Huazhong University of Science and Technology

Deqing Zou

Hai Jin

Weiqi Dai

Zhen Li

Peng Xu

Jing Tang

Yangrui Li

Jun Deng

Pei Duan

Yan Xia

Fengwei Wang

Jinkai Liu

Ziyi Liang

Kexuan Zhao

Bingcheng Zhao

Shuanghong He

Ruqian Zhang

A sample-difference-based method and system for interpreting a deep-learning model for code classification is provided, wherein the method includes a step of off-line training an interpreter: constructing code transformation for every code sample in a training set to generate difference samples; generating difference samples respectively through feature deletion and code snippets extraction and then calculating feature importance scores accordingly; and inputting the original code samples, the difference samples and the feature importance scores into a neural network to get a trained interpreter; and a step of on-line interpreting the code samples: using the trained interpreter to extract important features from the snippets, then using an influence-function-based method to identify training samples that are most contributive to prediction, comparing the obtained important features and the most contributive training samples, and generating interpretation results for the object samples. The inventive system includes an off-line training module and an on-line interpretation module.

Sample-difference-based method and system for interpreting deep-learning model for code classification

The present invention relates to a blockchain-based system and method for preventing unauthorized deletion of surveillance video, the system at least comprising: a plurality of camera components, for generating surveillance video; at least one video-recording node, for connecting to a blockchain network and randomly generating a key pair, splitting the video file received from the camera components into a plurality of video-file blocks and randomly sending them to participation nodes across the blockchain network for storage, and recording hash values of the stored video files and node information; and a blockchain network device, for updating the hash values of the video files uploaded by the video-recording nodes based on a smart contract. In the present invention, uploading and storing time for camera-videos is recorded by the smart contract as tamper-resistant time reference for law enforcement, protecting video data from malicious deletion at video-recording nodes without the need of redundant space.

Blockchain-based system and method for preventing unauthorized deletion of surveillance video

The present invention provides an anti-trapdoor-leakage on-chain data restoration system, at least comprising: a blockchain node, for broadcasting transaction data of a request-initiating person to blockchain nodes and proposer nodes in other groups, respectively; and a proposer node, for performing computation of a Chameleon-Hash function using a key set that is generated by a key-generating module provided in the proposer node, packaging the transaction data to generate a new block, and distributing the new block to all the blockchain nodes so that the blockchain nodes update their respective underlying ledgers according to the new blocks broadcasted by the proposer. The system of the present invention not only realizes such functions as restoration and editing of the transaction data, but also protects operational security and reliability of blockchains.

Anti-trapdoor-leakage on-chain data restoration system and method thereof

The present invention relates a device for improving robustness of deep-learning based detection of source-code vulnerability, the device at least comprises a code-converting module, a mapping module, and a neural-network module, wherein the mapping module is in data connection with the code-converting module, the mapping module is in data connection with the neural-network module, respectively, and the neural-network module includes at least two first classifiers, based on a received first training program source code, the mapping module maps a plurality of code snippets, and the neural-network module trains the at least two first classifiers according to a first sample vector. The present invention improves the robustness of detection of source-code vulnerability by performing classification training on the feature generators and the classifiers.

Deep-learning based device and method for detecting source-code vulnerability with improved robustness

The present disclosure involves a sidechain testing system and method for improving security and stability of a smart contract.

Sidechain testing system and method for improving security and stability of a smart contract by generating sidechains having mirror image accounts of part or all mainchain assets

The present invention provides a TrustZone-based security isolation system for shared library, the system at least comprising: a sandbox creator, a library controller, and an interceptor, the sandbox creator, in a normal world, dynamically creating a sandbox isolated from a Rich OS, the interceptor, intercepting corresponding system-calling information and/or Android framework APIs by means of inter-process stack inspection, the library controller, performing analysis based on the intercepted system-calling information and/or Android framework APIs, redirecting a library function to the sandbox, and switching calling states of the library function in the sandbox as well as setting up a library authority. The present invention has good versatility, low cost and high security. It realizes isolation of the library without increasing the trusted bases in the Secure World of the TrustZone, effectively reducing the risk of being attacked.

TrustZone-based security isolation method for shared library and system thereof

The present invention involves with a cloud tenant oriented method and system for protecting privacy data. The method comprises at least the following steps: analyzing event handler information and/or behavioral signature information of request information and determining an execution mode, selecting at least one node without a behavioral signature plot to execute the tenant request and recording an execution result, generating a behavioral signature plot based on the execution result, and dynamically detecting security-sensitive behavior based on the behavioral signature plot. The present invention ensures data security during processing of security-sensitive data for cloud services by adopting a technology based on behavioral signatures, and prevents attackers from exploiting vulnerabilities and bypassing security control to conduct malicious operations.

Cloud tenant oriented method and system for protecting privacy data

The present invention involves with a method of searchable public-key encryption, a system and server using the method.

Method of searchable public-key encryption and system and server using the same

To provide access to USB devices coupled to a client computing device for a user of virtual machine executing on a server, the server, in some examples, may be configured to pack USB request blocks into IP data packets and transmit the IP data packets over a network. Responsive to receiving the IP data packets, the client computing device may extract the USB request blocks from the IP data packets and provide the access to the USB devices for the virtual machine user.

Universal serial bus (USB) device access from one or more virtual machines

In at least one embodiment, a method for secured rollback of a virtual Trusted Platform Module (vTPM) that renders available functionalities offered by Trusted Platform Module (TPM) on a computer to a virtual machine (VM) is provided. The method includes taking and saving a snapshot of current state of a virtual machine as well as a snapshot of the corresponding vTPM, upon receiving a request for taking a snapshot of the virtual machine. The method further includes converting the snapshots into the current state of the virtual machine and the current state of the vTPM, respectively, upon receiving a request to roll back the virtual machine.

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Hubei, China

Deqing Zou