The Toronto-dominion Bank

Arilou Information Security Technologies Ltd.

Moshe Karl

Gil Litichever

Ziv Levi

Avraham Entelis

Nir Agiv

Allie David Shay

One example method includes receiving indicator of compromise (IOC) intelligence including an IOC. The IOC can then be validated. One or more security systems to add the IOC can then be determined based on one or more risk indicators of the IOC. The IOC can then be added to the one or more security systems. The validation of the IOC may include using machine learning to determine a likelihood of a threat.

Automatic validations and prioritizations of indicators of compromise

A testing system, tool and method for determining anonymization process quality are disclosed. The testing tool includes a processor, a communications module coupled to the processor, and a memory coupled to the processor. The memory stores computer executable instructions that when executed by the processor cause the processor to obtain a first dataset comprising data to be anonymized. The processor obtains a second dataset comprising data resulting from the first dataset being subjected to an anonymization process by a testing platform to anonymize a portion of the first dataset and generates a visual representation of the first dataset. The processor generates a second visual representation of the second dataset, the second visual representation reflecting at least in part a transformation from the first dataset to the second dataset. The processor generates an output including the first and second visual representations, the output being illustrative of the transformation.

Test and validation of privacy protection quality of anonymization solutions

A system, non-transitory computer readable medium and method for detecting anomalous activity based on a data distribution is disclosed. The system includes a processor, and a memory coupled to the processor. The memory stores computer executable instructions that, when executed by the processor, cause the processor to obtain a plurality of datapoints defining a data distribution. The processor classifies the plurality of datapoints as isolated or non-isolated datapoints, and clusters at least some of the non-isolated datapoints into at least one mode segment. A reference mode from the at least one mode segment is identified, and at least one outlier being either an isolated or non-isolated datapoint separated from the reference mode by a predetermined number of isolated datapoints representing a flat portion is identified. An action is initiated based on the identified at least one outlier.

System and method for detecting anomalous activity based on a data distribution

Method and system for lossless and stateless compression scheme is used with a fixed-length data such as frames. Frames having a payload of M bits length are mapped into a payload of N bits length, where N&#x3c;M. The N bits payload of each received frame is extracted, and mapped using a memory, PLD, or a processor, to reconstruct the uncompressed M bits payload, and to form the original frame. The reconstruction may use a set of N coefficients that are each multiplied by the corresponding received payload bit, and summarized modulo-2 to obtain the original pre-compressed M payload bits. The method and system may be used with a vehicle bus, such as Controller Area Network (CAN). The compressed frames may use the same or different protocol than the uncompressed ones, and may further carry an additional code such as metadata, error detection or correction code, or authentication related code.

Growing community of inventors

Petah Tikva, Israel

Moshe Karl