Trend Micro Inc.

Lite-on It Corporation

Macronix International Co., Ltd.

Tomofun Co., Ltd.

Chia-Yen Chang

Wen-Kwang Tsao

Jonathan James Oliver

Hsin-Ping Cheng

Fyodor Yarochkin

Federico Maggi

Ping Huan Wu

Jiri Gogela

Leslie Zsohar

Cheng-Yao Liao

Brandon Niemczyk

Min-Wei Chen

Jayson Pryde

Peng-Yuan Yueh

Te-Ching Chen

Yo-Chen Victor Chang

PingHuan Wu

Po-I Wang

Yueh-Lin Li

Annie Bu

Mun-Hou Wong

Liam Huang

Chien-Yi Chiu

Ping-Huan Wu

Allen Lai

Joseph Cepe

Paul Christian D Pajares

Li-Hsin Hsu

Chiaming Chiang

Maria Estella Manly Reyes

A computer system generates a hierarchical evolutionary tree of digests of sample files. The digests are generated using a locality sensitive hashing function. The digests are grouped into several clusters, and the clusters are grouped into several nodes. The nodes are connected in hierarchical order to generate the hierarchical evolutionary tree. A digest of a file being evaluated for malware is generated using the locality sensitive hashing function. The digest is put in a cluster of the hierarchical evolutionary tree having digests that are most similar to the digest relative to digests of other clusters of the hierarchical evolutionary tree. The digest is identified to be of the same malware family as the digests of the cluster.

Detection and identification of malware using a hierarchical evolutionary tree

An interactive device for animals is provided that includes a main body, a driving module and a first rotating member. The main body includes an accommodating groove, an opening, and a communicating channel. The driving module is disposed on the main body. The first rotating member is rotatably disposed in the main body and separates the communicating channel and the accommodating groove. When the driving module drives the first rotating member to rotate in a first rotating direction, the first rotating member drives at least one object disposed in the accommodating groove to enter the communicating channel and leave the main body through the opening.

Interactive device for animals

A computer network includes a camera node, a network access node, a verification node, and a display node. Video content recorded by a camera at the camera node is transmitted to the display node and to the verification node for verification. The video content is verified at the display node and at the verification node. Recording metadata of the video content is stored in a distributed ledger and retrieved by the display node to verify the video content. The verification node receives, from the network access node, verification data for verifying the video content.

Secure video content transmission over a computer network

A system for stateful detection of cyberattacks includes an endpoint computer and a backend computer system. The endpoint computer monitors for cyberattacks and sends out queries for detected security events. The backend computer system stores observation data that are included in the queries. The backend computer system combines current observation data from a current query, relevant observation data from previous queries, and relevant cybersecurity data. The combined data are evaluated for cyberattacks.

Stateful detection of cyberattacks

A machine learning system includes multiple machine learning models. A target object, such as a file, is scanned for machine learning features. Context information of the target object, such as the type of the object and how the object was received in a computer, is employed to select a machine learning model among the multiple machine learning models. The machine learning model is also selected based on threat intelligence, such as census information of the target object. The selected machine learning model makes a prediction using machine learning features extracted from the target object. The target object is allowed or blocked depending on whether or not the prediction indicates that the target object is malicious.

Context-aware machine learning system

Disclosed are a method and system for static behavior-predictive malware detection. The method and system use a transfer learning model from behavior prediction to malware detection based on static features. In accordance with an embodiment, machine learning is used to capture the relations between static features, behavior features, and other context information. For example, the machine learning may be implemented with a deep learning network model with multiple embedded layers pre-trained with metadata gathered from various resources, including sandbox logs, simulator logs and context information. Synthesized behavior-related static features are generated by projecting the original static features to the behavior features. A final static model may then be trained using the combination of the original static features and the synthesized features as the training data. The detection stage may be performed in real time with static analysis because only static features are needed. Other embodiments and features are disclosed.

Method and system for static behavior-predictive malware detection

A system for evaluating files for cyber threats includes a machine learning model and a locality sensitive hash (LSH) repository. When the machine learning model classifies a target file as normal, the system searches the LSH repository for a malicious locality sensitive hash that is similar to a target locality sensitive hash of the target file. When the machine learning model classifies the target file as malicious, the system checks if response actions are enabled for the target file. The system reevaluates files that have been declared as normal, and updates the LSH repository in the event of false negatives. The system disables response actions for files that have been reported as false positives.

