Proactive anomaly detection is an approach to enterprise level information security where businesses gather, process and present data to make informed decisions about security. It’s also used by software engineers when debugging software code for security defects and in risk management.
The goal of proactive detection is to minimize the time delay required for response to security incidents. Delays in security detection can mean the difference between effective and ineffective security.
The goal of proactive anomaly detection is to identify and respond quickly to changes in a monitored system that could result in security. This involves collecting, processing, analyzing, and communicating anomalies that are detected.
In order to be effective, these data must be communicated to a member of the security team before the data is released to the general infrastructure or application servers. One of the primary benefits of using time series data to monitor security is the ability to make quick judgments regarding changes in critical processes and assets.
A primary challenge associated with proactive anomaly detection is identifying events that occur quickly and reliably. Time series are ideal for passive monitoring as they provide a continuous record of what is going on in an enterprise. However, for passive monitoring, such as in a manufacturing facility, real-time monitoring is often necessary to determine response times and to perform corrective actions promptly.
Most of today’s enterprise security systems use application-layer security solutions and they require an active monitoring capability. By enabling real-time application-layer security solution monitoring using near real-time monitoring capabilities, application-layer security solutions can monitor activity from just a few meters away. A prime example of an application layer security solution is a GSM alarm that can be remotely activated when an alert is received from one of the monitoring locations.
An important factor in GSM proactive anomaly detection is the ability of the communication network to withstand failure. The root cause of failures can often be determined by performing analysis using real-time data. When a company fails to update its monitoring system for root cause analysis, the company risks losing crucial information that can affect its ability to respond to disasters.
To ensure an efficient and effective response to failures in a manufacturing facility, the company must deploy a good root cause monitoring system that has the capability to detect weak links in the communication network. Similarly, an effective GSM monitoring system must allow the transmission of alerts at higher speeds and distances than conventional phone lines.
Data-driven security solutions are also a big part of a company’s defense strategy. Data-driven security solutions allow security measures to be proactive rather than reactive. The machine learning component of this technology enables the machine to remember and execute security measures automatically when it encounters a risk or potential threat, based on pre-existing criteria.
Machine learning significantly reduces the time required to implement security solutions because it enables the system to continuously learn over time by interacting with the outside world. Ultimately, a company’s security system can be made more effective through the combination of proactive anomaly detection and data-driven security solutions.