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Статті в журналах з теми "Secure classification"

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Автор: Grafiati

Опубліковано: 14 березня 2023

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1

Pearson, Jane L., Deborah A. Cohn, Philip A. Cowan, and Carolyn Pape Cowan. "Earned- and continuous-security in adult attachment: Relation to depressive symptomatology and parenting style." Development and Psychopathology 6, no. 2 (1994): 359–73. http://dx.doi.org/10.1017/s0954579400004636.

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AbstractThe secure working model classification of adult attachment, as derived from Main and Goldwyn's (in press) Adult Attachment Interview scoring system, was considered in terms of earned-security and continuous-security. Earned-security was a classification given to adults who described difficult, early relationships with parents, but who also had current secure working models as indicated by high coherency scores; continuous-security referred to a classification in which individuals described secure early attachment relationship with parents and current secure working models. Working models of attachment were classified as earned-secure, continuous-secure, or insecure in a sample of 40 parents of preschool children. Comparisons among the classifications were conducted on a measure of depressive symptoms and two sets of ratings of observed parenting styles. Adults with earned-secure classifications had comparable depressive symptomatology to insecures, with 30% of the insecures, 40% of the earned-secures, and only 10% of the continuous-secures having scores exceeding the clinical cut-off. The rate of depressive symptomatology in the earned-secure group suggests that reconstructions of past difficulties may remain emotional liabilities despite a current secure working model. With regard to parenting styles with their preschoolers, the behavior of earned-secure parents was comparable to that of the continuous-secures. This refinement in conceptualizing secure working models suggests ways for understanding variation in pathways to competent parenting as well as a possible perspective on how adults' adverse early experiences may continue to place them and their children at risk.

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2

Hareesha, K. S., and M. Sumana. "Secure Two Party Privacy Preserving Classification Using Encryption." International Journal of Information and Electronics Engineering 6, no. 2 (2016): 67–71. http://dx.doi.org/10.18178/ijiee.2016.6.2.597.

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3

Baars, Thijs, and Marco Spruit. "Designing a Secure Cloud Architecture." International Journal of Information Security and Privacy 6, no. 1 (January 2012): 14–32. http://dx.doi.org/10.4018/jisp.2012010102.

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Security issues are paramount when considering adoption of any cloud technology. This article proposes the Secure Cloud Architecture (SeCA) model on the basis of data classifications which defines a properly secure cloud architecture by testing the cloud environment on eight attributes. The SeCA model is developed using a literature review and a Delphi study with seventeen experts, consisting of three rounds. The authors integrate the CI3A—an extension on the CIA-triad—to create a basic framework for testing the classification inputted. The data classification is then tested on regional, geo-spatial, delivery, deployment, governance and compliance, network, premise and encryption attributes. After this testing has been executed, a specification for a secure cloud architecture is outputted.

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4

T., Tamanna, and Rajeev Kumar. "Secure Cloud Model using Classification and Cryptography." International Journal of Computer Applications 159, no. 6 (February 15, 2017): 8–13. http://dx.doi.org/10.5120/ijca2017912953.

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5

Usharani, A. V., and Girija Attigeri. "Secure EMR Classification and Deduplication Using MapReduce." IEEE Access 10 (2022): 34404–14. http://dx.doi.org/10.1109/access.2022.3161439.

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6

Wiseman, Simon R. "Classification services in the SWORD secure DBMS." Computers & Security 14, no. 4 (January 1995): 307–21. http://dx.doi.org/10.1016/0167-4048(95)00004-r.

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7

Salehi, Mahmood, and Azzedine Boukerche. "Secure opportunistic routing protocols: methods, models, and classification." Wireless Networks 25, no. 2 (September 11, 2017): 559–71. http://dx.doi.org/10.1007/s11276-017-1575-1.

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8

., S. Narayanan. "CLASSIFICATION OF SECURE ENCRYPTED RELATIONALDATA IN CLOUD COMPUTING." International Journal of Research in Engineering and Technology 05, no. 03 (March 25, 2016): 159–66. http://dx.doi.org/10.15623/ijret.2016.0503034.

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9

S L Swapna and V Saravanan. "Jaccard Index Cat Gradient Boosting Classification for Secured Big Data Communication." Applied Science and Engineering Journal for Advanced Research 1, no. 5 (September 30, 2022): 1–14. http://dx.doi.org/10.54741/asejar.1.5.1.

