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作者： Liu, Qi Chen, Xuedong Liu, Xiaodong Linge, Nigel Jiangsu Collaborative Innovation Centre of Atmospheric Environment and Equipment Technology School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China School of Computer and Software Jiangsu Engineering Centre of Network Monitoring Nanjing University of Information Science and Technology Nanjing210044 China School of Computing Edinburgh Napier University 10 Clinton Road EdinburghEH10 5DT United Kingdom School of Computing Science and Engineering University of Salford Salford Greater ManchesterM5 4WT United Kingdom

Underwater acoustic sensor networks (UANs) have emerged as a promising technology recently, which can be applied in many areas such as military and civil, where the communication between devices is crucial and challenging owing to the unique characteristics of underwater acoustic-based environment, such as high latency and low bandwidth. In this paper, context awareness is applied to the design of an underwater communication approach, called context-aware communication approach for a UAN (CACA-UAN), which aims to improve the overall performance of the underwater communication. According to the results, the proposed CACA-UAN can increase the efficiency and reliability of the underwater communication system. © Copyright 2018 Inderscience Enterprises Ltd.

关键词： Efficiency

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Minimum length key in MST cryptosystems

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Science China(Information Sciences) 2017年第5期60卷 142-152页

作者： Haibo HONG Licheng WANG Haseeb AHMAD Yixian YANG Zhiguo QU School of Computer Science and Information Engineering Zhejiang Gongshang University Information Security Center State Key Laboratory of Networking and Switching TechnologyBeijing University of Posts and Telecommunications Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science & Technology

As a special factorization category of finite groups, logarithmic signature(LS) is used as the main component of cryptographic keys that operate within secret key cryptosystems such as PGM and public key cryptosystems like M ST1, M ST2 and M ST3. An LS with the shortest length is called a minimal logarithmic signature(MLS) that constitutes of the smallest sized blocks and offers the lowest complexity, and is therefore desirable for cryptographic constructions. However, the existence of MLSs for finite groups should be firstly taken into an account. The MLS conjecture states that every finite simple group has an MLS. If it holds, then by the consequence of Jordan-H¨older Theorem, every finite group would have an MLS. In fact, many cryptographers and mathematicians are keen for solving this problem. Some effective work has already been done in search of MLSs for finite groups. Recently, we have made some progress towards searching a minimal length key for MST cryptosystems and presented a theoretical proof of MLS conjecture.

关键词： MLS conjecture finite groups (minimal) logarithmic signature minimum length key MST cryp tosystems

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Motor Fault Diagnosis Based on Short-time Fourier Transform and Convolutional Neural network

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Chinese Journal of Mechanical engineering 2017年第6期30卷 1357-1368页

作者： Li-Hua Wang Xiao-Ping Zhao Jia-Xin Wu Yang-Yang Xie Yong-Hong Zhang School of Information and Control Nanjing University ofInformation Science and Technology Nanjing 210044 China School of Computer and Software Nanjing University ofInformation Science and Technology Nanjing 210044 China Network Monitoring Center of Jiangsu Province NanjingUniversity of Information Science and TechnologyNanjing 210044 China

With the rapid development of mechanical equipment, the mechanical health monitoring field has entered the era of big data. However, the method of manual feature extraction has the disadvantages of low efficiency and poor accuracy, when handling big data. In this study, the research object was the asynchronous motor in the drivetrain diagnostics simulator system. The vibration signals of different fault motors were collected. The raw signal was pretreated using short time Fourier transform （STFT） to obtain the corresponding time-frequency map. Then, the feature of the time-frequency map was adap- tively extracted by using a convolutional neural network （CNN）. The effects of the pretreatment method, and the hyper parameters of network diagnostic accuracy, were investigated experimentally. The experimental results showed that the influence of the preprocessing method is small, and that the batch-size is the main factor affecting accuracy and training efficiency. By investigating feature visualization, it was shown that, in the case of big data, the extracted CNN features can represent complex mapping relationships between signal and health status, and can also overcome the prior knowledge and engineering experience requirement for feature extraction, which is used by tra- ditional diagnosis methods. This paper proposes a new method, based on STFT and CNN, which can complete motor fault diagnosis tasks more intelligently and accurately.

