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A review on cyber security named entity recognition Review Article

Chen Gao, Xuan Zhang, Mengting Han, Hui Liu,zhxuan@ynu.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 9,   Pages 1153-1168 doi: 10.1631/FITEE.2000286

Abstract: This paper describes various approaches and techniques for NER in this domain, including the rule-basedapproach, dictionary-based approach, and based approach, and discusses the problems faced by NER researchThree future directions of NER in are proposed: (1) application of unsupervised or semi-supervised technology

Keywords： 命名实体识别（NER）；信息抽取；网络空间安全；机器学习；深度学习    

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Learning to select pseudo labels: a semi-supervised method for named entity recognition Research Articles

Zhen-zhen Li, Da-wei Feng, Dong-sheng Li, Xi-cheng Lu,lizhenzhen14@nudt.edu.cn,davyfeng.c@gmail.com,dsli@nudt.edu.cn,xclu@nudt.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 6,   Pages 809-962 doi: 10.1631/FITEE.1800743

Abstract: models have achieved state-of-the-art performance in (NER); the good performance, however, relies heavilyIn this study, we propose a for NER tasks, which learns to create high-quality labeled data by applyingtask, learning a module that evaluates pseudo labels, and creating new labeled data and improving the NERExperimental results on two English NER tasks and one Chinese clinical NER task demonstrate that ourcomparable performance to those state-of-the-art models on the CoNLL-2003 and OntoNotes 5.0 English NER

Keywords： 命名实体识别；无标注数据；深度学习；半监督学习方法    

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Disambiguating named entitieswith deep supervised learning via crowd labels Article

Le-kui ZHOU,Si-liang TANG,Jun XIAO,Fei WU,Yue-ting ZHUANG

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 1,   Pages 97-106 doi: 10.1631/FITEE.1601835

Abstract: Named entity disambiguation (NED) is the task of linking mentions of ambiguous entities to their referenced entities in a knowledge base such as Wikipedia. We propose an approach to effectively disentangle the discriminative features in the manner of collaborative utilization of collective wisdom (via human-labeled crowd labels) and deep learning (via human-generated data) for the NED task. In particular, we devise a crowd model to elicit the underlying features (crowd features) from crowd labels that indicate a matching candidate for each mention, and then use the crowd features to fine-tune a dynamic convolutional neural network (DCNN). The learned DCNN is employed to obtain deep crowd features to enhance traditional hand-crafted features for the NED task. The proposed method substantially benefits from the utilization of crowd knowledge (via crowd labels) into a generic deep learning for the NED task. Experimental analysis demonstrates that the proposed approach is superior to the traditional hand-crafted features when enough crowd labels are gathered.

Keywords： Named entity disambiguation     Crowdsourcing     Deep learning    

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Artificial intelligence algorithms for cyberspace security applications: a technological and status review Review

Jie CHEN, Dandan WU, Ruiyun XIE,chenjie1900@mail.nwpu.edu.cn,wudd@cetcsc.com

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 8,   Pages 1117-1142 doi: 10.1631/FITEE.2200314

Abstract: Three technical problems should be solved urgently in : the timeliness and accuracy of network attack detection, the credibility assessment and prediction of the security situation, and the effectiveness of security defense strategy optimization. algorithms have become the core means to increase the chance of security and improve the network attack and defense ability in the application of . Recently, the breakthrough and application of AI technology have provided a series of advanced approaches for further enhancing network defense ability. This work presents a comprehensive review of AI technology articles for applications, mainly from 2017 to 2022. The papers are selected from a variety of journals and conferences: 52.68% are from Elsevier, Springer, and IEEE journals and 25% are from international conferences. With a specific focus on the latest approaches in , , and some popular s, the characteristics of the algorithmic models, performance results, datasets, potential benefits, and limitations are analyzed, and some of the existing challenges are highlighted. This work is intended to provide technical guidance for researchers who would like to obtain the potential of AI technical methods for and to provide tips for the later resolution of specific issues, and a mastery of the current development trends of technology and application and hot issues in the field of network security. It also indicates certain existing challenges and gives directions for addressing them effectively.