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Анотація:

Big data is observed as a novel field dealing with datasets that are too complex in providing indispensable services for daily chores and also discovering hidden patterns. Network security has become a major issue due to big data analytics, which offers unlimited research potential. More specifically, secure data communication without a third party is a major concern. Also, as large, heterogeneous, and complex data sets emerge, existing security mechanisms cannot provide or address network threats quickly or accurately. Therefore, along with the decrease in time, accuracy and error rate are other research concerns. Accordingly, an accurate and timely big data-based secure method called Jaccard Index Cat Gradient Boosting Classification-based Secured Data Communication (JICGBC-SDC) using the Internet of Things is presented. Firstly, for each cloud user, user registration is performed by acquiring information from various sensors. Second, information is collected from the registered cloud users by means of the Jaccard Index Cat Gradient Boosting Classifier algorithm. Such a proposed algorithm imposes a lower error rate and minimizes classification time, ensuring the most reliable and secured data communication between cloud users. To ensure secure data communication, weak learners' results are combined to form a strong classifier. The proposed method is implemented in Java and tested on the CloudSim simulator for classification accuracy, classification time, and error rate. The experimental results reveal the JICGBC-SDC method increases the performance of secured data communication for error rate by 77%, classification time by 79% and classification accuracy by 25% as compared to the state-of-the-art work.

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10

B, Murugeshwari, Jayakumar C, and Sarukesi K. "Secure Multi Party Computation Technique for Classification Rule Sharing." International Journal of Computer Applications 55, no. 7 (October 20, 2012): 1–10. http://dx.doi.org/10.5120/8764-2683.

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11

Katarahweire, Marriette, Engineer Bainomugisha, and Khalid A. Mughal. "Data Classification for Secure Mobile Health Data Collection Systems." Development Engineering 5 (2020): 100054. http://dx.doi.org/10.1016/j.deveng.2020.100054.

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12

Ahmed, Kazi Wasif, Omit Chanda, Noman Mohammed, and Yang Wang. "Obfuscated image classification for secure image-centric friend recommendation." Sustainable Cities and Society 41 (August 2018): 940–48. http://dx.doi.org/10.1016/j.scs.2017.10.001.

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13

Tawalbeh, Lo’ai, Nour S. Darwazeh, Raad S. Al-Qassas, and Fahd AlDosari. "A Secure Cloud Computing Model based on Data Classification." Procedia Computer Science 52 (2015): 1153–58. http://dx.doi.org/10.1016/j.procs.2015.05.150.

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14

Balachander, J., and E. Ramanujam. "Rule based Medical Content Classification for Secure Remote Health Monitoring." International Journal of Computer Applications 165, no. 4 (May 17, 2017): 21–26. http://dx.doi.org/10.5120/ijca2017913852.

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15

Xue, Liang, Dongxiao Liu, Cheng Huang, Xiaodong Lin, and Xuemin Sherman Shen. "Secure and Privacy-Preserving Decision Tree Classification with Lower Complexity." Journal of Communications and Information Networks 5, no. 1 (March 2020): 16–25. http://dx.doi.org/10.23919/jcin.2020.9055107.

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16

Liu, Jiasen, Chao Wang, Zheng Tu, Xu An Wang, Chuan Lin, and Zhihu Li. "Secure KNN Classification Scheme Based on Homomorphic Encryption for Cyberspace." Security and Communication Networks 2021 (November 3, 2021): 1–12. http://dx.doi.org/10.1155/2021/8759922.

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With the advent of the intelligent era, more and more artificial intelligence algorithms are widely used and a large number of user data are collected in the cloud server for sharing and analysis, but the security risks of private data breaches are also increasing in the meantime. CKKS homomorphic encryption has become a research focal point in the cryptography field because of its ability of homomorphic encryption for floating-point numbers and comparable computational efficiency. Based on the CKKS homomorphic encryption, this paper implements a secure KNN classification scheme in cloud servers for Cyberspace (CKKSKNNC) and supports batch calculation. This paper uses the CKKS homomorphic encryption scheme to encrypt user data samples and then uses Euclidean distance, Pearson similarity, and cosine similarity to compute the similarity between ciphertext data samples. Finally, the security classification of the samples is realized by voting rules. This paper selects IRIS data set for experimental, which is the classification data set commonly used in machine learning. The experimental results show that the accuracy of the other three similarity algorithms of the IRIS data is around 97% except for the Pearson correlation coefficient, which is almost the same as that in plaintext, which proves the effectiveness of this scheme. Through comparative experiments, the efficiency of this scheme is proved.

