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Building an efficient intrusion detection system based on feature selection and ensemble classifier

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arXiv 2019年

作者： Zhou, Yuyang Cheng, Guang Jiang, Shanqing Dai, Mian School of Cyber Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Ministry of Education Nanjing China Jiangsu Provincial Key Laboratory of Computer Network Technology Southeast University Nanjing China National Key Laboratory of Science and Technology on Information System Security Beijing China

Intrusion detection system (IDS) is one of extensively used techniques in a network topology to safeguard the integrity and availability of sensitive assets in the protected systems. Although many supervised and unsupervised learning approaches from the field of machine learning have been used to increase the efficacy of IDSs, it is still a problem for existing intrusion detection algorithms to achieve good performance. First, lots of redundant and irrelevant data in high-dimensional datasets interfere with the classification process of an IDS. Second, an individual classifier may not perform well in the detection of each type of attacks. Third, many models are built for stale datasets, making them less adaptable for novel attacks. Thus, we propose a new intrusion detection framework in this paper, and this framework is based on the feature selection and ensemble learning techniques. In the first step, a heuristic algorithm called CFS-BA is proposed for dimensionality reduction, which selects the optimal subset based on the correlation between features. Then, we introduce an ensemble approach that combines C4.5, Random Forest (RF), and Forest by Penalizing Attributes (Forest PA) algorithms. Finally, voting technique is used to combine the probability distributions of the base learners for attack recognition. The experimental results, using NSL-KDD, AWID, and CIC-IDS2017 datasets, reveal that the proposed CFS-BA-Ensemble method is able to exhibit better performance than other related and state of the art approaches under several metrics. Copyright © 2019, The Authors. All rights reserved.

关键词： Data mining

An Intelligent Learning Method and System for Cybersecurity Threat Detection

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Journal of Physics: Conference Series 2020年第1期1575卷

作者： Yuan Tao Wei Hu Moyan Li The Third Research Institute of Ministry of Public Security Shanghai China National Engineering Laboratory for Key Technology of Classified Information Security Protection Beijing China

To protect information system and data effectively is the core of cybersecurity. At present, most technologies are based on known static protection strategies. Through the cybersecurity threat is dynamic, so it is necessary to detect the cybersecurity in advance, so as to update the protection strategy in time, which means the dynamic and active defence. The intelligent learning method and system for cybersecurity threat detection is proposed. And the immune selection of cybersecurity threat detection is proposed to select the appropriate threshold, and the knowledge map of cybersecurity threat detection are constructed from suspicious time, security management centre, security computing environment, security area boundary and security communication network. The threshold is built by immune selection, and the cybersecurity intelligent detection can be achieved by knowledge map. The architecture of intelligent detection system is proposed. So that the cybersecurity threat detection can be dynamic and intelligent, and the performance of cybersecurity threat detection and analysis can be greatly improved.

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Research on Knowledge Graph Model for Cybersecurity Logs Based on Ontology and Classified protection

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Journal of Physics: Conference Series 2020年第1期1575卷

作者： Yuan Tao Moyan Li Wei Hu The Third Research Institute of Ministry of Public Security Shanghai China National Engineering Laboratory for Key Technology of Classified Information Security Protection Beijing China

In order to audit and analyse cybersecurity events from massive logs, the knowledge graph model for cybersecurity logs is proposed. The data layer and the pattern layer of the knowledge graph model for cybersecurity logs is obtained based on ontology and classified protection. Entity classification of cybersecurity logs is extracted by using classified protection standards in the data layer of the knowledge graph. The relationship and attribute of ontology from cybersecurity logs is defined. The pattern layer of the knowledge graph model for cybersecurity logs is defined, classified protection data is integrated in entity alignment, and classified protection information gain is used in ontology construction. The structure of the knowledge graph for cybersecurity logs is given. So that the efficient association and deep mining analysis of cybersecurity logs are realized, and it can be directly analysed and processed on the data without the need for precise modelling of the problem. The efficiency of logs analysis is improved. The model has heuristic characteristics and generalization abstract ability, and the model is suitable for big data analysis of large-scale cybersecurity logs.

