Trend Micro Inc.

Peng-Shih Pu

Ming-Hung Chen

Che-Fu Yeh

Shr-an Su

Yu-Min Chang

Chih-Chia Chen

Yi-Li Cheng

Chao-Yu Chen

Ming-Tai Allen Chang

Yao-Tang Chang

Shih-Han Hsu

Cheng Hsin Hsu

Cheng-Hsin Hsu

Yu-Fang Tsai

Pei-Yin Wu

Tsung-Fu Lin

Shu-Ying Liao

Casper Wang

Rung Chi Chen

One embodiment disclosed relates to a system for detecting anomalous messaging, discovering compromised accounts, and generating responses to threatened attacks. The system utilizes API commands and log forwarding for interaction and communication between a messaging and account hunting platform, other hunting platforms, an action center, and a security operations center. Another embodiment relates to a method of, and system for, performing a complete root cause analysis. Another embodiment relates to a method of, and system for, anomaly discovery which may advantageously utilize reference data to correlate different anomalies for reporting as a single incident.

Systems and methods for detecting and responding to anomalous messaging and compromised accounts

A method for determining which web page among multiple candidate web pages is similar to a given web page. For each candidate web page, a set of scoring rules is provided to score the components therein. When the given web page is compared against a candidate web page, each component that is found in both the given web page and the candidate web page under examination is given a score in accordance with the set of scoring rules that is specific to that web page under examination. A composite similarity score is computed for each comparison between the given webpage and a candidate web page. If the composite similarity score exceeds a predefined threshold value for a comparison between the given webpage and a candidate web page, that candidate web page is deemed the web page that is similar.

Dynamic page similarity measurement

A smart home includes Internet of things (IOT) devices that are paired with an IOT gateway. A backend system is in communication with the IOT gateway to receive IOT operating data of the IOT devices. The backend system generates a machine learning model for an IOT device. The machine learning model is consulted with IOT operating data of the IOT device to detect anomalous operating behavior of the IOT device. The machine learning model is updated as more and newer IOT operating data of the IOT device are received by the backend system.

Cybersecurity for a smart home

Unwanted web contents are detected in an endpoint computer. The endpoint computer receives a web page from a website. The reputation of the website is determined and the web page is scanned for malicious codes to protect the endpoint computer from web threats. To further protect the endpoint computer from web threats including mutating unwanted web contents, page structure traits of the web page are generated and compared to page structure traits of other web pages detected to contain unwanted web contents.

Methods and systems for detecting unwanted web contents

A mobile computing device includes a roaming bandwidth advisor for determining size information of web data (e.g., webpage, streaming media) before the web data is received in the mobile computing device. The mobile computing device may cooperatively work with a cloud computing system to obtain size information of the web data. Roaming fee information (e.g., the size of the web data and/or associated roaming fee) for receiving the web data is displayed on the mobile computing device before a request for the web data is sent out of the mobile computing device.

Roaming bandwidth guidance for mobile computing devices

Uniform resource locator (URL) patterns are found in browsing histories of client computers. The URL patterns are employed to find URL browsing chains for particular client computers. A URL browsing chain includes URL requests that match URL patterns arranged in sequential order by time stamp. The URL browsing chains may be normalized and then evaluated for web threat infection chains. URL browsing chains that are deemed to be web threat infection chains are built into a model. The model may be deployed locally in a client computer or in a remotely located server computer to detect web threat infection chains.

Methods and apparatus for detecting web threat infection chains

Growing community of inventors

Taipei, Taiwan

Peng-Shih Pu