Evaluation of files for cyber threats using a machine learning model

A global locality sensitive hash (LSH) database stores global locality sensitive hashes of files of different private computer networks. Each of the private computer networks has a corresponding local LSH database that stores local locality sensitive hashes of files of the private computer network. A target locality sensitive hash is generated for a target file of a private computer network. The global and local LSH databases are searched for a locality sensitive hash that is similar to the target locality sensitive hash. The target file is marked for further evaluation for malware or other cybersecurity threats when the target locality sensitive hash is not similar to any of the global and local locality sensitive hashes.

Evaluation of files for cybersecurity threats using global and local file information

A method of detecting and compensating fail pixels in a holographic storage system. The method includes steps of: providing a plurality of image frames to show on a data plane for all pixels on the data plane being capable of outputting a light state or a dark state; sequentially recording the image frames into a storage medium; detecting the image frames by using a detecting apparatus for all pixels on the detecting apparatus being capable of outputting sensing signals corresponding to the light state and the dark state; defining a sensing difference value, which is a difference of the sensing signal outputting the light state and the dark state generated by one pixel; comparing the sensing difference value with a threshold value; and defining the corresponding pixel is a fail pixel if the sensing difference value is smaller than the threshold value.

Method of detecting and compensating fail pixel in hologram optical storage system

A data reading method used in a reading device for reading a holographic storage medium is provided. The holographic storage medium has several data areas. Each data area has several data pages, and each data page forms a data image that has at least one reserved block pattern on the reading device. The reserved block pattern of the data image corresponding to a first data page is searched within a first searching region to obtain a first pattern position. The reserved block pattern of the data image corresponding to a second data page is searched within a second searching region to obtain a second pattern position. A third searching position is then determined according to the first and second pattern positions. The reserved block pattern of the data image corresponding to a third data page is searched within a third searching region whose center is at the third searching position.

Data reading method

A method of designing a look-up table of a finite-state encoder, applied to a finite-state encoder, comprises steps of: determining a bit length of a legal output codeword derived from the finite-state encoder and a restrict condition; collecting a plurality of legal output codeword satisfied the restrict condition; determining a bit length of an input codeword derived to the finite-state encoder according to the amount of the plurality of the legal output codeword; determining the amount of states in the finite-state encoder according to the bit length of the input codeword and the bit length of the legal output codeword; dividing the plurality of legal output codeword to a plurality of subset according to the amount of the states in the finite-state encoder and a specific mathematic equation; determining the amount of the legal output codewords in the plurality of subset; and, completing the look-up table through determining a relationship among a present state, a previous state, and a corresponding subset, wherein the amount of the subsets used in the look-up table is greater than the amount of the states in the finite-state encoder and smaller than two times of the amount of the states in the finite-state encoder.

Encoding method and encoder for generating balanced code or constant weighted code

The present invention provides a method for determining output signals of a Viterbi decoder. Firstly, in step (a), a plurality of digital signals are received through a path memory module of the Viterbi decoder with decoding an input signal; in step (b) the received digital signals in step (a) are compared with a default number; and in step (c), an output according to the comparison result provided by step (b) is determined.

Method for determining output signals of a Viterbi decoder

A method for simplifying a Viterbi decoder includes receiving a partial response, and determining an amount of redundant selector modules according to a tap number of the partial response; analyzing an output signal of the redundant selector modules for determining an initial input signal; and taking Viterbi decoding process to a sequence of data in the Viterbi decoder according to a forward trace status and the initial input signal.

Method for simplifying a viterbi decoder and a simplified viterbi decoder using the same

A decoding method for optical storage device, which decodes channel bits upon a disc by partial response maximum likelihood (PRML) detection, is proposed. Unlike the conventional skills, the present method does not decode the detected RF signals directly, but adjusts values of parts of detected RF signals to their original ideal values before channel decoding instead, thereby reducing the decoding error rate of channel bits. The decoding method includes the step of: retrieving a first sequence of RF signals; identifying if the first sequence of RF signals contains a signal combination which satisfies a particular condition; adjusting a plurality of RF signals in the first sequence of RF signals to some pre-determined RF signals' value and then forming a second sequence of RF signals; and decoding the second sequence of RF signals. In a preferred embodiment of the invention, the method is applied to an optical storage system of PR (1,2,2,2,1) channel.

Growing community of inventors

Taipei, Taiwan

Chia-Yen Chang