can lead to disgruntled users, a greater churn rate, and significant revenue loss. Therefore, ongoing monitoring of these systems is now considered standard practice.
It has become crucial to anticipate any anomalous system actions promptly in order to avoid interruptions. When the log data was smaller in the past, it was manually examined using keyword search or rule matching. The volume of logs has however skyrocketed with this contemporary large-scale distributed system, making human scrutiny impossible. As a result, Machine Learning (ML) for log analysis has recently gained a lot of momentum.
Using machine learning to find logs
Organizations are more and more interested in implementing machine learning for log analysis to comprehend, identify, and anticipate undesirable data incidents. A ML-based intelligent log analysis solution, for instance, has a chance to succeed in a large finance organization’s environment, which has 10,000+ apps at various maturity stages. In OS logs, Network NMS logs, Machine Data, APM, Netcool, BMC capacity, or Splunk, these programs’ logs and events have been aggregated. However, due to the size and unstructured nature of the logs, it is challenging for an ML model to determine whether a given logline is distinct from other instances of the same event.
It becomes crucial to provide the right set of meaningful data to the ML models so they can assess it and raise a flag when necessary. As a result, anomaly identification (also known as outlier analysis) is a crucial data analysis step. Here’s a quick rundown of how the logs are obtained, read, and supplied to the model before we explore anomaly detection and comprehend how ML models might be used.
The three primary stages of log analysis for anomaly identification are as follows:
- Log Gathering: How do you get logs from various sources?
- How do you read a log message so you can save it properly?
- How can you extract important data that can be used to train machine learning models, which in turn may be used to detect anomalies?
A data mining approach called anomaly detection looks for particular events that can arouse suspicion since they are statistically distinct from the rest of the datasets. Various ML ideas can be used for anomaly detection. However, when there is more data available for the model to learn from and forecast, the results with ML models would be more accurate. However, it could present a few difficulties for logging. In order to create accurate predictions, one must first have access to the weeks and months worth of data that are required. The model must learn the logs from scratch in order to make accurate predictions when the behavior of the application changes. Both supervised and unsupervised algorithms can be used for anomaly detection, depending on the quantity, quality, and ML concepts used.
Supervised methods: Requires labeled training data that clearly distinguishes between what occurs frequently and what does not. Then a model is investigated to determine how to employ the discrimination between the cases.
Methods without labels: Labels are not necessary. The models will attempt to independently identify the patterns and correlations in the data, which are then applied to make predictions.
Both groups offer a variety of anomaly detection strategies. For instance, supervised techniques like SVM, Random Forest, Logistic Regression, and Decision Tree can be used. SVM, for instance, classifies the likelihood of an incident being connected to particular words in a logline. Common terms like “error” or “unsuccess” would be linked to an incident and have a higher probability score than other words. An issue is found using the Log message’s overall score. However, as was previously noted, supervised algorithms would need a large amount of data to predict effectively, and application behavior is constantly changing, making their deployment in real-time settings challenging.
Unsupervised learning has training data that is unlabeled, in contrast to supervised approaches, which makes it more suitable for a real-time production setting. Common unsupervised methods include association rule mining, PCA, and clustering techniques.
Organizations have recently begun utilizing deep learning to find anomalies in logs. The long short-term memory is a recently prominent neural network (LSTM). The system log is represented as a series of natural language words using the neural network model. This makes it possible to examine log patterns and spot anomalies when the trained model deviates from reality. Workflows are also built underneath the underlying system log so that programmers can examine the anomaly and carry out the required RCA for it.
The difficulty with this strategy is that, in addition to the substantial amount of data needed to train it, Deep Learning can be quite computationally costly. Despite the fact that GPU instances are available to train the models, they are expensive. Also available for training deep learning algorithms are third-party services like Nginx and MySQL. However, these services focus on well-known operating environments, and any bespoke software that is part of the landscape is occasionally overlooked or missed from monitoring.
Real-time Log analysis application cases that might be possible
Security compliance: In the banking and healthcare industries, securely storing sensitive data is essential. Regex and advanced analytics can be used to identify PII and PHI data stored in locations other than the destination location. Therefore, it is crucial to identify PII, PCI, and PHI data early in a log lifetime, which lowers the likelihood of compliance violations.
Any application or system disruption has financial repercussions. System health monitoring and failure prediction. As a result, it is crucial to identify the critical factors that contribute to system failure and to make sure that the alert is raised for prospective outages. This use case might be applied to any discrete manufacturing facility where having a good maintenance strategy is essential for increasing overall equipment availability and decreasing downtime. Analysis can be done using the PLC’s log messages.
Network management allows decision-makers in the telecommunications sector to effectively monitor and evaluate data gathered from various devices and network components throughout the system. By studying the usage patterns, this can assist identify issues before customers ever see them, increasing customer happiness and providing an opportunity to upsell to more advanced gadgets.
Monitoring for security: Since the pandemic, cybercrimes have increased. Every firm must continuously adjusting its current security monitoring procedures. Log analytics can be used as a powerful security monitoring tool by utilizing unsupervised machine learning techniques. This will facilitate speedier recovery, event reconstruction, and the detection of security breaches.
Conclusion – Machine Learning
There are several approaches for detecting anomalies, however defining anomalies correctly requires the help of data analysts. Many firms have developed anomaly detection systems, however the problem is that the methodology they present would only be appropriate for that specific dataset and use case. Techniques from one profession are probably ineffective in another and would even be contagious. Therefore, before applying the method to the production, it is crucial to focus the problem and test it using existing benchmarks or datasets (if available).