关键词： Big data Deep learning Short-time Fouriertransform Convolutional neural network Motor

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Coverless image steganography based on SIFT and BOF

Coverless image steganography based on SIFT and BOF

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作者： Yuan, Chengsheng Xia, Zhihua Sun, Xingming School of Computer and Software Nanjing University of Information Science and Technology China Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology China

In order to realize secret communication, conventional steganography techniques can embed the secret message into carrier images by modifying the content and structure of carrier images. Although steganography techniques provide us with some more secure communication, the modification traces will be detected through using these brand-new steganalysis tools. Thereby, in order to eliminate the influence of modification traces, we propose a novel coverless image steganography scheme based on scale invarient feature transform and bag of feature. Different from conventional steganography, images have already contained the concealing information, so we can transmit natural images, whose features are the same with the secret information, to receivers. Firstly, the robust feature hash sequences are constructed with our protocol of feature. Then, the secret message is converted into bitstreams. Finally, these images with the seceret information are chosen and sent to receivers through using inverted index. Throughout the process, original images are never modified in our scheme, so our method can resist the analysis of existing steganalysis tools. Numerical experiments also indicate that our scheme has a desirable robustness to the common image attacks.

关键词： Steganography

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Short text classification technology based on KNN+hierarchy SVM 12th

Short text classification technology based on KNN+hierarchy ...

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12th International Conference on Future Information Technology, FutureTech 2017

作者： Yin, Chunyong Shi, Lingfeng Wang, Jin School of Computer and Software Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing China College of Information Engineering Yangzhou University Yangzhou China

ISBN: (纸本)9789811050404

A short text classification method based on combination of KNN and hierarchical SVM is proposed. First, the KNN algorithm is improved to get the K nearest neighbor class labels quickly, so as to effectively filter the candidate classes of documents. And then classify them from top to bottom using a multi-class sparse hierarchical SVM classifier. By this way, the document can be classified efficiently. © Springer Nature Singapore Pte Ltd. 2017.

关键词： Classification (of information)

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Improved data stream clustering algorithm for anomaly detection 12th

Improved data stream clustering algorithm for anomaly detect...

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12th International Conference on Future Information Technology, FutureTech 2017

作者： Yin, Chunyong Zhang, Sun Wang, Jin School of Computer and Software Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing China College of Information Engineering Yangzhou University Yangzhou China

ISBN: (纸本)9789811050404

Intrusion detection provides important protection for network security and anomaly detection as a type of intrusion detection, can recognize the pattern of normal behaviors and label the behaviors which departure from normal pattern as abnormal behaviors. We think that the traditional methods based on dataset do not satisfy the needs of dynamic network environment. The network data stream is temporal and cannot be treated as static dataset. The concept and distribution of data objects is variety in different time stamps and the changing is unpredictable. Therefore, we propose an improved data stream clustering algorithm and design the frame of anomaly detection according to the improved algorithm. It can modify the established model with the changing of data stream and detect abnormal behaviors in time. © Springer Nature Singapore Pte Ltd. 2017.

关键词： Intrusion detection

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Application of an improved data stream clustering algorithm in intrusion detection system 12th

Application of an improved data stream clustering algorithm ...

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12th International Conference on Future Information Technology, FutureTech 2017

作者： Yin, Chunyong Xia, Lian Wang, Jin School of Computer and Software Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing China College of Information Engineering Yangzhou University Yangzhou China

ISBN: (纸本)9789811050404

With the continuous development of computer network technology, traditional intrusion detection system is short of good adaptability. Aiming at the traditional intrusion detection system is difficult to adapt to the increasing amount of data demand for real-time processing capability, this paper proposes a clustering algorithm based on sliding window data streams, based on which we build the IDS network security defense model. The experiment results show that the model is able to adapt to the high-speed network intrusion detection requirements. © Springer Nature Singapore Pte Ltd. 2017.

关键词： Data mining

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A duplicate data detection approach based on MapReduce and HDFS

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Recent Patents on computer Science 2017年第2期10卷 140-148页

作者： Wei, Fang Xue-Zhi, Wen Yu, Zheng Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science & Technology Nanjing210044 China School of Computer and Software Nanjing University of Information Science & Technology Nanjing210044 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing210066 China Department of Electrical & Computer Engineering University of Florida GainesvilleFL32611 United States

