As cybercrime and concerns about cybercrime grow, tools for preventing and interdicting cybercrime, specifically for reducing online fraud, are proliferating in the marketplace. Many of these new tools bring real value, in that they do in fact make it harder for criminals to operate, and such tools do reduce fraud.
Several categories of tools and services compose this security ecosystem. On the supply side there are various intelligence services. The forms of intelligence provided may include information about:
- Users: Users and associated credentials, credential and identity proofing results, user attributes, user history, behavioral biometrics, and user behavioral analysis. Output format is generally a numerical range.
- Devices: Device type, device fingerprint from Unified Endpoint Management (UEM) or Endpoint Mobility Management (EMM) solutions, device hygiene (operating system patch versions, anti-malware and/or UEM/EMM clients presence and versions, and Remote Access Trojan detection results), Mobile Network Operator carrier information (SIM, IMEI, etc), jailbreak/root status, and device reputation. Output format is usually a numerical range.
- Cyber Threat: IP and URL blacklisting status and mapped geo-location reputation, if available. STIX and TAXII are standards used for exchanging cyber threat intel. Besides these standards, many proprietary exchange formats exist as well.
- Bot and Malware Detection: Analysis of session and interaction characteristics to assess the likelihood of manipulation by bots or malware. Output format can be Boolean, or a numerical range of probabilities, or even text information about suspected malware or botnet attribution.
Risk-adaptive authentication and authorization systems are the primary consumers of these types of intelligence. Conceptually, risk-adaptive authentication and authorization functions can be standalone services or can be built into identity and web access management solutions, web portals, VPNs, banking apps, consumer apps, and many other kinds of applications.
Depending on the technical capabilities of the authentication and authorization systems, administrators can configure risk engines to evaluate one or more of these different kinds of intelligence sources in accordance with policies. For example, consider a banking application. In order for a high-value transaction (HVT) to be permitted, the bank requires a high assurance that the proper user is in possession of the proper registered credential, and that the requested transaction is intended by this user. To accomplish this, the bank’s administrators subscribe to multiple “feeds” of intelligence which can be processed by the bank’s authentication and authorization solutions at transaction time.
The results of a runtime risk analysis that yields ‘permit’ may be interpreted as “yes, there is a high probability that the proper user has authenticated using a high assurance credential from a low risk IP/location, the request is within previously noted behavioral parameters for this user, and the session does not appear to be influenced by malware or botnet activity.”
This is great for the user and for the enterprise. However, it can be difficult to implement by the administrators because there are few standards for representing the results of intelligence-gathering and risk analysis. The numerical ranges mentioned above vary from service to service. Some vendors provide scores from 0 to 99 or 999. Others range from -100 to 100. What do the ranges mean? How can the scores be normalized across vendors? Does a score of 75 from intel source A mean the same as 750 from intel source B?
Perhaps there is room for a little more standardization. What if a few attribute name value pairs were introduced and ranges limited to improve interoperability and to make it easier for policy authors to implement? Consider the following claims set, which could be translated into formats such as JWT, SAML, XACML, etc :
The above example* shows an Issuer of “IntelSource”, with timestamp and expiry, Audience of “RiskEngine”, Subject (user ID), and 3 additional attributes: “UserAssuranceLevel”, “DeviceAssuranceLevel”, and “BotProbability”. These new attributes are composites of the information types listed above for each category. Ranges for all 3 attributes are 0-99. In this example, the user looks legitimate. Low user and device assurance levels and/or high bot probability would make the transaction look like a fraud attempt.
KuppingerCole believes that standardization of a few intelligence attributes as well as normalization of values may help with implementation of risk-adaptive authentication and authorization services, thereby improving enterprise cybersecurity posture.
*Thanks to http://jwtbuilder.jamiekurtz.com/ for the JWT sample.
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