scholarly journals Cryptosystem Identification Scheme Based on ASCII Code Statistics

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Wenyu Zhang ◽  
Yaqun Zhao ◽  
Sijie Fan
Keyword(s):  
Language Processing ◽  
Block Cipher ◽  
Feature Vector ◽  
Identification Accuracy ◽  
Encrypted Data ◽  
Machine Learning Classifiers ◽  
Theoretical Significance ◽  
Ascii Code ◽  
Extraction Scheme ◽  
Computation Load

In the field of information security, block cipher is widely used in the protection of messages, and its safety naturally attracts people’s attention. The identification of the cryptosystem is the premise of encrypted data analysis. It belongs to the category of attack analysis in cryptanalysis and has important theoretical significance and application value. This paper focuses on the extraction of ciphertext features and the construction of cryptosystem identification classifiers. The main contents and innovations of this paper are as follows. Firstly, inspired by language processing, we propose the feature extraction scheme based on ASCII statistics of ciphertexts which decrease the dimension of data preprocessing. Secondly, on the basis of previous work, we increase the types of block ciphers to eight, encrypt plaintext of the same sizes as experimental objects, and recognize the cryptosystem. Thirdly, we use two machine learning classifiers to perform classification experiments including random forest and SVM. The experimental results show that our scheme can not only improve the identification accuracy of 8 typical block cipher algorithms but also shorten the experimental time and reduce the computation load by greatly minimizing the dimension of the feature vector. And the various evaluation indicators obtained by the scheme have been greatly improved compared with the existing published literature.

scholarly journals Dynamic Rounds Chaotic Block Cipher Based on Keyword Abstract Extraction

Entropy ◽  
2018 ◽  
Vol 20 (9) ◽  
pp. 693 ◽  
Author(s):  
Juan Wang ◽  
Qun Ding
Keyword(s):  
Information Retrieval ◽  
Language Processing ◽  
Block Cipher ◽  
Design Method ◽  
Chaotic Sequence ◽  
Cryptographic Algorithm ◽  
Nist Tests ◽  
Feistel Structure ◽  
Different Levels ◽  
Chaotic Theory

According to the keyword abstract extraction function in the Natural Language Processing and Information Retrieval Sharing Platform (NLPIR), the design method of a dynamic rounds chaotic block cipher is presented in this paper, which takes into account both the security and efficiency. The cipher combines chaotic theory with the Feistel structure block cipher, and uses the randomness of chaotic sequence and the nonlinearity of chaotic S-box to dynamically generate encrypted rounds, realizing more numbers of dynamic rounds encryption for the important information marked by NLPIR, while less numbers of dynamic rounds encryption for the non-important information that is not marked. Through linear and differential cryptographic analysis, ciphertext information entropy, “0–1” balance and National Institute of Science and Technology (NIST) tests and the comparison with other traditional and lightweight block ciphers, the results indicate that the dynamic variety of encrypted rounds can achieve different levels of encryption for different information, which can achieve the purpose of enhancing the anti-attack ability and reducing the number of encrypted rounds. Therefore, the dynamic rounds chaotic block cipher can guarantee the security of information transmission and realize the lightweight of the cryptographic algorithm.

scholarly journals Application of Axis Orbit Image Optimization in Fault Diagnosis for Rotor System

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Xinyu Pang ◽  
Jie Shao ◽  
Xuanyi Xue ◽  
Wangwang Jiang
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Fault Diagnosis ◽  
Mathematical Morphology ◽  
Bp Neural Network ◽  
Feature Vector ◽  
Rotor System ◽  
Identification Accuracy ◽  
Frequency Noise ◽  
Axis Orbit