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17

Resende, Amanda, Davis Railsback, Rafael Dowsley, Anderson C. A. Nascimento, and Diego F. Aranha. "Fast Privacy-Preserving Text Classification Based on Secure Multiparty Computation." IEEE Transactions on Information Forensics and Security 17 (2022): 428–42. http://dx.doi.org/10.1109/tifs.2022.3144007.

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18

Tangod, Kiran, and Gururaj Kulkarni. "Secure Communication through MultiAgent System-Based Diabetes Diagnosing and Classification." Journal of Intelligent Systems 29, no. 1 (June 29, 2018): 703–18. http://dx.doi.org/10.1515/jisys-2017-0353.

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Анотація:

Abstract The main objective of the research is to provide a multi-agent data mining system for diagnosing diabetes. Here, we use multi-agents for diagnosing diabetes such as user agent, connection agent, updation agent, and security agent, in which each agent performs their own task under the coordination of the connection agent. For secure communication, the user symptoms are encrypted with the help of Elliptic Curve Cryptography and Optimal Advanced Encryption Standard. In Optimal Advanced Encryption Standard algorithm, the key values are optimally selected by means of differential evaluation algorithm. After receiving the encrypted data, the suggested method needs to find the diabetes level of the user through multiple kernel support vector machine algorithm. Based on that, the agent prescribes the drugs for the corresponding user. The performance of the proposed technique is evaluated by classification accuracy, sensitivity, specificity, precision, recall, execution time and memory value. The proposed method will be implemented in JAVA platform.

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19

Samanthula, Bharath K., Yousef Elmehdwi, and Wei Jiang. "k-Nearest Neighbor Classification over Semantically Secure Encrypted Relational Data." IEEE Transactions on Knowledge and Data Engineering 27, no. 5 (May 1, 2015): 1261–73. http://dx.doi.org/10.1109/tkde.2014.2364027.

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20

Udhaya, P., and G. Manikandan. "An enhanced approach for secure pattern classification in adversarial environment." Contemporary Engineering Sciences 8 (2015): 533–38. http://dx.doi.org/10.12988/ces.2015.5269.

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21

Zhu, Youwen, Xingxin Li, Jian Wang, Yining Liu, and Zhiguo Qu. "Practical Secure Naïve Bayesian Classification Over Encrypted Big Data in Cloud." International Journal of Foundations of Computer Science 28, no. 06 (September 2017): 683–703. http://dx.doi.org/10.1142/s0129054117400135.

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Анотація:

Cloud can provide much convenience for big data storage and analysis. To enjoy the advantage of cloud service with privacy preservation, huge data is increasingly outsourced to cloud in encrypted form. Unfortunately, encryption may impede the analysis and computation over the outsourced dataset. Naïve Bayesian classification is an effective algorithm to predict the class label of unlabeled samples. In this paper, we investigate naïve Bayesian classification on encrypted large-scale dataset in cloud, and propose a practical and secure scheme for the challenging problem. In our scheme, all the computation task of naïve Bayesian classification are completed by the cloud, which can dramatically reduce the burden of data owner and users. We give a formal security proof for our scheme. Based on the theoretical proof, we can strictly guarantee the privacy of both input dataset and output classification results, i.e., the cloud can learn nothing useful about the training data of data owner and the test samples of users throughout the computation. Additionally, we not only theoretically analyze our computation complexity and communication overheads, but also evaluate our implementation cost by leveraging extensive experiments over real dataset, which shows our scheme can achieve practical efficiency.

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22

Yoo, Yongjae, Jihwan Suh, Jeongyeup Paek, and Saewoong Bahk. "Secure Region Detection Using Wi-Fi CSI and One-Class Classification." IEEE Access 9 (2021): 65906–13. http://dx.doi.org/10.1109/access.2021.3076176.

