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Localization Algorithm of Indoor Wi-Fi Access Points Based on Signal Strength Relative Relationship and Region Division

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Computers, Materials & Continua 2018年第4期55卷 71-93页

作者： Wenyan Liu Xiangyang Luo Yimin Liu Jianqiang Liu Minghao Liu Yun Q.Shi State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou 450001China School of Electronics and Communication Engineering Zhengzhou University of AeronauticsZhengzhou 450001China New Jersey Institute of Technology NJUSA

Precise localization techniques for indoor Wi-Fi access points(APs)have important application in the security ***,due to the interference of environment factors such as multipath propagation and NLOS(Non-Line-of-Sight),the existing methods for localization indoor Wi-Fi access points based on RSS ranging tend to have lower accuracy as the RSS(Received Signal Strength)is difficult to accurately ***,the localization algorithm of indoor Wi-Fi access points based on the signal strength relative relationship and region division is proposed in this *** algorithm hierarchically divide the room where the target Wi-Fi AP is located,on the region division line,a modified signal collection device is used to measure RSS in two directions of each reference *** RSS values are compared and the region where the RSS value has the relative largest signal strength is located as next candidate *** location coordinate of the target Wi-Fi AP is obtained when the localization region of the target Wi-Fi AP is successively approximated until the candidate region is smaller than the accuracy *** are 360 experiments carried out in this paper with 8 types of Wi-Fi APs including fixed APs and portable *** experimental results show that the average localization error of the proposed localization algorithm is 0.30 meters,and the minimum localization error is 0.16 meters,which is significantly higher than the localization accuracy of the existing typical indoor Wi-Fi access point localization methods.

关键词： Wi-Fi access points indoor localization RSS signal strength relative relationship region division.

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A GAN-based Method for Generating SQL Injection Attack Samples 10

A GAN-based Method for Generating SQL Injection Attack Sampl...

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10th IEEE Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2022

作者： Lu, Dongzhe Fei, Jinlong Liu, Long Li, Zecun Information Engineering University Zhengzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (数字)9781665422079

ISBN: (纸本)9781665422079

Due to the simplicity of implementation and high threat level, SQL injection attacks are one of the oldest, most prevalent, and most destructive types of security attacks on Web-based information systems. With the continuous development and maturity of artificial intelligence technology, it has been a general trend to use AI technology to detect SQL injection. The selection of the sample set is the deciding factor of whether AI algorithms can achieve good results, but dataset with tagged specific category labels are difficult to obtain. This paper focuses on data augmentation to learn similar feature representations from the original data to improve the accuracy of classification models. In this paper, deep convolutional generative adversarial networks combined with genetic algorithms are applied to the field of Web vulnerability attacks, aiming to solve the problem of insufficient number of SQL injection samples. This method is also expected to be applied to sample generation for other types of vulnerability attacks. © 2022 IEEE.

关键词： Generative adversarial networks

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The Causal Reasoning Ability of Open Large Language Model: A Comprehensive and Exemplary Functional Testing 23

The Causal Reasoning Ability of Open Large Language Model: A...

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23rd IEEE International Conference on Software Quality, Reliability, and Security, QRS 2023

作者： Li, Shun-Hang Zhou, Gang Li, Zhi-Bo Lu, Ji-Cang Huang, Ning-Bo State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Information Engineering University Zhengzhou China

ISBN: (纸本)9798350319583

As the intelligent software, the development and application of large language models are extremely hot topics recently, bringing tremendous changes to general AI and software industry. Nonetheless, large language models, especially open source ones, incontrollably suffer from some potential software quality issues such as instability, inaccuracy, and insecurity, making software testing necessary. In this paper, we propose the first solution for functional testing of open large language models to check full-scene availability and conclude empirical principles for better steering large language models, particularly considering their black box and intelligence properties. Specifically, we focus on the model's causal reasoning ability, which is the core of artificial intelligence but almost ignored by most previous work. First, for comprehensive evaluation, we deconstruct the causal reasoning capability into five dimensions and summary the forms of causal reasoning task as causality identification and causality matching. Then, rich datasets are introduced and further modified to generate test cases along with different ability dimensions and task forms to improve the testing integrity. Moreover, we explore the ability boundary of open large language models in two usage modes: prompting and lightweight fine-tuning. Our work conducts comprehensive functional testing on the causal reasoning ability of open large language models, establishes benchmarks, and derives empirical insights for practical usage. The proposed testing solution can be transferred to other similar evaluation tasks as a general framework for large language models or their derivations. © 2023 IEEE.