Keywords： Artificial intelligence (AI)     Machine learning (ML)     Deep learning (DL)     Optimization algorithm     Hybrid algorithm     Cyberspace security    

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Study on the Development of China’s Cyberspace Security Industry

An Da, Liang Zhihao, Xu Shouren

Strategic Study of CAE 2016, Volume 18, Issue 4,   Pages 38-43 doi: 10.15302/J-SSCAE-2016.04.006

Abstract:

The paper summarizes the development situation and experiences of the Chinese cyberspace security industry during the 12th Five-Year Plan, analyzes its new development trends, and proposes policy suggestions for the industry development to provide a reference for cyberspace security industry during the 13th Five-Year Plan.

Keywords： cyberspace security     cyberspace security industry     independent and controllable     standards    

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Research on the International Strategy for National Cyberspace Security

Fang Binxing,Du Aning and Zhang Xi,Wang Zhongru

Strategic Study of CAE 2016, Volume 18, Issue 6,   Pages 13-16 doi: 10.15302/J-SSCAE-2016.06.003

Abstract:

Cyberspace security has been a crucial part in national security and is more and more important in the development of economy and society. Based on the current situation of international cyberspace security, this article analyzes the opportunities and challenges that China is confronted with, and study China's international cyberspace security strategy suitable for its own value and national interest. We then propose the objectives, principles and tasks of the strategy.

Keywords： cyberspace security     cyberspace governance     cyberspace strategy    

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一种易用的实体识别消歧系统评测框架 Article

辉 陈,宝刚 魏,一鸣 李,Yong-huai LIU,文浩 朱

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 2,   Pages 195-205 doi: 10.1631/FITEE.1500473

Abstract: 实体识别消歧是知识库扩充和信息抽取的重要技术之一。近些年该领域诞生了很多研究成果，提出了许多实体识别消歧系统。但由于缺乏对这些系统的完善评测对比，该领域依然处于良莠淆杂的状态。本文提出一个实体识别消歧系统的统一评测框架，用于公平地比较各个实体识别消歧系统的效果。该框架代码开源，可以采用新的系统、数据集、评测机制扩展。通过该框架评测实体系统，可以分析得到系统各个模块的优劣之处。本文分析对比了几个公开的实体识别消歧系统，并总结出了一些有用的结论。

Keywords： 实体识别消歧；评测框架；信息抽取    

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Cyberspace Endogenous Safety and Security Article

Jiangxing Wu

Engineering 2022, Volume 15, Issue 8,   Pages 179-185 doi: 10.1016/j.eng.2021.05.015

Abstract:

Uncertain security threats caused by vulnerabilities and backdoors are the most serious and difficult problem in cyberspace. This paper analyzes the philosophical and technical causes of the existence of so-called “dark functions” such as system vulnerabilities and backdoors, and points out that endogenous security problems cannot be completely eliminated at the theoretical and engineering levels; rather, it is necessary to develop or utilize the endogenous security functions of the system architecture itself. In addition, this paper gives a definition for and lists the main technical characteristics of endogenous safety and security in cyberspace, introduces endogenous security mechanisms and characteristics based on dynamic heterogeneous redundancy (DHR) architecture, and describes the theoretical implications of a coding channel based on DHR.

Keywords： Cyberspace endogenous security problem     Uncertain threat     Cyberspace endogenous safety and security     Relative right axiom     Dynamic heterogeneous redundant architecture    

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Adversarial Attacks and Defenses in Deep Learning Feature Article

Kui Ren, Tianhang Zheng, Zhan Qin, Xue Liu

Engineering 2020, Volume 6, Issue 3,   Pages 346-360 doi: 10.1016/j.eng.2019.12.012

Abstract:

With the rapid developments of artificial intelligence (AI) and deep learning (DL) techniques, it is critical
to ensure the security and robustness of the deployed algorithms. Recently, the security vulnerability of
DL algorithms to adversarial samples has been widely recognized. The fabricated samples can lead to various
misbehaviors of the DL models while being perceived as benign by humans. Successful implementations
of adversarial attacks in real physical-world scenarios further demonstrate their practicality.
Hence, adversarial attack and defense techniques have attracted increasing attention from both machine
learning and security communities and have become a hot research topic in recent years. In this paper,
we first introduce the theoretical foundations, algorithms, and applications of adversarial attack techniques.
We then describe a few research efforts on the defense techniques, which cover the broad frontier
in the field. Several open problems and challenges are subsequently discussed, which we hope will provoke
further research efforts in this critical area.

