User account takeovers, credentials theft, and online payment method takeovers have been, and continue to be the primary ways fraudsters cause big dollar losses and reputation damages to businesses throughout the years. More than 50% of successful damaging attacks are initiated with valid user credentials.
Geo location? Threat intelligence feeds? Device recognition? IP address correlation? Attackers are well aware of these detection techniques.
Fraudsters are becoming increasingly more sophisticated. Within minutes they can leverage worldwide clouds of cheap virtual machines to conceal their true physical locations and utilize specialized device identity spoofing tools in attempts to deceive the most sophisticated fraud detection systems as well as human experts.
Identifying malicious actor or suspicious transaction by IP address, computer device identity or browser user agent anomalies is becoming more challenging.
To build more sophisticated defenses we need to consider enriching traditional machine data sources with information that reflects unique and complex behavior patterns of a person behind the screen. That’s when we’ll start finding something that fraudsters and cybercriminals cannot steal or fake.
Humans are using computer systems in a ways that are unique and consistent with their own biology and physiology. The way humans click and type, the way humans use mouse and other input devices are pretty consistent with that person’s own behavior, habits, education level, and familiarity with a service or system.
Habits and behaviors are very difficult to change and if we can identify legitimate users by their typical behavior patterns - we can detect anomalies on a totally new level. Same goes with fraudsters - ability to identify and quantify behavior patterns of cyber criminal will allow us to uncover and neutralize threats that may be undetectable by other means.
The question is: can we recognize the user - or a class of users by some unique ways they use their input devices, such as mouse or keyboard? Enter Behavioral Biometrics: the field of study related to measure of uniquely identifying and measurable patterns in human activities.
This post will show one of the ways you can implement advanced user behavior biometrics solution for security based on Splunk and one of the Deep Learning frameworks.
Attempts to discover and classify behavior biometrics patterns were attempted by a number of industry players. Let’s consider the task of matching user to mouse activity. Traditional detection system executes complicated actions of feature extraction, data measurements and normalization. For each mouse movement event, the system would apply artificial trajectory smoothing, measure multiple points of velocity, acceleration, curvature, relative distances, inflection points, etc...
After such heavy pre processing traditional machine learning techniques are applied to extracted features. Model is trained and subsequent predictions are made.
The limitation of such approach is that complexity of task is artificially reduced to a subset of calculated features and the rest of data is ignored.
This almost guarantees that more complex, subtle, and yet very personal behavior patterns that are naturally present in the raw data will be missed.
To detect, extract and recognize infinitely complex behavior patterns that might be present in a treasure trove of user behavior data we need to look at complete, unfiltered datasets through data science that must go beyond traditional algorithms based on a subset of engineered features.
But how do we get access to detailed datasets representing user activity?
Just like with any other data source - it’s easy to get precise, fine-grained end user input device activity directly into Splunk.
Thanks to our talented senior software engineer Oleg Izmerly - here is the complete source code that demonstrates how to do exactly that.
Knowing that each mouse movement generates X and Y coordinates of mouse pointer and also a timestamp - we can collect this data and send it to Splunk to enrich traditional data sources, like clickstream, web and application logs. This way user’s session activity data will also contain information about potentially unique behavior patterns.https://www.splunk.com/blog/2017/04/18/deep-learning-with-splunk-and-tensorflow-for-security-catching-the-fraudster-in-neural-networks-with-behavioral-biometrics.html