The shape characteristic of the axis orbit plays an important role in the fault diagnosis of rotating machinery. However, the original signal is typically messy, and this affects the identification accuracy and identification speed. In order to improve the identification effect, an effective fault identification method for a rotor system based on the axis orbit is proposed. The method is a combination of ensemble empirical mode decomposition (EEMD), morphological image processing, Hu invariant moment feature vector, and back propagation (BP) neural network. Experiments of four fault forms are performed in single-span rotor and double-span rotor test rigs. Vibration displacement signals in the X and Y directions of the rotor are processed via EEMD filtering to eliminate the high-frequency noise. The mathematical morphology is used to optimize the axis orbit including the dilation and skeleton operation. After image processing, Hu invariant moments of the skeleton axis orbits are calculated as the feature vector. Finally, the BP neural network is trained to identify the faults of the rotor system. The experimental results indicate that the time of identification of the tested axis orbits via morphological processing corresponds to 13.05 s, and the identification accuracy rate ranges to 95%. Both exceed that without mathematical morphology. The proposed method is reliable and effective for the identification of the axis orbit and aids in online monitoring and automatic identification of rotor system faults.

scholarly journals Lightweight Block Cipher Security Evaluation Based on Machine Learning Classifiers and Active S-Boxes

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 134052-134064
Author(s):  
Ting Rong Lee ◽  
Je Sen Teh ◽  
Norziana Jamil ◽  
Jasy Liew Suet Yan ◽  
Jiageng Chen
Keyword(s):  
Machine Learning ◽  
Block Cipher ◽  
Security Evaluation ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Lightweight Block Cipher

scholarly journals Multi-Dictionary Based Collaborative Representation: With Applications to 3D Ear and 3D Palmprint Identification

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Lin Zhang ◽  
Xining Zhu ◽  
Lida Li
Keyword(s):  
Large Scale ◽  
Feature Vector ◽  
Collaborative Representation ◽  
Final Decision ◽  
Simple Majority ◽  
Negative Effects ◽  
Benchmark Datasets ◽  
3D Scanners ◽  
Extraction Scheme ◽  
Class Labels

Biometrics based personal authentication has been found to be an effective method for recognizing, with high confidence, a person’s identity. With the emergence of reliable and inexpensive 3D scanners, recent years have witnessed a growing interest in developing 3D biometrics systems. As a commonsense, matching algorithms are crucial for such systems. In this paper, we focus on investigating identification methods for two specific 3D biometric identifiers, 3D ear and 3D palmprint. Specifically, we propose a Multi-Dictionary based Collaborative Representation (MDCR) framework for classification, which can reduce the negative effects aroused by some local regions. With MDCR, a range map is partitioned into overlapping blocks and, from each block, a feature vector is extracted. At the dictionary construction stage, feature vectors from blocks having the same locations in gallery samples can form a dictionary and, accordingly, multiple dictionaries are obtained. Given a probe sample, by coding its each feature vector on the corresponding dictionary, multiple class labels can be obtained and then we use a simple majority-based voting scheme to make the final decision. In addition, a novel patch-wise and statistics-based feature extraction scheme is proposed, combining the range image’s local surface type information and local dominant orientation information. The effectiveness of the proposed approach has been corroborated by extensive experiments conducted on two large-scale and widely-used benchmark datasets, the UND Collection J2 3D ear dataset and the PolyU 3D palmprint dataset. To make the results reproducible, we have publicly released the source code.

Parsing Models for Identifying Multiword Expressions

2013 ◽  
Vol 39 (1) ◽  
pp. 195-227 ◽  
Author(s):  
Spence Green ◽  
Marie-Catherine de Marneffe ◽  
Christopher D. Manning
Keyword(s):  
Language Processing ◽  
Prediction Models ◽  
State Of The Art ◽  
Lexical Representation ◽  
Structured Prediction ◽  
Identification Accuracy ◽  
Syntactic Analysis ◽  
Multiword Expressions ◽  
Context Free ◽  
Alternative Theories

