Ke Lin

Tsinghua University

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Co-Authors

Xiaorong Gao

Tsinghua University

Shilong Jiang

Hong Kong University of Science and Technology

Zhiyong Li

Inner Mongolia University

Mourad Nouioua

King Fahd University of Petroleum and Minerals

Shangkai Gao

Tsinghua University

Te Sun

Changchun University

Yijun Wang

University of Chinese Academy of Sciences

Xiaoshan Huang

Imperial College London

Xi Wu Wang

Ordnance Engineering College

Chaohua Wu

University of Wollongong

Cooperative Institutions

Shanghai Jiao Tong University

Chinese Academy of Sciences

Tsinghua University

University of Chinese Academy of Sciences

University of Science and Technology of China

Harbin Institute of Technology

Fudan University

Ordnance Engineering College

Huazhong University of Science and Technology

Beijing Academy of Quantum Information Sciences

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AyatDroid: A Lightweight Code Cloning Technique Using Different Static Features

Yasir Glani Ping Luo Ke Lin Syed Asad Shah

In recent decades, malicious code reuse has surged in numbers and sophistication, it is a common practice among adversaries to reuse malicious code, which significantly threatens user privacy and security. Several signature-based code clone detection techniques have been proposed to detect malicious clones in Android applications that use the MD5 hash function to generate signatures. Meanwhile, these techniques only retrieve signatures from Java files. Due to the 128-bit signature size of the MD5 hash function, these techniques take longer to generate signatures. In this article, we propose the AyatDroid technique, which efficiently identifies malicious chunks by retrieving signatures from Java and manifest files . AyatDroid technique is tested on reliable CiCMalDroid 2020 dataset. We have evaluated the AyatDroid technique with other cutting-edge code clone detection techniques. Our experimental results demonstrated that AyatDroid outperformed regarding detection time and accuracy. AyatDroid is not only lightweight but also efficient, allowing it to be implemented on the large scale.

Code reuse

Code (set theory)

MD5

Signature (topology)

10.1109/seai59139.2023.10217577

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Study on the Intellectual Property and its Protect Strategy in the Electronic Commerce

Ke Lin

With the development of the global information network, electronic commerce has becoming an overwhelming flood. Developing electronic commerce will not only relate to the growing of national economy, but also affect the life of social public directly. It also involve a series of issues, such as the developing of national policy, law, information technology and the construction of infrastructure, and etc, a series of issues. Electronic commerce is a digital trade movement and intellectual property is the major component during the trade. The authors analyze the tortuous phenomenon which happen in electronic commerce, such as infringe copyright, infringe trademark right ,infringe patent right, infringe the business secrets and cyberpiracy and use some examples to explain it. Basing on several aspects such as law, technic, economy moral, the authors discuss the problems that how to protect the intellectual property and give some advices.

Digital Economy

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MDASC: Advanced Dual-Layered Code Cloning Technique for Identifying Reused Malicious Code

Proceedings/Proceedings of the ... International Conference on Software Engineering and Knowledge Engineering (2024)

Yasir Glani Paul Lo Ke Lin

Cloning (programming)

Code (set theory)

10.18293/seke2024-130

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An online hybrid BCI system based on SSVEP and EMG

Journal of Neural Engineering (2016)

Ke Lin Andrea Cinetto Yijun Wang Xiaogang Chen Shangkai Gao

Objective. A hybrid brain-computer interface (BCI) is a device combined with at least one other communication system that takes advantage of both parts to build a link between humans and machines. To increase the number of targets and the information transfer rate (ITR), electromyogram (EMG) and steady-state visual evoked potential (SSVEP) were combined to implement a hybrid BCI. A multi-choice selection method based on EMG was developed to enhance the system performance. Approach. A 60-target hybrid BCI speller was built in this study. A single trial was divided into two stages: a stimulation stage and an output selection stage. In the stimulation stage, SSVEP and EMG were used together. Every stimulus flickered at its given frequency to elicit SSVEP. All of the stimuli were divided equally into four sections with the same frequency set. The frequency of each stimulus in a section was different. SSVEPs were used to discriminate targets in the same section. Different sections were classified using EMG signals from the forearm. Subjects were asked to make different number of fists according to the target section. Canonical Correlation Analysis (CCA) and mean filtering was used to classify SSVEP and EMG separately. In the output selection stage, the top two optimal choices were given. The first choice with the highest probability of an accurate classification was the default output of the system. Subjects were required to make a fist to select the second choice only if the second choice was correct. Main results. The online results obtained from ten subjects showed that the mean accurate classification rate and ITR were 81.0% and 83.6 bits min−1 respectively only using the first choice selection. The ITR of the hybrid system was significantly higher than the ITR of any of the two single modalities (EMG: 30.7 bits min−1, SSVEP: 60.2 bits min−1). After the addition of the second choice selection and the correction task, the accurate classification rate and ITR was enhanced to 85.8% and 90.9 bit min−1. Significance. These results suggest that the hybrid system proposed here is suitable for practical use.

10.1088/1741-2560/13/2/026020

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Citations (67)

Assessing seawater quality with a variable fuzzy recognition model

Ke Lin

With the rapid development of the marine economy industry, human exploitation of marine resources is increasing, which is contributing to the growing trend of eutrophication and frequent occurrence of red tide. Accordingly, investigations of seawater quality have attracted a great deal of attention. This study was conducted to construct a seawater environmental quality assessment model based on the variable fuzzy recognition model. The uncertainty and ambiguity of the seawater quality assessment were then considered, combining the monitoring values of evaluation indicators with the standard values of seawater quality. Laizhou Bay was subsequently selected for a case study. In this study, the correct variable model for different parameters was obtained according to the linear and nonlinear features of evaluation objects. Application of the variable fuzzy recognition model for Laizhou Bay, water quality evaluation and comparison with performance obtained using other approaches revealed that the generated model is more reliable than traditional methods, can more reasonably determine the water quality of various samples, and is more suitable for evaluation of a multi-index, multi-level, nonlinear marine environment system; accordingly, the generated model will be an effective tool for seawater quality evaluation.