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23

S. Almasoud, Ahmed, Abdelzahir Abdelmaboud, Faisal S. Alsubaei, Manar Ahmed Hamza, Ishfaq Yaseen, Mohammed Abaker, Abdelwahed Motwakel, and Mohammed Rizwanullah. "Deep Learning with Image Classification Based Secure CPS for Healthcare Sector." Computers, Materials & Continua 72, no. 2 (2022): 2633–48. http://dx.doi.org/10.32604/cmc.2022.024619.

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24

IDRIS, ISMAILA, UMAR MAJIGI MUHAMMAD, MUHAMMAD ABDULHAMID SHAFI'I, OLALERE MORUFU, BASHIR ABDULLAHI MUHAMMAD, and O. NWAOCHA VIVIAN. "SECURE UNIVERSITY NETWORK ARCHITECTURE, VULNERABILITIES, RISK PRIORITY LEVEL CLASSIFICATION AND COUNTERMEASURES." i-manager’s Journal on Wireless Communication Networks 7, no. 2 (2018): 42. http://dx.doi.org/10.26634/jwcn.7.2.15604.

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25

Yang, Haomiao, Shaopeng Liang, Jianbing Ni, Hongwei Li, and Xuemin Sherman Shen. "Secure and Efficient k NN Classification for Industrial Internet of Things." IEEE Internet of Things Journal 7, no. 11 (November 2020): 10945–54. http://dx.doi.org/10.1109/jiot.2020.2992349.

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26

Liu, Yi, Yu Luo, Youwen Zhu, Yang Liu, and Xingxin Li. "Secure multi-label data classification in cloud by additionally homomorphic encryption." Information Sciences 468 (November 2018): 89–102. http://dx.doi.org/10.1016/j.ins.2018.07.054.

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27

Deng, Guoqiang, Min Tang, Yuhao Zhang, Ying Huang, and Xuefeng Duan. "Privacy-Preserving Outsourced Artificial Neural Network Training for Secure Image Classification." Applied Sciences 12, no. 24 (December 14, 2022): 12873. http://dx.doi.org/10.3390/app122412873.

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Анотація:

Artificial neural network (ANN) is powerful in the artificial intelligence field and has been successfully applied to interpret complex image data in the real world. Since the majority of images are commonly known as private with the information intended to be used by the owner, such as handwritten characters and face, the private constraints form a major obstacle in developing high-precision image classifiers which require access to a large amount of image data belonging to multiple users. State-of-the-art privacy-preserving ANN schemes often use full homomorphic encryption which result in a substantial overhead of computation and data traffic for the data owners, and are restricted to approximation models by low-degree polynomials which lead to a large accuracy loss of the trained model compared to the original ANN model in the plain domain. Consequently, it is still a huge challenge to train an ANN model in the encrypted-domain. To mitigate this problem, we propose a privacy-preserving ANN system for secure constructing image classifiers, named IPPNN, where the server is able to train an ANN-based classifier on the combined image data of all data owners without being able to observe any images using primitives, such as randomization and functional encryption. Our system achieves faster training time and supports lossless training. Moreover, IPPNN removes the need for multiple communications among data owners and servers. We analyze the security of the protocol and perform experiments on a large scale image recognition task. The results show that the IPPNN is feasible to use in practice while achieving high accuracy.

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28

Kodati, Sarangam, Kumbala Pradeep Reddy, Thotakura Veerananna, S. Govinda Rao, and G. Anil Kumar. "Security Framework Connection Assistance for IoT Device Secure Data communication." E3S Web of Conferences 309 (2021): 01061. http://dx.doi.org/10.1051/e3sconf/202130901061.

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Анотація:

Today, Internet of Things (IoT) services has been increasing extensively because of their optimum device sizes and their developed network infrastructure that includes devices based on internet embedded with various sensors, actuators, communication, and storage components providing connection and data exchange. Presently number of industries use vast number of IoT devices, there are some challenges like reducing the risks and threats that exposure, accommodating the huge number of IoT devices in network and providing secure vulnerabilities have risen. Supervised learning has recently been gaining popularity to provide device classification. But this supervised learning became unrealistic as producing millions of new IoT devices each year, and insufficient training data. In this paper, security framework connection assistance for IoT device secured data communication is proposed. A multi-level security support architecture which combines clustering technique with deep neural networks for designing the resource oriented IoT devices with high security and these are enabling both the seen and unseen device classification. The datasets dimensions are reduced by considering the technique as auto encoder. Therefore in between accuracy and overhead classification good balancing is established. The comparative results are describes that proposed security system is better than remaining existing systems.

