Paypal, Inc.

Renderx, Inc.

David Tolpin

Michael Dymshits

Benjamin Hillel Myara

Raoul Christopher Johnson

Omri Moshe Lahav

Shlomi Boutnaru

Yuri Shafet

Liron Florens Ben Kimon

Eli Strajnik

Liron Ben Kimon

Amit Batzir

Nofar Betzalel

Aspects of the present disclosure involve systems, methods, devices, and the like for generating compact tree representations applicable to machine learning. In one embodiment, a system is introduced that can retrieve a decision tree structure to generate a compact tree representation model. The compact tree representation model may come in the form of a matrix design to maintain the relationships expressed by the decision tree structure.

System and method for compact tree representation for machine learning

Methods and systems for creating and analyzing low-dimensional representation of webpage sequences are described. Network traffic history data associated with a particular website is retrieved and a word embedding algorithm is applied to the network traffic history data to produce a low dimensional embedding. A prediction model is created based on the low-dimensional embedding. Browsing activity on the particular website is monitored. A set of sessions in the current browsing activity is flagged based on a result of applying the prediction model to the monitored browsing activity.

System and method for creating and analyzing a low-dimension representation of webpage sequence

Aspects of the present disclosure involve a system and method for malware detection. The system and method introduce a probabilistic model that can observe user transaction data over a predetermined window of time. Then, using posterior probability, the system can determine whether multiple users where present during the window observed.

System and method for malware detection

Machine learning techniques can be used to train a classifier, in some embodiments, to accurately detect similarities between different records of user activity for a same user. When more recent data is received, newer data can be analyzed by selectively removing particular sub-groups of data to see if there is any particular data that accounts for a large difference (e.g. when run through a classifier that has been trained to produce similar results for known activity data from a same user). If a sub-group of data is identified as being significantly different from other user data, this may indicate an account breach. Advanced machine learning techniques described herein may be applicable to a variety of different environments.

Advanced techniques for machine learning using sample comparisons

Anomalies in a data set may be difficult to detect when individual items are not gross outliers from a population average. Disclosed is an anomaly detector that includes neural networks such as an auto-encoder and a discriminator. The auto-encoder and the discriminator may be trained on a training set that does not include anomalies. During training, an auto-encoder generates an internal representation from the training set, and reconstructs the training set from the internal representation. The training continues until data loss in the reconstructed training set is below a configurable threshold. The discriminator may be trained until the internal representation is constrained to a multivariable unit normal. Once trained, the auto-encoder and discriminator identify anomalies in the evaluation set. The identified anomalies in an evaluation set may be linked to transaction, security breach or population trends, but broadly, disclosed techniques can be used to identify anomalies in any suitable population.

Population anomaly detection through deep gaussianization

System and method for creating and analyzing a low-dimensional representation of webpage sequences

Systems and methods for detecting data exfiltration using domain name system (DNS) queries include, in various embodiments, performing operations that include parsing a DNS query to determine whether that DNS query is likely to contain hidden data that is being exfiltrated from a system or network. Statistical methods can be used to analyze the DNS query to determine a likelihood whether each of a plurality of segments of the DNS query are indicative of data exfiltration methods. If one or multiple DNS queries are deemed suspicious based on the analysis, a security action on the DNS query can be performed, including sending an alert and/or blocking the DNS query from being forwarded.

Systems and methods for detecting data exfiltration

A system for predicting that a user session will be fraudulent. The system can analyze an incomplete session and determine the likelihood that the session is fraudulent or not by generating completed sessions based on the incomplete session.

Fraud prediction based on partial usage data

The systems and methods that detect malware from count vectors are provided. A count vector having multiple components is generated. The count vector tracks a number and types of system calls generated by a process. Each component in the count vector is mapped to a type of a system call that exists in an operating system. Multiple system calls generated by the process are received over a first time interval. Each system call is mapped to a component in the count vector. The count vectors are aggregated according to a second time interval into a vector packet. The vector packet is transmitted over a network to a malware detection system that uses the count vectors in the vector packet to determine whether the process is a malware process.

System call vectorization

Computer system drift can occur when a computer system or a cluster of computer systems deviates from ideal and/or desired behavior. In a server farm, for example, many different machines may be identically configured to work in conjunction with each other to provide an electronic service (serving web pages, processing electronic payment transactions, etc.). Over time, however, one or more of these systems may drift from previous behavior. Early drift detection can be important, especially in large enterprises, to avoiding costly downtime. Changes in a computer's configuration files, network connections, and/or executable processes can indicate ongoing drift, but collecting this information at scale can be difficult. By using certain hashing and min-Hash techniques, however, drift detection can be streamlined and accomplished for large scale operations. Velocity of drift may also be tracked using a decay function.

Advanced computer system drift detection

Techniques are provided for rendering media as layers. Logical units of media form a media stream. The media stream as a whole is processed to divide components within the units into assigned layers. The layers are then formatted to a desired output format in parallel with one another when dependencies permit. Next, each unit of media is rendered to the output format by superimposing or merging multiple layers to reconstruct each unit of media in the output format.

Techniques for rendering media as layers

A system for discovering programming variants. The system analyzes system calls from executing a program to generate programming code or executable for a particular OS and/or CPU that would perform the same or similar actions as the program. The code that is generated is then mutated, augmented, and/or changed to create variations of the program which still functions and/or obtains the same objectives as the original code.

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Ashdod, Israel

David Tolpin