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International Conference on Mobile Ad-hoc and Sensor networks, MSN

作者： Muhammad Aslam Dengpan Ye Muhammad Hanif Muhammad Asad Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cybersecurity and Engineering Wuhan University Wuhan China COMSATS University Islamabad Wah Campus Wahcantt Pakistan Nagoya Institute of Technology Nagoya Japan

ISBN: (数字)9781728199160

ISBN: (纸本)9781728192369

Applications of Machine Learning (ML) algorithms in cybersecurity provide significant performance enhancement over traditional rule-based algorithms. These intelligent cyber-security solutions demand careful integration of the learning algorithms to develop a significant cyber incident detection system to formulate security analysts' industrial level. The development of advanced malware programs poses a critical threat to cybersecurity systems. Hence, an efficient, robust, and scalable malware recognition module is essential for every cybersecurity product. Conventional Signature-based methods struggle in terms of robustness and effectiveness during malware detection, specifically in the case of zero-day and polymorphic viruses attacks. In this paper, we design an adaptive Machine Learning based active malware detection framework which provides a cybersecurity solution against phishing attacks. The proposed framework utilize ML algorithms in a multilayered feed-forwarding approach to successfully detect the malware by examining the static features of the web pages. The proposed framework successfully extracts the features from the web pages and performs a successful detection process for the phishing attack. In the multilayered feed-forwarding framework, the first layer utilizes Random Forest (RF), Support Vector Machine (SVN), and K-Nearest Neighbor (K-NN) classifiers to build a model for detecting malware from the real-time input. The output of the first layer passes to the Ensemble Voting (EV) algorithm, which accumulates earlier classifiers' performance. At the third layer, adaptive frameworks investigate second layer input data and formulate the phishing detection model. We analyze the proposed framework's performance on three different phishing datasets and validate the higher accuracy rate.

关键词： Adaptation models Machine learning algorithms Adaptive systems Phishing Web pages Feature extraction Malware

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How Does Knowledge Graph Embedding Extrapolate to Unseen Data: A Semantic Evidence View

arXiv

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arXiv 2021年

作者： Li, Ren Cao, Yanan Zhu, Qiannan Bi, Guanqun Fang, Fang Liu, Yi Li, Qian Institute of Information Engineering Chinese Academy of Sciences China School of Cyber Security University of Chinese Academy of Sciences China Gaoling School of Artificial Intelligence Renmin University of China China Beijing Key Laboratory of Big Data Management and Analysis Methods China National Computer Network Emergency Response Technical Team Coordination Center of China China University of Technology Sydney Australia

Knowledge Graph Embedding (KGE) aims to learn representations for entities and relations. Most KGE models have gained great success, especially on extrapolation scenarios. Specifically, given an unseen triple (h, r, t), a trained model can still correctly predict t from (h, r,?), or h from (?, r, t), such extrapolation ability is impressive. However, most existing KGE works focus on the design of delicate triple modeling function, which mainly tells us how to measure the plausibility of observed triples, but offers limited explanation of why the methods can extrapolate to unseen data, and what are the important factors to help KGE extrapolate. Therefore in this work, we attempt to study the KGE extrapolation of two problems: 1. How does KGE extrapolate to unseen data? 2. How to design the KGE model with better extrapolation ability? For the problem 1, we first discuss the impact factors for extrapolation and from relation, entity and triple level respectively, propose three Semantic Evidences (SEs), which can be observed from train set and provide important semantic information for extrapolation. Then we verify the effectiveness of SEs through extensive experiments on several typical KGE methods. For the problem 2, to make better use of the three levels of SE, we propose a novel GNN-based KGE model, called Semantic Evidence aware Graph Neural network (SE-GNN). In SE-GNN, each level of SE is modeled explicitly by the corresponding neighbor pattern, and merged sufficiently by the multi-layer aggregation, which contributes to obtaining more extrapolative knowledge representation. Finally, through extensive experiments on FB15k-237 and WN18RR datasets, we show that SE-GNN achieves state-of-the-art performance on Knowledge Graph Completion task and performs a better extrapolation ability. Our code is available at https://***/renli1024/SE-GNN. Copyright © 2021, The Authors. All rights reserved.