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29

Zuber, Martin, and Renaud Sirdey. "Efficient homomorphic evaluation of k-NN classifiers." Proceedings on Privacy Enhancing Technologies 2021, no. 2 (January 29, 2021): 111–29. http://dx.doi.org/10.2478/popets-2021-0020.

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Abstract We design and implement an efficient, secure, homomorphic k-Nearest Neighbours determination algorithm, to be used for regression or classification over private data. Our algorithm runs in quadratic complexity with regard to the size of the database but is the only one in the literature to make the secure determination completely non-interactively. We show that our secure algorithm is both efficient and accurate when applied to classification problems requiring a small set of model vectors, and still scales to larger sets of model vectors with high accuracy yet at greater (sequential) computational costs.

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30

Schoppmann, Phillipp, Lennart Vogelsang, Adrià Gascón, and Borja Balle. "Secure and Scalable Document Similarity on Distributed Databases: Differential Privacy to the Rescue." Proceedings on Privacy Enhancing Technologies 2020, no. 2 (April 1, 2020): 209–29. http://dx.doi.org/10.2478/popets-2020-0024.

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AbstractPrivacy-preserving collaborative data analysis enables richer models than what each party can learn with their own data. Secure Multi-Party Computation (MPC) offers a robust cryptographic approach to this problem, and in fact several protocols have been proposed for various data analysis and machine learning tasks. In this work, we focus on secure similarity computation between text documents, and the application to k-nearest neighbors (k-NN) classification. Due to its non-parametric nature, k-NN presents scalability challenges in the MPC setting. Previous work addresses these by introducing non-standard assumptions about the abilities of an attacker, for example by relying on non-colluding servers. In this work, we tackle the scalability challenge from a different angle, and instead introduce a secure preprocessing phase that reveals differentially private (DP) statistics about the data. This allows us to exploit the inherent sparsity of text data and significantly speed up all subsequent classifications.

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31

Tarik, Boudheb, and Elberrichi Zakaria. "Privacy Preserving Classification of Biomedical Data With Secure Removing of Duplicate Records." International Journal of Organizational and Collective Intelligence 8, no. 3 (July 2018): 41–58. http://dx.doi.org/10.4018/ijoci.2018070104.

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Анотація:

Classifying data is to automatically assign predefined classes to data. It is one of the main applications of data mining. Having complete access to all data is critical for building accurate models. Data can be highly sensitive, such as biomedical data, which cannot be disclosed or shared with third party, because it can harm individuals and organizations. The challenge is how to preserve privacy and usefulness of data. Privacy preserving classification addresses this problem. Collaborative models are constructed over networks without violating the data owners' privacy. In this article, the authors address two problems: privacy records deduplication of the same records and privacy-preserving classification. They propose a randomized hash technic for deduplication and an enhanced privacy preserving classification of biomedical data over horizontally distributed data based on two homomorphic encryptions. No private, intermediate or final results are disclosed. Experimentations show that their solution is efficient and secure without loss of accuracy.

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32

Kjamilji, Artrim, Arben Idrizi, Shkurte Luma-Osmani, and Ferihane Zenuni-Kjamilji. "Secure Naïve Bayes Classification without Loss of Accuracy with Application to Breast Cancer Prediction." Proceeding International Conference on Science and Engineering 3 (April 30, 2020): 397–403. http://dx.doi.org/10.14421/icse.v3.536.

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Анотація:

The classification and prediction accuracy of Machine Learning (ML) algorithms, which often outperform human experts of the related field, have enabled them to be used in areas such as health and disease prediction, image and speech recognition, cyber-security threats and credit-card fraud detection and others. However, laws, ethics and privacy concerns prevent ML algorithms to be used in many real-case scenarios. In order to overcome this problem, we introduce a few flexible and secure building blocks which can be used to build different privacy preserving classifications schemes based on already trained ML models. Then, as a use-case scenario, we utilize and practically use those blocks to enable a privacy preserving Naïve Bayes classifier in the semi-honest model with application to breast cancer detection. Our theoretical analysis and experimental results show that the proposed scheme in many aspects is more efficient in terms of computation and communication cost, as well as in terms of security properties than several state of the art schemes. Furthermore, our privacy preserving scheme shows no loss of accuracy compared to the plain classifier.