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Secure medical data collection via local differential privacy 4

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Secure medical data collection via local differential privac...

4th IEEE International Conference on Computer and Communications, ICCC 2018

作者： Wang, Zhiqiang Ma, Pingchuan Wang, Ruming Zhang, Jianyi Chi, Yaping Ma, Yanzhe Yang, Tao Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei China Beijing Electronic Science and Technology Institute Beijing China Hainan University Haikou China Ministry of Public Security Key Lab of Information Network Security Shanghai China

ISBN: (纸本)9781538683392

As the volume of medical data mining increases, so do the need to preserve patient privacy. And the exposure of medical data may degrade the level of health care service and reduce the trust of patients. Local Differential Privacy (LDP) was proposed to solve problems in the context of local privacy, by which data collectors are hardly to get exact individual information. We present a secure medical data collection framework and apply our framework on synthetic data at different scale. Finally, we evaluate the performance of our work. Our experimental results show that both privacy and validity of medical data are aligned. © 2018 IEEE.

关键词： Data acquisition

Device-independent quantum private query protocol without the assumption of perfect detectors

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arXiv 2019年

作者： Li, Dan-Dan Huang, Xiao-Hong Huang, Wei Gao, Fei Institute of Network Technology Beijing University of Posts and Telecommunications Beijing100876 China State Key Laboratory of Cryptology P. O. Box 5159 Beijing100878 China Science and Technology on Communication Security Laboratory Institute of Southwestern Communication Chengdu610041 China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China

The first device-independent quantum private query protocol (MRT17) which is proposed by Maitra et al. [Phys. Rev. A 95, 042344 (2017)] to enhance the security through the certification of the states and measurements. However, the MRT17 protocol works under an assumption of perfect detectors, which increases difficulty in the implementations. Therefore, it is crucial to investigate what would affect the security of this protocol if the detectors were imperfect. Meanwhile, Maitra et al. also pointed out that this problem remains open. In this paper, we analyze the security of MRT17 protocol when the detectors are imperfect and then find that this protocol is under attack in the aforementioned case. Furthermore, we propose device-independent QPQ protocol without the assumption of perfect detectors. Compared with MRT17 protocol, our protocol is more practical without relaxing the security in the device-independent framework. Copyright © 2019, The Authors. All rights reserved.

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Multidimensional particle swarm optimization based long short-term memory method for human activity recognition 4

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Multidimensional particle swarm optimization based long shor...

4th IEEE International Conference on Computer and Communications, ICCC 2018

作者： Zuo, Peiliang Li, Haoliang Zhang, Yuanli Yang, Tao Ministry of Education Beijing University of Posts and Telecommunications Key Laboratory of Universal Wireless Communication Beijing China Department of Network Space Security Beijing Electronic Science and Technology Institute Beijing China

ISBN: (纸本)9781538683392

Human activity recognition (HAR) is indispensable for human-machine interaction. Related applications in many fields, e.g. medical, military, and security, facilitate and even change the lives of human beings. However, a key issue, i.e. the unsatisfactory recognition accuracy, still remains to be addressed. In this paper, we explore a new way to design a long short-term memory (LSTM) method specifically for HAR with datasets collected by sensors or mobile devices. In particular, combining with the multidimensional particle swarm optimization (MD-PSO), the LSTM method, which has advantages in processing collected datasets with timing characteristics, is not only optimized in terms of parameters (i.e. weights and bias), but also of the network topology as well as the number of blocks in each layer. In other words, we can further get the optimal topology and network mapping parameters of the LSTM network under the conditions of supervised learning. By utilizing the real dataset, performance of the proposed method is verified. Moreover, the test results show that our method significantly outperforms the commonly used support vector machine (SVM) and LSTM methods. © 2018 IEEE.