Multiword expressions lie at the syntax/semantics interface and have motivated alternative theories of syntax like Construction Grammar. Until now, however, syntactic analysis and multiword expression identification have been modeled separately in natural language processing. We develop two structured prediction models for joint parsing and multiword expression identification. The first is based on context-free grammars and the second uses tree substitution grammars, a formalism that can store larger syntactic fragments. Our experiments show that both models can identify multiword expressions with much higher accuracy than a state-of-the-art system based on word co-occurrence statistics. We experiment with Arabic and French, which both have pervasive multiword expressions. Relative to English, they also have richer morphology, which induces lexical sparsity in finite corpora. To combat this sparsity, we develop a simple factored lexical representation for the context-free parsing model. Morphological analyses are automatically transformed into rich feature tags that are scored jointly with lexical items. This technique, which we call a factored lexicon, improves both standard parsing and multiword expression identification accuracy.

scholarly journals iMPT-FDNPL: Identification of Membrane Protein Types with Functional Domains and a Natural Language Processing Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Wei Chen ◽  
Lei Chen ◽  
Qi Dai
Keyword(s):  
Natural Language Processing ◽  
Membrane Protein ◽  
Natural Language ◽  
Language Processing ◽  
Functional Domains ◽  
Functional Domain ◽  
Processing Approach ◽  
Cell Membrane Proteins ◽  
Extraction Scheme ◽  
Domain Information

Membrane protein is an important kind of proteins. It plays essential roles in several cellular processes. Based on the intramolecular arrangements and positions in a cell, membrane proteins can be divided into several types. It is reported that the types of a membrane protein are highly related to its functions. Determination of membrane protein types is a hot topic in recent years. A plenty of computational methods have been proposed so far. Some of them used functional domain information to encode proteins. However, this procedure was still crude. In this study, we designed a novel feature extraction scheme to obtain informative features of proteins from their functional domain information. Such scheme termed domains as words and proteins, represented by its domains, as sentences. The natural language processing approach, word2vector, was applied to access the features of domains, which were further refined to protein features. Based on these features, RAndom k-labELsets with random forest as the base classifier was employed to build the multilabel classifier, namely, iMPT-FDNPL. The tenfold cross-validation results indicated the good performance of such classifier. Furthermore, such classifier was superior to other classifiers based on features derived from functional domains via one-hot scheme or derived from other properties of proteins, suggesting the effectiveness of protein features generated by the proposed scheme.

Identification of prediabetes discussions in unstructured clinical documentation using natural language processing methods (Preprint)

2021 ◽  
Author(s):  
Jessica Schwartz ◽  
Eva Tseng ◽  
Nisa M Maruthur ◽  
Masoud Rouhizadeh
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Human Performance ◽  
Management Practices ◽  
Search Strategy ◽  
Keyword Search ◽  
Machine Learning Techniques ◽  
Clinical Documentation ◽  
Machine Learning Classifiers ◽  
Learning Techniques

BACKGROUND Prediabetes affects 1 in 3 US adults. Most are not receiving evidence-based interventions so understanding how providers discuss prediabetes with patients will inform how to improve their care. OBJECTIVE Develop an NLP algorithm using machine learning techniques to identify discussions of prediabetes in narrative documentation. METHODS We developed and applied a keyword search strategy to identify discussions of prediabetes in clinical documentation for patients with prediabetes. We manually reviewed matching notes to determine which represented actual prediabetes discussions. We applied seven machine learning models against our manual annotation. RESULTS Machine learning classifiers were able to achieve classification results that were close to human performance with up to 98% precision and recall to identify prediabetes discussions in clinical documentation. CONCLUSIONS We demonstrated that prediabetes discussions can be accurately identified using an NLP algorithm. This approach can be used to understand and identify prediabetes management practices in primary care, thereby informing interventions to improve guideline-concordant care.

scholarly journals Practical Forward-Secure Range and Sort Queries with Update-Oblivious Linked Lists

2015 ◽  
Vol 2015 (2) ◽  
pp. 81-98 ◽  
Author(s):  
Erik-Oliver Blass ◽  
Travis Mayberry ◽  
Guevara Noubir
Keyword(s):  
New Record ◽  
Block Cipher ◽  
Bilinear Pairings ◽  
Forward Security ◽  
Encrypted Data ◽  
Asymmetric Cryptography ◽  
Data Store ◽  
Practical Evaluation ◽  
The Face ◽  
Work First