关键词： Knowledge graph

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Simulation and Optimization of Temperature Field in Large-Sized MOCVD Reactor by Resistance Heating 4

Simulation and Optimization of Temperature Field in Large-Si...

引用

4th International Conference on Manufacturing, Material and Metallurgical Engineering, ICMMME 2019

作者： Zhao, Lili Li, Zhiming Guo, Runqiu Feng, Lansheng Lu, Ligen Fu, Xiaoqian Shandong Provincial Key Laboratory of Network Based Intelligent Computing School of Information Science and Engineering University of Jinan Jinan Shandong250022 China State Key Discipline Laboratory of Wide Band-Gap Semiconductor Technology School of Microelectronics Xidian University Xi'an Shanxi710071 China

The effect of the number of layers and the length of the heat shields on the heating efficiency and temperature distributions in the substrate have been studied by establishing the model of a single-piece 18-inch MOCVD reactor. The results show that the number of layers of the heat shields is directly proportional to the heating efficiency, decreasing the length of the thermal shield can reduce the standard deviation(STD) of the substrate temperature. When the length of the heat shields is 56mm, the coefficient of substrate temperature STD is 21.41 °C and the STD is about 45% lower than the traditional susceptor. An area within substrate radius of 200mm, the coefficient of substrate temperature STD is 2.64 °C and the STD is about 93% lower than the traditional susceptor. The results obtained will provide theoretical basis for developing the heating structure of large-sized MOCVD reactor. © Published under licence by IOP Publishing Ltd.

关键词： Heat shielding

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OpenClinicalAI: enabling AI to diagnose diseases in real-world clinical settings

arXiv

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arXiv 2021年

作者： Huang, Yunyou Wang, Nana Tang, Suqin Ma, Li Hao, Tianshu Jiang, Zihan Zhang, Fan Kang, Guoxin Miao, Xiuxia Guan, Xianglong Zhang, Ruchang Zhang, Zhifei Zhan, Jianfeng Guangxi Key Lab of Multi-Source Information Mining & Security School of Computer Science and Engineering School of Software Guangxi Normal University Guilin China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China Guilin Medical University Guilin China Department of Physiology and Pathophysiology Capital Medical University Beijing China University of Chinese Academy of Sciences China International Open Benchmark Council

This paper quantitatively reveals the state-of-the-art and state-of-the-practice AI systems only achieve acceptable performance on the stringent conditions that all categories of subjects are known, which we call closed clinical settings, but fail to work in real-world clinical settings. Compared to the diagnosis task in the closed setting, real-world clinical settings pose severe challenges, and we must treat them differently. We build a clinical AI benchmark named Clinical AIBench to set up real-world clinical settings to facilitate researches. We propose an open, dynamic machine learning framework and develop an AI system named OpenClinicalAI to diagnose diseases in real-world clinical settings. The first versions of Clinical AIBench and OpenClinicalAI target Alzheimer’s disease. In the real-world clinical setting, OpenClinicalAI significantly outperforms the state-of-the-art AI system. In addition, OpenClinicalAI develops personalized diagnosis strategies to avoid unnecessary testing and seamlessly collaborates with clinicians. It is promising to be embedded in the current medical systems to improve medical services. © 2021, CC BY.

关键词： Diagnosis

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Robust Visual Tracking with Hierarchical Deep Features Weighted Fusion

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Journal of Beijing Institute of technology 2019年第4期28卷 770-776页

作者： Dianwei Wang Chunxiang Xu Daxiang Li Ying Liu Zhijie Xu Jing Wang School of Telecommunication and Information Engineering Xi'an University of Posts and TelecommunicationsXi'an 710121China Key Laboratory of Electronic Information Application Technology for Scene Investigation Ministry of Public SecurityPeople's Republic of ChinaXi'an 710121China School of Computing and Engineering University of HuddersfieldHuddersfieldHD13DHUK