关键词： Black-box testing

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IBM Small-Scale Superconducting Quantum Computer Not Gate Error Test

IBM Small-Scale Superconducting Quantum Computer Not Gate Er...

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2021 International Conference on Electronic Communication, Computer Science and Technology, ECCST 2021

作者： He, Ming Liu, Xiaonan Wang, Junchao An, Jiale State Key Laboratory of Mathematical Engineering and Advanced Computing Information Engineering University Henan Zhengzhou450001 China

Quantum NOT gates play an important role in the process of quantum information conversion. However, when the X-gate operation is executed on a real quantum computer, there is a large deviation between the actual operation result and the theory, which will lead to inaccurate results when the quantum algorithm containing the X-gate operation is executed. In order to facilitate users to understand the error fluctuations of the X gate in time before executing the quantum algorithm containing the X gate operation on the IBM quantum cloud platform, this paper proposes a method to measure the X gate error. By measuring the X-gate error of four small-scale superconducting quantum computers on the back end of the IBM quantum cloud platform, analyze the degree of fluctuation of the actual measurement value of the quantum device;at the same time, under different execution times shots, the influence of the X-gate test is analyzed. The test results show that the measured value of each qubit of different quantum devices fluctuates to different degrees;the execution times of shots are different, and the test results of X-gate error will be affected to different degrees. This test method helps users select the optimal performance of the qubits before executing quantum algorithms with X gate on IBM's small-scale quantum computers. © Published under licence by IOP Publishing Ltd.

关键词： Errors

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Practical software diversification tool chain for dissimilar redundancy architecture 9

Practical software diversification tool chain for dissimilar...

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9th IEEE Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2020

作者： Zhang, Gaofei Zhang, Zheng Wang, Jiang Ji, Xinsheng State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Information Engineering University Zhengzhou China

With the rapid development of the information society, there are corresponding tool solutions for various daily problems. Aiming at the complex generation process and inconvenient operation of heterogeneous components in a security defense system constructed with dissimilar redundancy, in this paper, we propose a practical basic software application isomerization tool chain, which integrates a series of key technologies of heterogeneous structures. At the same time, corresponding automated tests are provided for users to conduct comprehensive evaluation. Experimental evaluation shows that the tool chain will be able to provide heterogeneous component support for security defense technologies based on dissimilarity redundancy construction and improve the security of software systems. © 2020 IEEE.

关键词： Application programs

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Vulnerability Sample Analysis Based on Dynamic Binary Platform

Vulnerability Sample Analysis Based on Dynamic Binary Platfo...

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2021 International Conference on Aviation Safety and Information Technology, ICASIT 2021

作者： Bu, Wenjuan Kang, Fei Zhao, Yuntian Xu, Jianbo State Key Laboratory of Mathematical Engineering and Advanced Computing Information Engineering University Henan Zhengzhou450000 China

ISBN: (纸本)9781450390422

Based on the existing vulnerability exploitation detection technology, this paper analyzes the buffer overflow vulnerability exploitation process, and detects vulnerability triggering, protection mechanism bypassing and shellcode execution by dynamic binary instrumentation (DBI) monitoring program execution process;According to the analysis of vulnerability sample shellcode behavior, the method of recording instruction information and key API function information is designed based on instrumentation API function;A prototype platform of vulnerability sample transplantation and utilization based on dynamic binary is designed and implemented, and test analysis of 40 typical vulnerability samples is carried out to verify the effectiveness and feasibility of the system. © 2021 ACM.

关键词： Software testing

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Exploiting historical diffusion data to maximize information spread in social networks

International Journal of Database Theory and Application

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International Journal of Database Theory and Application 2015年第2期8卷 193-204页

作者： Zhou, Donghao Han, Wenbao Wang, Yongjun College of Computers National University of Defense Technology Changsha China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China China

Information spread maximization is to find a small subset of nodes in social network such that they can maximize the expected spread of information. In this paper, we attempt harnessing historical information cascades data to learn how information propagates in social networks and how to maximize its spread. In particular, we proposed a voting algorithm to learn diffusion probabilities of edges from cascades data. Then a pruning method is developed to remove trivial edges whose weights are smaller than a threshold. Moreover, motivated by the social influence locality, we propose a Local Influence Model to evaluate node's influence within a local area instead of the whole network, which can effectively reduce the computational complexity. Based on Local Influence Model, we use greedy algorithm to find an approximate optimal solution. Experimental results show that our method significantly outperforms state-of-the-art models both in terms of information spread and algorithm runtime. © 2015 SERSC.