Background: With the surge in the volume of collected data, deduplication will undoubtedly become one of the problems faced by researchers. There is significant advantage for deduplication to reduce storage, network bandwidth, and system scalability of coarse-grained redundant data. Since the conventional methods of deleting duplicate data include hash comparison and binary differential incremental. They will lead to several bottlenecks for processing large scale data. And, the traditional Simhash similarity method has less consideration on the natural similarity of text in some specific fields and cannot run in parallel program with large scale text data processing efficiently. This paper examines several most important patents in the area of data detection. Then, this paper will focus on large scale of data deduplication based on MapReduce and HDFS. Methods: We propose a duplicate data detection approach based on MapReduce and HDFS, which uses the Simhash similarity computing algorithm and SSN algorithm, and explain our distributed duplicate detection workflow. The important technical advantages of the invention include generating a checksum for each processed record and comparing the generated checksum to detect duplicate record. It produces the fingerprints of short text with Simhash similarity algorithm. It clusters the fingerprint results using Shared Nearest Neighbor (SNN) algorithm. The whole parallel progress is implemented using MapReduce programming model. Results: From the experimental results, we conclude that our proposed approach obtains MapReduce job schedules with significantly less executing time, making it suitable for processing large scale datasets in real applications. The experimental results show the proposed approach has better performance and efficiency. Conclusion: In this patent, we propose a duplicate data detection approach based on MapReduce and HDFS, which uses the Simhash similarity computing algorithm and SSN algorithm. The results

关键词： MapReduce

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Error analysis of time delay based on ultrasonic phased array structural health monitoring

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Journal of computers (Taiwan) 2018年第4期29卷 176-184页

作者： Ji, Sai Gu, Feihong Sun, Yajie Wang, Jin Jiangsu engineering Centre of Network Monitoring Nanjing University of Information Science and Technology Nanjing210044 China Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology Nanjing University of Information Science and Technology Nanjing210044 China School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China School of Information Engineering Yangzhou University Yangzhou225002 China

Ultrasonic phased array technology becomes significant in structural health monitoring with its flexible beam deflection and strong focusing performance. The transducer is used to complete the multiple directions scanning of the structure. During the process of beam scanning, the signal exciting from each element needs to add a time delay in time domain, due to the difference to the distance from each element to the far field target point, so that the excitation signal can reach the target point and the far field at the same time. Besides, in the process of receiving and transmitting process, it also needs to add a time delay for the response signal, so that the sensor can receive the reflection signal from the preset deflection angle. Therefore, the error will be generated in the calculation of time delay. Making the anti-rust aluminum plate as the experimental object, the process of ultrasonic phased array structural health monitoring will be introduced. And then the reason of time delay error will be pointed out and analyzed. © 2018 computer Society of the Republic of China. All Rights Reserved.

关键词： Time delay

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Fingerprint Liveness Detection from Different Fingerprint Materials Using Convolutional Neural network and Principal Component Analysis

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computers, Materials & Continua 2017年第4期53卷 357-372页

作者： Chengsheng Yuan Xinting Li Q.M.Jonathan Wu Jin Li Xingming Sun School of Computer and Software Nanjing University of Information Science&TechnologyNing Liu RoadNo.219NanjingChina210044 Jiangsu Engineering Center of Network Monitoring Ning Liu RoadNo.219NanjingChina210044 Department of Electrical and Computer Engineering University of Windsor401 Sunset AvenueWindsorONCanada N9B 3P4 School of Computer Science Guangzhou UniversityYudongxi Road 36Tianhe DistrictGuangzhouChina510500

Fingerprint-spoofing attack often occurs when imposters gain access illegally by using artificial fingerprints,which are made of common fingerprint materials,such as silicon,latex,***,to protect our privacy,many fingerprint liveness detection methods are put forward to discriminate fake or true *** work on liveness detection for fingerprint images is focused on the construction of complex handcrafted features,but these methods normally destroy or lose spatial information between *** from existing methods,convolutional neural network(CNN)can generate high-level semantic representations by learning and concatenating low-level edge and shape features from a large amount of labeled ***,CNN is explored to solve the above problem and discriminate true fingerprints from fake ones in this *** reduce the redundant information and extract the most distinct features,ROI and PCA operations are performed for learned features of convolutional layer or pooling *** that,the extracted features are fed into SVM *** results based on the LivDet(2013)and the LivDet(2011)datasets,which are captured by using different fingerprint materials,indicate that the classification performance of our proposed method is both efficient and convenient compared with the other previous methods.

关键词： Fingerprint liveness detection CNNs PCA SVM ROI LivDet 2013 LivDet 2011

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