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A Novel Algorithm for Learning Sparse Spatio-Spectral Patterns for Event-Related Potentials

IEEE Transactions on Neural Networks and Learning Systems (2016)

Chaohua Wu Ke Lin Wei Wu Xiaorong Gao

Recent years have witnessed brain-computer interface (BCI) as a promising technology for integrating human intelligence and machine intelligence. Currently, event-related potential (ERP)-based BCI is an important branch of noninvasive electroencephalogram (EEG)-based BCIs. Extracting ERPs from a limited number of trials remains challenging due to their low signal-to-noise ratio (SNR) and low spatial resolution caused by volume conduction. In this paper, we propose a probabilistic model for trial-by-trial concatenated EEG, in which the concatenated ERPs are expressed as a linear combination of a set of discrete sine and cosine bases. The bases are simply determined by the data length of a single trial. A sparse prior on the rank of the spatio-spectral pattern matrix is introduced into the model to allow the number of components to be automatically determined. A maximum posterior estimation algorithm based on cyclic descent is then developed to estimate the spatiospectral patterns. A spatial filter can then be obtained by maximizing the SNR of the ERP components. Experiments on both synthetic data and real N170 ERP from 13 subjects were conducted to test the efficacy and efficiency of the algorithm. The results showed that the proposed algorithm can estimate the ERPs more accurately than the several state-of-the-art algorithms.

Data set

10.1109/tnnls.2015.2496284

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Citations (14)

Demonstration Guided Actor-Critic Deep Reinforcement Learning for Fast Teaching of Robots in Dynamic Environments

IFAC-PapersOnLine (2020)

Liang Gong Te Sun Xudong Li Ke Lin Natalia Díaz-Rodríguez

Using direct reinforcement learning (RL) to accomplish a task can be very inefficient, especially in robotic configurations where interactions with the environment are lengthy and costly. Instead, learning from expert demonstration (LfD) is an alternative approach to gain better performance in an RL setting, which also greatly improves sample efficiency. We propose a novel demonstration learning framework for actor-critic based algorithms. Firstly, we put forward an environment pre-training paradigm to initialize the model parameters without interacting with the target environment, which effectively avoids the cold start problem in deep RL scenarios. Secondly, we design a general-purpose LfD framework for most of the mainstream actor-critic RL algorithms that include a policy network and a value function like PPO, SAC, TRPO, A3C. Thirdly, we build a dedicated model training platform to perform the human-robot interaction and numerical experimentation. We evaluate the method in six Mujoco simulated locomotion environments and our robot control simulation platform. Results show that several epochs of pre-training can improve the agent’s performance over the early stage of training. Also, the final converged performance of the RL algorithm is also boosted by external demonstration. In general the sample efficiency is improved by 30% with the proposed method. Our demonstration pipeline makes full use of the exploration property of the RL algorithm, which is feasible for fast teaching robots in dynamic environments.

Sample (material)

10.1016/j.ifacol.2021.04.227

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Citations (3)

.NET realization of rural financial risk early warning platform based on BP neural network

2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA) (2021)

Kel Chang Huang Jielu Ke Lin

In recent years, my country's rural financial system reform has made great progress, and rural finance has played an important role in promoting the healthy development of the national economy, especially the development of the rural economy. However, the overall risks of rural finance, especially business risks, are still very prominent, which has become a major obstacle to the smooth progress of the construction of a new countryside. This article comprehensively adopts a variety of methods such as causal analysis, system analysis, empirical analysis and model analysis, and applies BP neural network to rural financial operation risk early warning, and completes the .NET implementation of rural financial risk early warning.

Early warning system

Rural Economy

Realization (probability)

10.1109/icirca51532.2021.9559626

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A Dynamic Multimodal Differential Evolution Algorithm

Xiaogang Dong Qing Xie Ke Lin

t-Differential evolution algorithm in solving complex function optimization problems, the problems of convergence rate and precision is not high.At the same time, there is a big difference in the performance of evolutionary algorithms for solving the different types of optimization problems.To solve above two problems, this paper proposes a dynamic multimodal differential evolution algorithm.Firstly, the dynamically population is used to improve the exploration ability of algorithms; In addition, the algorithm uses Four different types of mutation operator to Produce among individuals, choose the best among individuals to enter the next iteration , improved the algorithms's performance of solving different types of optimization problems.Through a variety of BenchMark functions to the algorithm simulation experiment, and comparing and several other classical differential evolution algorithm, show that this algorithm has better optimization performance.

Differential Evolution

10.2991/ccit-14.2014.101

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Design of a Gas Detection System Based on BP Neural Network

Applied Mechanics and Materials (2011)

Yin Long Wang Ke Lin Xi Wu Wang Zhi Geng Qi Gen Zhong

On the basis of the brief overview of principles of the gas detection system, this paper has analyzed the characteristics, structure and identification theory to explain the method of gas detection based on an artificial neural network. And it has analyzed and researched gas detection system based on neural network and thus solved the problems such as cross-sensitiveness in present gas sensor. The results show that the gas sensor array "cross-sensitive" issue can be effectively solved through the combination of the pattern recognition of artificial neural network and the gas sensor array technology, which accordingly realizes qualitative identification for different gases and has broad application prospects.

Identification

Sensor array

10.4028/www.scientific.net/amm.55-57.1819

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Citations (2)