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33

Sumana M. and Hareesha K.S. "Accurate Classification Models for Distributed Mining of Privately Preserved Data." International Journal of Information Security and Privacy 10, no. 4 (October 2016): 58–73. http://dx.doi.org/10.4018/ijisp.2016100104.

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Анотація:

Data maintained at various sectors, needs to be mined to derive useful inferences. Larger part of the data is sensitive and not to be revealed while mining. Current methods perform privacy preservation classification either by randomizing, perturbing or anonymizing the data during mining. These forms of privacy preserving mining work well for data centralized at a single site. Moreover the amount of information hidden during mining is not sufficient. When perturbation approaches are used, data reconstruction is a major challenge. This paper aims at modeling classifiers for data distributed across various sites with respect to the same instances. The homomorphic and probabilistic property of Paillier is used to perform secure product, mean and variance calculations. The secure computations are performed without any intermediate data or the sensitive data at multiple sites being revealed. It is observed that the accuracy of the classifiers modeled is almost equivalent to the non-privacy preserving classifiers. Secure protocols require reduced computation time and communication cost.

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34

Alrayes, Fatma S., Saud S. Alotaibi, Khalid A. Alissa, Mashael Maashi, Areej Alhogail, Najm Alotaibi, Heba Mohsen, and Abdelwahed Motwakel. "Artificial Intelligence-Based Secure Communication and Classification for Drone-Enabled Emergency Monitoring Systems." Drones 6, no. 9 (August 26, 2022): 222. http://dx.doi.org/10.3390/drones6090222.

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Анотація:

Unmanned Aerial Vehicles (UAVs), or drones, provided with camera sensors enable improved situational awareness of several emergency responses and disaster management applications, as they can function from remote and complex accessing regions. The UAVs can be utilized for several application areas which can hold sensitive data, which necessitates secure processing using image encryption approaches. At the same time, UAVs can be embedded in the latest technologies and deep learning (DL) models for disaster monitoring areas such as floods, collapsed buildings, or fires for faster mitigation of its impacts on the environment and human population. This study develops an Artificial Intelligence-based Secure Communication and Classification for Drone-Enabled Emergency Monitoring Systems (AISCC-DE2MS). The proposed AISCC-DE2MS technique majorly employs encryption and classification models for emergency disaster monitoring situations. The AISCC-DE2MS model follows a two-stage process: encryption and image classification. At the initial stage, the AISCC-DE2MS model employs an artificial gorilla troops optimizer (AGTO) algorithm with an ECC-Based ElGamal Encryption technique to accomplish security. For emergency situation classification, the AISCC-DE2MS model encompasses a densely connected network (DenseNet) feature extraction, penguin search optimization (PESO) based hyperparameter tuning, and long short-term memory (LSTM)-based classification. The design of the AGTO-based optimal key generation and PESO-based hyperparameter tuning demonstrate the novelty of our work. The simulation analysis of the AISCC-DE2MS model is tested using the AIDER dataset and the results demonstrate the improved performance of the AISCC-DE2MS model in terms of different measures.

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35

Aparna, R., and B. B. Amberker. "Analysis of Key Management Schemes for Secure Group Communication and Their Classification." Journal of Computing and Information Technology 17, no. 2 (2009): 203. http://dx.doi.org/10.2498/cit.1001215.

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36

Tangod, Kiran, and Gururaj Kulkarni. "Secure agent-based diagnosis and classification using optimal kernel support vector machine." International Journal of Biomedical Engineering and Technology 37, no. 1 (2021): 25. http://dx.doi.org/10.1504/ijbet.2021.117513.

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37

Kulkarni, Gururaj, and Kiran Tangod. "Secure Agent Based Diagnosis and Classification Using Optimal Kernel Support Vector Machine." International Journal of Biomedical Engineering and Technology 1, no. 1 (2018): 1. http://dx.doi.org/10.1504/ijbet.2018.10013947.

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Pak, Wooguil. "Design of Integrated packet classification algorithm for Supporting High Performance Secure Routers." Journal of Security Engineering 15, no. 2 (April 30, 2018): 103–18. http://dx.doi.org/10.14257/jse.2018.04.02.