Abstract We revisit the problem of privacy-preserving range search and sort queries on encrypted data in the face of an untrusted data store. Our new protocol RASP has several advantages over existing work. First, RASP strengthens privacy by ensuring forward security: after a query for range [a, b], any new record added to the data store is indistinguishable from random, even if the new record falls within range [a, b]. We are able to accomplish this using only traditional hash and block cipher operations, abstaining from expensive asymmetric cryptography and bilinear pairings. Consequently, RASP is highly practical, even for large database sizes. Additionally, we require only cloud storage and not a computational cloud like related works, which can reduce monetary costs significantly. At the heart of RASP, we develop a new update-oblivious bucket-based data structure. We allow for data to be added to buckets without leaking into which bucket it has been added. As long as a bucket is not explicitly queried, the data store does not learn anything about bucket contents. Furthermore, no information is leaked about data additions following a query. Besides formally proving RASP’s privacy, we also present a practical evaluation of RASP on Amazon Dynamo, demonstrating its efficiency and real world applicability.

Substation Equipment 3D Identification Based on KNN Classification of Subspace Feature Vector

2017 ◽  
Vol 28 (5) ◽  
pp. 807-819 ◽  
Author(s):  
Weiying Guo ◽  
Yong Ji ◽  
Yong Luo ◽  
Yan Zhou
Keyword(s):  
Point Cloud ◽  
Nearest Neighbor ◽  
Feature Vector ◽  
Identification Accuracy ◽  
K Nearest Neighbor ◽  
Positive Direction ◽  
Cloud Data ◽  
Knn Classification ◽  
Substation Equipment

Abstract Aiming to realize rapid and efficient three-dimensional (3D) identification of substation equipment, this article proposes a new method in which the 3D identification of substation equipment is based on K-nearest neighbor (KNN) classification of subspace feature vector. First of all, the article uses octree encoding to reduce and denoise the point cloud data obtained by a 3D laser scanner. Secondly, position calibration and size standardization are used for the point cloud after pretreatment. Then, the normalized point cloud is divided into a number of cubes with same size. The cosine of the angle between the positive direction of z axis and a vector from the global centroid of the point cloud to the centroid of each subspace is regarded as the feature of the subspace. All cosines of subspaces constitute the feature of the point cloud. Finally, we classify the subspace feature vector by using the KNN algorithm and improve classification accuracy by using the particle swarm optimization algorithm. The simulation results show that the identification accuracy of the proposed method for unknown substation equipment is about 90% and the proposed method is applicable to low-degree losses. Apparently, this method can accurately identify 3D substation equipment. At the same time, increasing the number of subspaces will improve the accuracy; however, it will increase the recognition time.

Application of Deep Learning Model Convolution Neural Network for Effective Web Information Retrieval

2021 ◽  
pp. 100-120
Author(s):  
Suruchi Chawla
Keyword(s):  
Neural Network ◽  
Information Retrieval ◽  
Deep Learning ◽  
Language Processing ◽  
Feature Vector ◽  
Convolution Neural Network ◽  
Web Information Retrieval ◽  
Fixed Length ◽  
Web Information ◽  
The Rich

Convolution neural network (CNN) is the most popular deep learning method that has been used for various applications like image recognition, computer vision, and natural language processing. In this chapter, application of CNN in web query session mining for effective information retrieval is explained. CNN has been used for document analysis to capture the rich contextual structure in a search query or document content. The document content represented in matrix form using Word2Vec is applied to CNN for convolution as well as maxpooling operations to generate the fixed length document feature vector. This fixed length document feature vector is input to fully connected neural network (FNN) and generates the semantic document vector. These semantic document vectors are clustered to group similar document for effective web information retrieval. An experiment was performed on the data set of web query sessions, and results confirm the effectiveness of CNN in web query session mining for effective information retrieval.

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