Trend Micro Inc.

Vicone Corporation

Yao-Tang Chang

Yi-Li Cheng

Shih-Han Hsu

Chih-Sheng Chen

Shr-an Su

Ming-Hung Chen

Yu-Min Chang

Che-Fu Yeh

Peng-Shih Pu

Tsung-Fu Lin

Chih-Sung Chen

Wei-Jen Chang

Yi-Hong Chu

Pei-Zhong Luo

A large language model is trained by inputting a TARA configuration of an ECU of a connected vehicle, an EEA specification, and IDPS rules of the ECU. Software modules are used to extract feature vectors for input to the LLM. The LLM is fine tuned by further inputting later versions of the ECU and data from other ECUs of the vehicle. IDPS rules for another ECU of the vehicle which has no rules or few rules are generated by inputting the TARA configuration of the other ECU into the trained LLM and prompting the LLM to generate rules. The prompt may include instructions to minimize overhead on the other ECU, reduce rules within the vehicle, etc. The generated rules are output from the LLM and stored within the IDPS engine of the other ECU, stored on a computer, transmitted to another vehicle. Rules for two other ECUs may be generated.

Generation of TARA-based IDPS rules utilizing generative artificial intelligence

Versions of an application program are evaluated to protect a customer from a supply chain attack. The versions of the application program are executed in to identify behaviors exhibited by the versions of the application program, each of the behaviors including activities that perform computer operations. A behavior change is detected by identifying a behavior that is not common to the versions of the application program.

Protecting customers against supply chain attacks by detecting a behavior change between versions of an application program

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

One embodiment relates to a computer-implemented method for detecting malware-infected electronic mail messages. A server computer monitors an email account which is associated with a unique email address for receipt of an email message, the unique email address being associated with a unique contact in an address book on a client computer. The server computer determines that an email message addressed to the unique email address has been received and performs automated analysis to confirm that the email message contains malware. Other embodiments, aspects and features are also disclosed.

Apparatus and method for detecting malware-infected electronic mail

A computer-implemented method for reducing phishing attacks that employ malicious websites is provided. The computer-implemented method includes gathering information about potentially malicious websites from a plurality of sources. The computer-implemented method also includes reducing the number of potentially malicious websites gathered; thereby creating a malicious website database that tracks at least a subset of the potentially malicious websites. In addition, the computer-implemented method includes generating a malicious website blacklist from the malicious website database, each website in the malicious website blacklist satisfying inclusion criteria, wherein the malicious website blacklist is employed to decide whether an access request to a given website is permitted.

Aggregation-based phishing site detection

Growing community of inventors

Taipei, Taiwan

Yao-Tang Chang