关键词： Support vector machines

An energy efficient authentication scheme using chebyshev chaotic map for smart grid environment

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arXiv 2020年

作者： Zhang, Liping Zhu, Yue Ren, Wei Wang, Yinghan Xiong, Neal N. Zhang, Yan School of Computer Science China University of Geosciences Wuhan430074 China School of Computer Science China University of Geosciences Guangxi Key Laboratory of Cryptography and Information Security Guilin541004 China Key Laboratory of Network Assessment Technology CAS [Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Department of Mathematics and Computer Science Northeastern State University OK74464 United States Department of Informatics University of Oslo Norway

Smart grid (SG) is an automatic electric power transmission network with bidirectional flows of both energy and information. As one of the important applications of smart grid, charging between electric vehicles has attracted much attentions. However, authentication between vehicle users and an aggregator may be vulnerable to various attacks due to the usage of wireless communications. Although several authentication schemes are proposed for smart grid environments with privacy protection, some of them still impose security issues such as anonymity absence. In addition, most of the existing authentication schemes have not thoroughly tackled the charging peak in their design, so these schemes cannot guarantee the requirement in terms of low energy consumption in smart grid environments. In order to reduce the computational costs yet preserve required security, the Chevyshev chaotic map based authentication schemes are proposed. However, the security requirements of Chebyshev polynomials bring a new challenge to the design of authentication schemes based on Chebyshev chaotic maps. To solve this issue, we propose a practical Chebyshev polynomial algorithm by using a binary exponentiation algorithm based on square matrix to achieve secure and efficient Chebyshev polynomial computation. We further apply the proposed algorithm to construct an energy-efficient authentication and key agreement scheme for smart grid environments. Compared with state-of-the-art schemes, the proposed authentication scheme effectively reduces the computational costs and communication costs by adopting the proposed Chebyshev polynomial algorithm. Furthermore, the ProVerif tool is employed to analyze the security of the proposed authentication scheme. Our experimental results justified that our proposed authentication scheme can outperform state-of-the-art schemes in terms of the computational overhead while achieving privacy protection. Copyright © 2020, The Authors. All rights reserved.

关键词： Authentication

A new dance generation task from start to end

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Journal of Physics: Conference Series 2020年第1期1651卷

作者： Xiao Xu Lijuan Duan Faculty of Information Technology Beijing University of Technology Beijing 100124 China Beijing Key Laboratory of Trusted Computing Beijing 100124 China National Engineering Laboratory for Critical Technologies of Information Security Classified Protection Beijing 100124 China

The dance generation task is an interesting task. The general task is to predict the next dance sequence through a given initial frame, or to convert from music to dance through audio information. However, these works cannot control the direction of dance generation and lack suitable ground truth to evaluate the generated results. In our work, we give the start frame and the end frame, which can effectively solve the above problems, and can also be understood as a standardized new task. Our experimental results show that through a two-stream CNN network, we can well complete the new task that we proposed.

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A note on self-dual generalized reed-solomon codes

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arXiv 2020年

作者： Fang, Weijun Zhang, Jun Xia, Shu-Tao Fu, Fang-Wei Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen518055 China PCL Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen518055 China School of Mathematical Sciences Capital Normal University Beijing100048 China Chern Institute of Mathematics and LPMC Nankai University Tianjin300071 China Tianjin Key Laboratory of Network and Data Security Technology Nankai University Tianjin300071 China

A linear code is called an MDS self-dual code if it is both an MDS code and a self-dual code with respect to the Euclidean inner product. The parameters of such codes are completely determined by the code length. In this paper, we consider new constructions of MDS self-dual codes via generalized Reed-Solomon (GRS) codes and their extended codes. The critical idea of our constructions is to choose suitable evaluation points such that the corresponding (extended) GRS codes are self-dual. The evaluation set of our constructions is consists of a subgroup of finite fields and its cosets in a bigger subgroup. Four new families of MDS self-dual codes are obtained and they have better parameters than previous results in certain region. Moreover, by the Möbius action over finite fields, we give a systematic way to construct self-dual GRS codes with different evaluation points provided any known self-dual GRS codes. Specially, we prove that all the self-dual extended GRS codes over Fq with length n MSC Codes 94B05 Copyright © 2020, The Authors. All rights reserved.

关键词： Reed-Solomon codes