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39

Wu, Wei, Jian Liu, Hong Rong, Huimei Wang, and Ming Xian. "Efficient k-Nearest Neighbor Classification Over Semantically Secure Hybrid Encrypted Cloud Database." IEEE Access 6 (2018): 41771–84. http://dx.doi.org/10.1109/access.2018.2859758.

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40

Kaur, Kulwinder, and Vikas Zandu. "A Secure Data Classification Model in Cloud Computing Using Machine Learning Approach." International Journal of Grid and Distributed Computing 9, no. 8 (August 31, 2016): 13–22. http://dx.doi.org/10.14257/ijgdc.2016.9.8.02.

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41

J., Swathy, and Surya S.R. "Review on k -Nearest Neighbor Classification over Semantically Secure Encrypted Relational Data." International Journal of Computer Applications 133, no. 8 (January 15, 2016): 1–4. http://dx.doi.org/10.5120/ijca2016907988.

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42

Liu, Lin, Jinshu Su, Ximeng Liu, Rongmao Chen, Kai Huang, Robert H. Deng, and Xiaofeng Wang. "Toward Highly Secure Yet Efficient KNN Classification Scheme on Outsourced Cloud Data." IEEE Internet of Things Journal 6, no. 6 (December 2019): 9841–52. http://dx.doi.org/10.1109/jiot.2019.2932444.

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43

Sun, Maohua, and Ruidi Yang. "An efficient secure k nearest neighbor classification protocol with high‐dimensional features." International Journal of Intelligent Systems 35, no. 11 (August 30, 2020): 1791–813. http://dx.doi.org/10.1002/int.22272.

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44

Jararweh, Yaser, Mahmoud Al-Ayyoub, Lo’ai Tawalbeh, Ala’ Darabseh, and Houbing Song. "Software-defined systems support for secure cloud computing based on data classification." Annals of Telecommunications 72, no. 5-6 (November 8, 2016): 335–45. http://dx.doi.org/10.1007/s12243-016-0549-0.

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45

A. Alissa, Khalid, Mohammed Maray, Areej A. Malibari, Sana Alazwari, Hamed Alqahtani, Mohamed K. Nour, Marwa Obbaya, Mohamed A. Shamseldin та Mesfer Al Duhayyim. "Optimal Deep Learning Model Enabled Secure UAV Classification for營ndustry 4.0". Computers, Materials & Continua 74, № 3 (2023): 5349–67. http://dx.doi.org/10.32604/cmc.2023.033532.

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46

Lokhande, Meghana P., Dipti Durgesh Patil, Lalit V. Patil, and Mohammad Shabaz. "Machine-to-Machine Communication for Device Identification and Classification in Secure Telerobotics Surgery." Security and Communication Networks 2021 (August 27, 2021): 1–16. http://dx.doi.org/10.1155/2021/5287514.

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Анотація:

The capacity of machine objects to communicate autonomously is seen as the future of the Internet of Things (IoT), but machine-to-machine communication (M2M) is also gaining traction. In everyday life, security, transportation, industry, and healthcare all employ this paradigm. Smart devices have the ability to detect, handle, store, and analyze data, resulting in major network issues such as security and reliability. There are numerous vulnerabilities linked with IoT devices, according to security experts. Prior to performing any activities, it is necessary to identify and classify the device. Device identification and classification in M2M for secure telerobotic surgery are presented in this study. Telerobotics is an important aspect of the telemedicine industry. The major purpose is to provide remote medical care, which eliminates the requirement for both doctors and patients to be in the same location. This paper aims to propose a security and energy-efficient protocol for telerobotic surgeries, which is the primary concern at present. For secure telerobotic surgery, the author presents an Efficient Device type Detection and Classification (EDDC) protocol for device identification and classification in M2M communication. The periodic trust score is calculated using three factors from each sensor node. It demonstrates that the EDDC protocol is more effective and secure in detecting and categorizing rogue devices.

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47

Arkhipova, Anastasiya, and Danila Karevskiy. "Honeypot as a tool for creating an effective secure system." Digital Technology Security, no. 2 (June 25, 2021): 122–35. http://dx.doi.org/10.17212/2782-2230-2021-2-122-135.

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In the presente work, the theoretical aspects of honeypot systems were considered, and the classification of honeypots on various grounds was presented. The architecture of a honeypot system is presented, designed to investigate the behavior of an attacker after his penetration into the corporate system, as a tool for implementing a complex effective secure system of the organization.

