Abstract: A behavior change detection system collects behavior from a service, such as an online service, and detects behavior changes, either permanent or transient, in the service. Machine learning hierarchical (agglomerative) clustering techniques are utilized to compute deviations between clustered data sets representing an “answer” that the service presents to a series of requests.

Abstract: Various embodiments provide an approach to classifying security events based on the concept of behavior change detection or “volatility.” Behavior change detection is utilized, in place of a pre-defined patterns approach, to look at a system's behavior and detect any variances from what would otherwise be normal operating behavior. In operation, machine learning techniques are utilized as an event classification mechanism which facilitates implementation scalability. The machine learning techniques are iterative and continue to learn over time. Operational scalability issues are addressed by using the computed volatility of the events in a time series as input for a classifier. During a learning process (i.e., the machine learning process), the system identifies relevant features that are affected by security incidents. When in operation, the system evaluates those features in real-time and provides a probability that an incident is about to occur.

Abstract: A behavior change detection system collects behavior from a service, such as an online service, and detects behavior changes, either permanent or transient, in the service. Machine learning hierarchical (agglomerative) clustering techniques are utilized to compute deviations between clustered data sets representing an “answer” that the service presents to a series of requests.

Abstract: Various embodiments provide an approach to classifying security events based on the concept of behavior change detection or “volatility.” Behavior change detection is utilized, in place of a pre-defined patterns approach, to look at a system's behavior and detect any variances from what would otherwise be normal operating behavior. In operation, machine learning techniques are utilized as an event classification mechanism which facilitates implementation scalability. The machine learning techniques are iterative and continue to learn over time. Operational scalability issues are addressed by using the computed volatility of the events in a time series as input for a classifier. During a learning process (i.e., the machine learning process), the system identifies relevant features that are affected by security incidents. When in operation, the system evaluates those features in real-time and provides a probability that an incident is about to occur.

Abstract: A synthetic gesture trace generator is described. In an embodiment, a synthetic gesture trace is generated using a gesture synthesizer which may be implemented in software. The synthesizer receives a number of inputs, including parameters associated with a touch sensor to be used in the synthesis and a gesture defined in terms of gesture components. The synthesizer breaks each gesture component into a series of time-stamped contact co-ordinates at the frame rate of the sensor, with each time-stamped contact co-ordinate detailing the position of any touch events at a particular time. Sensor images are then generated from the time-stamped contact co-ordinates using a contact-to-sensor transformation function. Where there are multiple simultaneous contacts, there may be multiple sensor images generated having the same time-stamp and these are combined to form a single sensor image for each time-stamp. This sequence of sensor images is formatted to create the synthetic gesture trace.