QA: What are the synergies between Fraud Analytics and CyberSecurity?

By: Bart BaesensSeppe vanden Broucke

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You asked: What are the synergies between Fraud Analytics and CyberSecurity?

Our answer:

Fraud analytics creates both opportunities as well as threats for cybersecurity. Think about intrusion detection as an example: predictive methods can be adopted to study known intrusion patterns, whereas descriptive methods or anomaly detection can identify emerging cyber threats. The emergence of the Internet of Things (IoT) will certainly exacerbate the importance of fraud analytics for cybersecurity. Some examples of new fraud treats are:

  • Fraudsters might force access to web configurable devices (e.g. Automated Teller Machines (ATMs)) and set up fraudulent transactions;
  • Device hacking whereby fraudsters change operational parameters of connected devices (e.g. smart meters are manipulated to make them under register actual usage);
  • Denial of Service (DoS) attacks whereby fraudsters massively attack a connected device to stop it from functioning;
  • Data breach whereby a user’s log in information is obtained in a malicious way resulting into identity theft;
  • Gadget fraud also referred to as gadget lust whereby fraudsters file fraudulent claims to either obtain a new gadget or free upgrade;
  • Cyber espionage whereby exchanged data is eavesdropped by an intelligence agency or used by a company for commercial purposes.

More than ever before, fraud will be dynamic and continuously changing in an IoT context. From an analytical perspective, this implies that predictive techniques will continuously lag behind since they are based on a historical data set with known fraud patterns. Hence, as soon as the predictive model has been estimated, it will become outdated even before it has been put into production. Descriptive methods such as anomaly detection, peer group and break point analysis will gain in importance. These methods should be capable of analyzing evolving data streams and perform incremental learning to deal with concept drift. To facilitate (near) real-time fraud detection, the data and algorithms should be processed in-memory instead of relying on slow secondary storage. Furthermore, based upon the results of these analytical models, it should be possible to take fully automated actions such as the shutdown of a smart meter or ATM.