To solve the problem of low robustness of trackers under significant appearance changes in complex background,a novel moving target tracking method based on hierarchical deep features weighted fusion and correlation filter is ***,multi-layer features are extracted by a deep model pre-trained on massive object recognition *** linearly separable features of Relu3-1,Relu4-1 and Relu5-4 layers from VGG-Net-19 are especially suitable for target ***,correlation filters over hierarchical convolutional features are learned to generate their correlation response ***,a novel approach of weight adjustment is presented to fuse response *** maximum value of the final response map is just the location of the *** experiments on the object tracking benchmark datasets demonstrate the high robustness and recognition precision compared with several state-of-the-art trackers under the different conditions.

关键词： visual tracking convolution neural network correlation filter feature fusion

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RRNet: Relational reasoning network with parallel multi-scale attention for salient object detection in optical remote sensing images

arXiv

引用

arXiv 2021年

作者： Cong, Runmin Zhang, Yumo Fang, Leyuan Li, Jun Zhang, Chunjie Zhao, Yao Kwong, Sam Institute of Information Science Beijing Jiaotong University Beijing100044 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China Department of Computer Science City University of Hong Kong Hong Kong College of Electrical and Information Engineering Hunan University Changsha410082 China Peng Cheng Laboratory Shenzhen518000 China Guangdong Provincial Key Laboratory of Urbanization and Geosimulation School of Geography and Planning Sun Yat-sen University Guangzhou510275 China Key Laboratory of Information Perception and Systems for Public Security of MIIT Nanjing University of Science and Technology Nanjing210094 China Department of Computer Science City University of Hong Kong Hong Kong City University of Hong Kong Shenzhen Research Institute Shenzhen51800 China

Salient object detection (SOD) for optical remote sensing images (RSIs) aims at locating and extracting visually distinctive objects/regions from the optical RSIs. Despite some saliency models were proposed to solve the intrinsic problem of optical RSIs (such as complex background and scale-variant objects), the accuracy and completeness are still unsatisfactory. To this end, we propose a relational reasoning network with parallel multi-scale attention for SOD in optical RSIs in this paper. The relational reasoning module that integrates the spatial and the channel dimensions is designed to infer the semantic relationship by utilizing high-level encoder features, thereby promoting the generation of more complete detection results. The parallel multi-scale attention module is proposed to effectively restore the detail information and address the scale variation of salient objects by using the low-level features refined by multi-scale attention. Extensive experiments on two datasets demonstrate that our proposed RRNet outperforms the existing state-of-the-art SOD competitors both qualitatively and quantitatively. https://***/proj_***. © 2021, CC BY-NC-SA.

关键词： Object detection

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A Format Protection Method of Greybox Fuzzing 19

A Format Protection Method of Greybox Fuzzing

引用

19th IEEE International Conference on Communication technology, ICCT 2019

作者： Zhao, Shibin Zhou, Tianyang Duan, Xiaoli Lin, Hongyang Ying, Xinlei Shi, Qibo Peng, Jianshan State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China China United Network Communications Group Co. Ltd Shanxi Yuncheng Branch Yuncheng044000 China Ant-financial Light-Year Security Lab hangzhou310000 China

ISBN: (纸本)9781728105352

Greybox fuzzing, which can fuzz without knowledge of the format, is one of the most advanced fuzzing techniques. However, the lack of format knowledge makes the test cases' format messy as the fuzzing proceeds and reduces the effectiveness of fuzzing. In this paper, we introduce a format protection method that can protect the format of test cases during greybox fuzzing. Based on prior knowledge, the Naive Bayes classifier is applied to achieve a format check method, which is used to improve the favourite seed selection process and induce the position of variation. The experiment results indicate that this method not only has a higher code coverage ratio and more unique crashes in less time than other fuzzers but also maintains the invalid format ratio at a low level. 24 previously unreported CVEs were exposed. © 2019 IEEE.

