Jinan University

Other

Weiqi Luo

Ming Li

Yue Zhang

Jian Weng

Jiasi Weng

Huanming Zhang

Lin Cui

Zhefu Li

Quanlong Guan

Yongdong Wu

Anjia Yang

Yuanfen Wu

Jilian Zhang

Chuying Liu

Zhijian Shao

Rongjun Li

Disclosed is a description-entropy-based intelligent detection method for a big data mobile software similarity. The method comprises the following steps: acquiring a path of mobile software, and reading a file of the mobile software according to the path; performing preliminary reverse engineering decompilation on the file of the mobile software to obtain function characteristics of each piece of mobile software; counting distribution of description entropy of each piece of mobile software by means of description entropy in the function characteristics; further integrating description entropy of each piece of mobile software, after integration, comparing description entropy distribution conditions among the mobile software, and carrying out similarity score calculation to obtain similarity scores among the mobile software; and outputting the similarity scores of all mobile software to obtain a mobile software similarity result. According to the method, a source code of the mobile software is acquired by means of decompilation, a function compression code is acquired, and then the description entropy is acquired; and the description entropy is used as an information amount for representing an object and used for similarity detection of the mobile software, thus greatly increasing the speed of intelligent calculation of software similarity.

Description-entropy-based intelligent detection method for big data mobile software similarity

Disclosed is a method for constructing an auditable and privacy-preserving collaborative deep learning platform based on a blockchain-empowered incentive mechanism, which allows trainers of multiple similar models to cooperate for training deep learning models while protecting confidentiality and auditing correctness of shared parameters. The invention has the following technical effects. Firstly, the encryption method used by model trainers protects the confidentiality of sharing parameters; furthermore, the updated parameters are decrypted through the cooperation of all participants, which reduces the possible disclosure of parameters. Secondly, the encrypted parameters are stored in the blockchain, and are only available to participants and authorized miners who are responsible to update parameters. Thirdly, the blockchain-based incentive mechanism guarantees the validity of the parameters, where collaborative trainers need to pay deposit when uploading parameters at the beginning and then the shared parameters can be validated. Concretely, if the parameters are invalid, the deposit would be forfeited.

Auditable privacy protection deep learning platform construction method based on block chain incentive mechanism

With the popularity of Blockchain comes grave security-related concerns. Achieving privacy and traceability simultaneously remains an open question. Efforts have been made to address the issues, while they may subject to specific scenarios. The present disclosure proposes a method, a system, and a device for maintaining privacy and traceability of a Blockchain-based system. The method includes: registering in a Blockchain-based system; transmitting a message from a transmitter T to a receiver R via an anonymous network, after a registration request of the transmitter T has been approved and storing message transmission data generated during a message transmission process in a Blockchain; and performing an identity disclosure by using the message transmission data when the message is detected to be false. The anonymous network may transmit the message by onion routing. The solution proposed by the present disclosure can achieve traceability and privacy for a Blockchain-based system simultaneously.

Method and system for maintaining privacy and traceability of blockchain-based system

A method for detecting mobile malware, including step S: compressing a mobile software subject to detection and a primary mobile software each containing N functions, wherein each of the functions is compressed into a hash value, a description entropy and a compressed string; S: calculating a quantity of identical functions between the mobile software subject to detection and the primary mobile software; and then finding out a quantity of similar functions using Hdsim method or entropy_descpt_sim method; and then calculating a degree of similarity to obtain a value of similarity; S: comparing the value of similarity with a predetermined threshold value; if the value of similarity is greater then or equal to the predetermined threshold value, the mobile software subject to detection is suspected to repackaging; if the value of similarity is smaller than the predetermined threshold value, the mobile software subject to detection is not suspected to repackaging.

Method for fast and intelligent comparison and security detection of mobile malware big data

A method for pairing a plurality of devices in the context of internet of things (IoT), including: connecting power supplies of the plurality of devices to a same power source, wherein the plurality of devices includes an initiator device and a responder device; synchronizing clocks of the initiator device and the responder device; switching the power source off and on for a plurality of times n; recording time stamps S(T)={T1, T2, T3, . . . T} corresponding to time stamps when the initiator device and the responder device switch off in response to switching off of the power source; and performing the pairing of the initiator device and the responder device if each of the initiator device time stamps is within a predefined delay tolerance of a corresponding time stamp of the responder device time stamps, otherwise rejecting the pairing of the initiator device and the responder device.

Peripheral-free secure pairing protocol by randomly switching power

A method of digital forensics based on blockchain technology and a fine-grained access control scheme, using a finite state machine (FSM) based on smart contracts to manage a digital certificate, where a law enforcement agency ends the warrant request to a court and upon approval of the request collects the forensics data by obtaining the master secret key for accessing the forensic data from a plurality of authorities. The forensics data are encrypted by D-KP-ABE (Distributed Key Police Attribute-based Encryption) with privacy-preserved access policy. The secret sharings are required to form the decryption key for accessing the forensics data. The secret sharings are distributed among the plurality of authorized authorities so that no individual authority has a complete master key by itself. Each state of the FSM requires digital signature(s) of at least one specific authorized authority for transitioning to a next state.

Method and system for secure blockchain-based vehicular digital forensics

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Guangdong, China

Weiqi Luo