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48

Riggio, Giacomo, Angelo Gazzano, Borbála Zsilák, Beatrice Carlone, and Chiara Mariti. "Quantitative Behavioral Analysis and Qualitative Classification of Attachment Styles in Domestic Dogs: Are Dogs with a Secure and an Insecure-Avoidant Attachment Different?" Animals 11, no. 1 (December 23, 2020): 14. http://dx.doi.org/10.3390/ani11010014.

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Since several modified Strange Situation Procedures (SSP) have been used to investigate dog-to-owner attachment, in this study two different samples of dog-owner dyads underwent two modified versions of the SSP. Dogs’ attachment style to the owner was assessed based on a novel adaptation of the attachment pattern classification used for infant-caregivers. Dogs’ behavioral data were collected using continuous sampling and, in the second protocol, also with a scoring system for greeting and social play. In both studies, secure and avoidant dogs’ behavior was compared using the Mann Whitney test, while differences within each group across episodes were analyzed using the Wilcoxon paired sample test. The classification seemed to be effective at identifying both avoidant and secure attachment patterns in dogs. As expected, differences in key attachment behaviors, such as proximity/contact seeking toward the caregiver, between secure and avoidant dogs were more evident in the final episodes of the test. Differently from secure dogs, avoidant dogs did not show an increase in proximity/contact seeking behavior with the caregiver in any of the procedures. Further studies with larger samples are needed to support the effectiveness of this classification and investigate on the remaining attachment styles.

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49

Kumar, Abhinav, Sanjay Kumar Singh, K. Lakshmanan, Sonal Saxena, and Sameer Shrivastava. "A Novel Cloud-Assisted Secure Deep Feature Classification Framework for Cancer Histopathology Images." ACM Transactions on Internet Technology 21, no. 2 (June 2021): 1–22. http://dx.doi.org/10.1145/3424221.

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Анотація:

The advancements in the Internet of Things (IoT) and cloud services have enabled the availability of smart e-healthcare services in a distant and distributed environment. However, this has also raised major privacy and efficiency concerns that need to be addressed. While sharing clinical data across the cloud that often consists of sensitive patient-related information, privacy is a major challenge. Adequate protection of patients’ privacy helps to increase public trust in medical research. Additionally, DL-based models are complex, and in a cloud-based approach, efficient data processing in such models is complicated. To address these challenges, we propose an efficient and secure cancer diagnostic framework for histopathological image classification by utilizing both differential privacy and secure multi-party computation. For efficient computation, instead of performing the whole operation on the cloud, we decouple the layers into two modules: one for feature extraction using the VGGNet module at the user side and the remaining layers for private prediction over the cloud. The efficacy of the framework is validated on two datasets composed of histopathological images of the canine mammary tumor and human breast cancer. The application of differential privacy preserving to the proposed model makes the model secure and capable of preserving the privacy of sensitive data from any adversary, without significantly compromising the model accuracy. Extensive experiments show that the proposed model efficiently achieves the trade-off between privacy and model performance.

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Gongada, Sandhya Rani, Muktevi Chakravarthy, and Bhukya Mangu. "Power system contingency classification using machine learning technique." Bulletin of Electrical Engineering and Informatics 11, no. 6 (December 1, 2022): 3091–98. http://dx.doi.org/10.11591/eei.v11i6.4031.

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Анотація:

One of the most effective ways for estimating the impact and severity of line failures on the static security of the power system is contingency analysis. The contingency categorization approach uses the overall performance index to measure the system's severity (OPI). The newton raphson (NR) load flow technique is used to extract network variables in a contingency situation for each transmission line failure. Static security is categorised into five categories in this paper: secure (S), critically secure (CS), insecure (IS), highly insecure (HIS), and most insecure (MIS). The K closest neighbor machine learning strategy is presented to categorize these patterns. The proposed machine learning classifiers are trained on the IEEE 30 bus system before being evaluated on the IEEE 14, IEEE 57, and IEEE 118 bus systems. The suggested k-nearest neighbor (KNN) classifier increases the accuracy of power system security assessments categorization. A fuzzy logic approach was also investigated and implemented for the IEEE 14 bus test system to forecast the aforementioned five classifications.

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