Keywords： Machine learning     Deep neural network Adversarial example     Adversarial attack     Adversarial defense    

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Emergency and Response for Cyberspace Security

Yu Quan,Yang Lifeng and Gao Guijun、Kou Ziming、Zhai Lidong

Strategic Study of CAE 2016, Volume 18, Issue 6,   Pages 79-82 doi: 10.15302/J-SSCAE-2016.06.016

Abstract:

Based on the current situation and main problems with cyberspace security in China, this paper proposes that cyberspace security should shift its focus from emergency to response. Some transformation strategies are proposed, including three aspects: network security-monitoring capacity, network security guarantee capacity, and talents construction capacity.

Keywords： cyberspace security     emergency for cyberspace security     response for cyberspace security     transformation strategy    

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A deep Q-learning network based active object detection model with a novel training algorithm for service robots Research Article

Shaopeng LIU, Guohui TIAN, Yongcheng CUI, Xuyang SHAO

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 11,   Pages 1673-1683 doi: 10.1631/FITEE.2200109

Abstract:

This paper focuses on the problem of (AOD). AOD is important for to complete tasks in the family environment, and leads robots to approach the target object by taking appropriate moving actions. Most of the current AOD methods are based on reinforcement learning with low training efficiency and testing accuracy. Therefore, an AOD model based on a (DQN) with a novel training algorithm is proposed in this paper. The DQN model is designed to fit the Q-values of various actions, and includes state space, feature extraction, and a multilayer perceptron. In contrast to existing research, a novel training algorithm based on memory is designed for the proposed DQN model to improve training efficiency and testing accuracy. In addition, a method of generating the end state is presented to judge when to stop the AOD task during the training process. Sufficient comparison experiments and ablation studies are performed based on an AOD dataset, proving that the presented method has better performance than the comparable methods and that the proposed training algorithm is more effective than the raw training algorithm.

Keywords： Active object detection     Deep Q-learning network     Training method     Service robots    

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Diffractive Deep Neural Networks at Visible Wavelengths Article

Hang Chen, Jianan Feng, Minwei Jiang, Yiqun Wang, Jie Lin, Jiubin Tan, Peng Jin

Engineering 2021, Volume 7, Issue 10,   Pages 1485-1493 doi: 10.1016/j.eng.2020.07.032

Abstract:

Optical deep learning based on diffractive optical elements offers unique advantages for parallel processing, computational speed, and power efficiency. One landmark method is the diffractive deep neural network (D2NN) based on three-dimensional printing technology operated in the terahertz spectral range. Since the terahertz bandwidth involves limited interparticle coupling and material losses, this paper
extends D2NN to visible wavelengths. A general theory including a revised formula is proposed to solve any contradictions between wavelength, neuron size, and fabrication limitations. A novel visible light D2NN classifier is used to recognize unchanged targets (handwritten digits ranging from 0 to 9) and targets that have been changed (i.e., targets that have been covered or altered) at a visible wavelength of 632.8 nm. The obtained experimental classification accuracy (84%) and numerical classification accuracy (91.57%) quantify the match between the theoretical design and fabricated system performance. The presented framework can be used to apply a D2NN to various practical applications and design other new applications.

Keywords： Optical computation     Optical neural networks     Deep learning     Optical machine learning     Diffractive deep neural networks    

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A network security entity recognition method based on feature template and CNN-BiLSTM-CRF Research Papers

Ya QIN, Guo-wei SHEN, Wen-bo ZHAO, Yan-ping CHEN, Miao YU, Xin JIN

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 6,   Pages 872-884 doi: 10.1631/FITEE.1800520

Abstract:

By network security threat intelligence analysis based on a security knowledge graph (SKG), multi-source threat intelligence data can be analyzed in a fine-grained manner. This has received extensive attention. It is difficult for traditional named entity recognition methods to identify mixed security entities in Chinese and English in the field of network security, and there are difficulties in accurately identifying network security entities because of insufficient features extracted. In this paper, we propose a novel FT-CNN-BiLSTM-CRF security entity recognition method based on a neural network CNN-BiLSTM-CRF model combined with a feature template (FT). The feature template is used to extract local context features, and a neural network model is used to automatically extract character features and text global features. Experimental results showed that our method can achieve an F-score of 86% on a large-scale network security dataset and outperforms other methods.

