To embrace the significance of anomaly detection in modern information systems and information security, one should answer a question, what does it mean to be an anomaly? Overall, a standard system functions within some predetermined limitations, with all incoming and outgoing data fitting a range of values typical for it. Thus, when individual deviations from the typical expectations are identified, they always serve as red flags for the security personnel and analysts.
Some areas in which anomaly detection is popular include:
- Fraud detection (insurance, banking)
- intrusion detection (computer networks, national surveillance)
- medical informatics (diagnosis, disorder detection)
- fault/damage detection (commerce, industry)
In a nutshell, detecting anomalous data within a system means that something goes wrong. For instance, if all buyers of an e-shop pay $100 on average for a pair of shoes, and some client pays $1,000 for the same purchase, it is an anomaly meaning that either there is a problem with the client's bank or some glitch occurred in the merchant's system. Similarly, if an insurer receives an average MRI check for $300-400 from patients and suddenly gets a $550 check for the same procedure, it should be an alert about a potentially fraudulent transaction requiring a closer investigation.
Another example is the significance of anomaly detection in a computer network. If some anomalous traffic patterns are identified in it, this could be a sign of sensitive data leakage from a hacked computer. Anomalies in the nervous signal transmission on the MRI scan may be a sign of some serious degenerative disease. At the same time, bizarre purchases and cashing activities with a client's credit card may be an alert of the card's theft.
Thus, as one can see, anomaly detection is helpful in many industries and fields, helping specialists identify deviations from the norm to investigate them closer and determine the cause of such deviation. The core to successful detection practices in any organization is to define 'anomalous' for their own datasets, to set specific detection signals for the analytical systems, and to feed in the feedback about correct/incorrect anomaly labeling for the system to learn.