关键词： Classifiers

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Lightweight Task-Oriented Semantic Communication Empowered by Large-Scale AI Models

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IEEE Transactions on Vehicular technology 2025年

作者： Liu, Chuanhong Guo, Caili Yang, Yang Chen, Mingzhe Quek, Tony Q. S. Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing100876 China University of Miami Department of Electrical and Computer Engineering Institute for Data Science and Computing Coral GablesFL United States Singapore University of Technology and Design Dept. of Information Systems Technology and Design 487372 Singapore

Recent studies have focused on leveraging large-scale artificial intelligence (LAI) models to improve semantic representation and compression capabilities. However, the substantial computational demands of LAI models pose significant challenges for real-time communication scenarios. To address this, this paper proposes utilizing knowledge distillation (KD) techniques to extract and condense knowledge from LAI models, effectively reducing model complexity and computation latency. Nevertheless, the inherent complexity of LAI models leads to prolonged inference times during distillation, while their lack of channel awareness compromises the distillation performance. These limitations make standard KD methods unsuitable for task-oriented semantic communication scenarios. To address these issues, we propose a fast distillation method featuring a pre-stored compression mechanism that eliminates the need for repetitive inference, significantly improving efficiency. Furthermore, a channel adaptive module is incorporated to dynamically adjust the transmitted semantic information based on varying channel conditions, enhancing communication reliability and adaptability. In addition, an information bottleneck-based loss function is derived to guide the fast distillation process. Simulation results verify that the proposed scheme outperform baselines in term of task accuracy, model size, computation latency, and training data requirements. © 1967-2012 IEEE.

关键词： NP-hard

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Task-feature collaborative learning with application to personalized attribute prediction

arXiv

引用

arXiv 2020年

作者： Yang, Zhiyong Xu, Qianqian Cao, Xiaochun Huang, Qingming State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Cyberspace Security Research Center Peng Cheng Laboratory Shenzhen518055 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China University of Chinese Academy of Sciences Beijing101408 Peng Cheng Laboratory Shenzhen518055 China

As an effective learning paradigm against insufficient training samples, Multi-Task Learning (MTL) encourages knowledge sharing across multiple related tasks so as to improve the overall performance. In MTL, a major challenge springs from the phenomenon that sharing the knowledge with dissimilar and hard tasks, known as negative transfer, often results in a worsened performance. Though a substantial amount of studies have been carried out against the negative transfer, most of the existing methods only model the transfer relationship as task correlations, with the transfer across features and tasks left unconsidered. Different from the existing methods, our goal is to alleviate negative transfer collaboratively across features and tasks. To this end, we propose a novel multi-task learning method called Task-Feature Collaborative Learning (TFCL). Specifically, we first propose a base model with a heterogeneous block-diagonal structure regularizer to leverage the collaborative grouping of features and tasks and suppressing inter-group knowledge sharing. We then propose an optimization method for the model. Extensive theoretical analysis shows that our proposed method has the following benefits: (a) it enjoys the global convergence property and (b) it provides a block-diagonal structure recovery guarantee. As a practical extension, we extend the base model by allowing overlapping features and differentiating the hard tasks. We further apply it to the personalized attribute prediction problem with fine-grained modeling of user behaviors. Finally, experimental results on both simulated dataset and real-world datasets demonstrate the effectiveness of our proposed method. Copyright © 2020, The Authors. All rights reserved.

关键词： Knowledge management

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Image recognition and detection based on fast area convolutional neural network

引用

Journal of Physics: Conference Series 2021年第1期1976卷

作者： Hanwen Zhang Zhen Qin Hua Xie School of Automation Engineering UESTC Chengdu China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China

Nowadays, image recognition and detection technologies based on traditional artificial neural networks and convolutional neural networks are slightly inadequate in terms of training and recognition time and accuracy, and are difficult to deploy on devices with limited hardware resources. Therefore, this article proposes a recognition and detection technology based on fast regional convolutional neural networks. We use RPN (Region Proposal network) instead of Selective Search method to rebuild the network, and add a new ROI pooling layer before the fully connected layer of CNN. Determine the category. The average detection accuracy on our data set can reach 83.8%, and the training time is only 0.34 hours.

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