Keywords： Network security entity     Security knowledge graph (SKG)     Entity recognition     Feature template     Neural network    

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Tandem hiddenMarkovmodels using deep belief networks for offline handwriting recognition Article

Partha Pratim ROY, Guoqiang ZHONG, Mohamed CHERIET

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 7,   Pages 978-988 doi: 10.1631/FITEE.1600996

Abstract: Unconstrained offline handwriting recognition is a challenging task in the areas of document analysis and pattern recognition. In recent years, to sufficiently exploit the supervisory information hidden in document images, much effort has been made to integrate multi-layer perceptrons (MLPs) in either a hybrid or a tandem fashion into hidden Markov models (HMMs). However, due to the weak learnability of MLPs, the learnt features are not necessarily optimal for subsequent recognition tasks. In this paper, we propose a deep architecture-based tandem approach for unconstrained offline handwriting recognition. In the proposed model, deep belief networks are adopted to learn the compact representations of sequential data, while HMMs are applied for (sub-)word recognition. We evaluate the proposed model on two publicly available datasets, i.e., RIMES and IFN/ENIT, which are based on Latin and Arabic languages respectively, and one dataset collected by ourselves called Devanagari (an Indian script). Extensive experiments show the advantage of the proposed model, especially over the MLP-HMMs tandem approaches.

Keywords： Handwriting recognition     Hidden Markov models     Deep learning     Deep belief networks     Tandem approach    

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Research on Cyberspace Sovereignty

Fang Binxing,Zou Peng and Zhu Shibing

Strategic Study of CAE 2016, Volume 18, Issue 6,   Pages 1-7 doi: 10.15302/J-SSCAE-2016.06.001

Abstract:

Cyberspace sovereignty (referred to here by its short form, cyber sovereignty) is the extension of national sovereignty to the platform of information and communication technology systems. This article defines cyberspace and cyber sovereignty, discusses the existence of cyber sovereignty, and judges several erroneous points of view that deny cyber sovereignty.

Keywords： cyberspace     cyberspace sovereignty     stakeholder    

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Title Author Date Type Operation

A review on cyber security named entity recognition

Chen Gao, Xuan Zhang, Mengting Han, Hui Liu,zhxuan@ynu.edu.cn

Journal Article

Learning to select pseudo labels: a semi-supervised method for named entity recognition

Zhen-zhen Li, Da-wei Feng, Dong-sheng Li, Xi-cheng Lu,lizhenzhen14@nudt.edu.cn,davyfeng.c@gmail.com,dsli@nudt.edu.cn,xclu@nudt.edu.cn

Journal Article

Disambiguating named entitieswith deep supervised learning via crowd labels

Le-kui ZHOU,Si-liang TANG,Jun XIAO,Fei WU,Yue-ting ZHUANG

Journal Article

Artificial intelligence algorithms for cyberspace security applications: a technological and status review

Jie CHEN, Dandan WU, Ruiyun XIE,chenjie1900@mail.nwpu.edu.cn,wudd@cetcsc.com

Journal Article

Study on the Development of China’s Cyberspace Security Industry

An Da, Liang Zhihao, Xu Shouren

Journal Article

Research on the International Strategy for National Cyberspace Security

Fang Binxing,Du Aning and Zhang Xi,Wang Zhongru

Journal Article

一种易用的实体识别消歧系统评测框架

辉 陈,宝刚 魏,一鸣 李,Yong-huai LIU,文浩 朱

Journal Article

Cyberspace Endogenous Safety and Security

Jiangxing Wu

Journal Article

Adversarial Attacks and Defenses in Deep Learning

Kui Ren, Tianhang Zheng, Zhan Qin, Xue Liu

Journal Article

Emergency and Response for Cyberspace Security

Yu Quan,Yang Lifeng and Gao Guijun、Kou Ziming、Zhai Lidong

Journal Article

A deep Q-learning network based active object detection model with a novel training algorithm for service robots

Shaopeng LIU, Guohui TIAN, Yongcheng CUI, Xuyang SHAO

Journal Article

Diffractive Deep Neural Networks at Visible Wavelengths

Hang Chen, Jianan Feng, Minwei Jiang, Yiqun Wang, Jie Lin, Jiubin Tan, Peng Jin

Journal Article

A network security entity recognition method based on feature template and CNN-BiLSTM-CRF

Ya QIN, Guo-wei SHEN, Wen-bo ZHAO, Yan-ping CHEN, Miao YU, Xin JIN

Journal Article

Tandem hiddenMarkovmodels using deep belief networks for offline handwriting recognition

Partha Pratim ROY, Guoqiang ZHONG, Mohamed CHERIET

Journal Article

Research on Cyberspace Sovereignty

Fang Binxing,Zou Peng and Zhu Shibing

Journal Article