Using Machine Learning To Log Anomaly Detection
Anomaly detection is a critical process that is the core of many corporates and large-scale network systems. Logs are created to record and store useful data regarding the operation, processing, and behavior of the said anomaly detection systems. These logs help administrators look for any incoming attacks or system failures. But the way these logs are recorded leaves a lot of data that is never really fully analyzed, but the use of AI-based deep learning ML can change that. But to understand that, we have to first understand the current limitations in the Syslog anomaly detection logging process.
Limitations in the Current Anomaly Detection Logging Process
If you think Machine Learning is a new concept, you would be wrong. We are actually using a Machine Learning algorithm right now, but it’s quite generalized and doesn’t involve deep learning AI-based models. Currently, we use either Support Vector Machine or SVM for short and Random Forest to detect anomalies in string-based data. These models use a supervised version of ML and require a ton of data for the logs to be anywhere near accurate results.
Not to mention, they need to be trained for hours and hours, making it very costly and in some cases, nearly impossible to be used for any real-life situations. The overall costs, time, and amount of hardware a log file anomaly detection needs, really limit its applications and usefulness.
The Use of AI-based Deep Learning ML in the Anomaly Detection Logging Process
However, the use of deep learning-based ML changes everything. Deep learning uses powerful AI-based neural networks to process large volumes of data simultaneously, allowing it to find even the tiniest breaks in patterns that will ultimately help figure out an anomaly. Its speed and accuracy really set it apart from the traditional ML models we have been using for a while. However, not everything is as rosy as it sounds.
It also requires powerful hardware, particularly a compute-heavy GPU that can process large volumes of data parallelly. This can be done on publicly available open-sourced databases, which makes it a bit easy to deploy. Nonetheless, it is still an expensive setup. This is the reason why it’s being adopted at a rather slower rate.