关键词： information spread influence maximization local influence model greedy algorithm pruning method

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Analysis of cryptosystem recognition scheme based on Euclidean distance feature extraction in three machine learning classifiers 3

Analysis of cryptosystem recognition scheme based on Euclide...

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2019 3rd International Conference on Electrical, Mechanical and Computer engineering, ICEMCE 2019

作者： Zhao, Yaqun Fan, Sijie State Key Laboratory of Mathematical Engineering and Advanced Computing 450001 China State Key Laboratory of Cryptography and Science 450001 China

In reality, many cryptographic analysis techniques are based on a specific cryptographic system or a large number of encrypted ciphertext. The identification and detection of cryptographic system is of great significance for evaluating the security of the algorithm and guiding the design and improvement of the algorithm. In this paper, we transcode each character in ciphertext into a decimal number, construct these numbers into one-dimensional arrays, and obtain the Euclidean distance between these one-dimensional arrays. Then we use these distances as features and input them into three machine learning classifiers: random forest, logistic regression and support vector machine to recognize cryptosystem and compare their recognition accuracy. The subjects include 8 common block ciphers (DES, 3DES, AES-128, AES-256, IDEA, SMS4, Blowfish, Camellia-128). The experimental results show that using the feature extraction scheme not only shortens the experimental time, reduces the computational cost, but also improves the recognition accuracy of eight typical block cipher algorithms. The classification accuracy of the ECB mode in the random forest classifier is 75%, which is higher than the existing published literature experimental results. The classification accuracy rate of CBC mode is higher than 13.5%, which is higher than the accuracy of random classification. © Published under licence by IOP Publishing Ltd.

关键词： Feature extraction

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Interest Points Analysis for Internet Forum Based on Long-Short Windows Similarity

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Computers, Materials & Continua 2022年第8期72卷 3247-3267页

作者： Xinghai Ju Jicang Lu Xiangyang Luo Gang Zhou Shiyu Wang Shunhang Li Yang Yang State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001China School of Computing and Information Systems Singapore Management University188065Singapore College of Computer Science and Big Data Fuzhou UniversityFuzhou350116China

For Internet forum Points of Interest(PoI),existing analysis methods are usually lack of usability analysis under different conditions and ignore the long-term variation,which lead to blindness in method *** address this problem,this paper proposed a PoI variation prediction framework based on similarity analysis between long and short *** on the framework,this paper presented 5 PoI analysis algorithms which can be categorized into 2 types,i.e.,the traditional sequence analysis methods such as autoregressive integrated moving average model(ARIMA),support vector regressor(SVR),and the deep learning methods such as convolutional neural network(CNN),long-short term memory network(LSTM),Transformer(TRM).Specifically,this paper firstly divides observed data into long and short windows,and extracts key words as PoI of each ***,the PoI similarities between long and short windows are calculated for training and ***,series of experiments is conducted based on real Internet forum *** results show that,all the 5 algorithms could predict PoI variations well,which indicate effectiveness of the proposed *** the length of long window is small,traditional methods perform better,and SVR is the *** the contrary,the deep learning methods show superiority,and LSTM performs *** results could provide beneficial references for PoI variation analysis and prediction algorithms selection under different parameter configurations.

关键词： Point of interest(PoI)analysis long and short windows sequential analysis deep learning

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An landmark evaluation algorithm based on router identification and delay measurement 5th

An landmark evaluation algorithm based on router identificat...

引用

5th International Conference on Artificial Intelligence and Security, ICAIS 2019

作者： Ma, Te Liu, Fenlin Zhang, Fan Luo, Xiangyang State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Science and Technology Institute Zhengzhou China

ISBN: (纸本)9783030242701

City-level landmarks serve as an important foundation for achieving city-level and higher-precision IP geolocation. On the basis of the feature that router host names in network-developed areas often imply geographical location information, this paper proposes a city-level landmark evaluation algorithm based on router identification. Firstly, router host name matching rules are formulated by using plenty of network topology information obtained through probing. Secondly, the matching rules are used to extract router host names;the geographical locations corresponding to routers are queried through the established geographical location dictionary;and the nearest router that can obtain a city-level location in the probing path is reserved. Finally, candidate landmarks are evaluated based on the rule that the physical distance between network entities is less than the delay conversion distance. The experimental results show that the proposed algorithm can effectively evaluate the reliability of landmarks: among the experimental results on 8000 network landmarks in 4 cities in the United states, compared with the typical database query-based landmark obtaining, the city-level accuracy rates of landmarks are increased by 4.4%, 6.85%, 4.35% and 7.05% respectively. © Springer Nature Switzerland AG 2019.

关键词： Location

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