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Zhao, Yongjun, Chow, Sherman S.M..  2017.  Updatable Block-Level Message-Locked Encryption. Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security. :449–460.
Deduplication is a widely used technique for reducing storage space of cloud service providers. Yet, it is unclear how to support deduplication of encrypted data securely until the study of Bellareetal on message-locked encryption (Eurocrypt 2013). Since then, there are many improvements such as strengthening its security, reducing client storage, etc. While updating a (shared) file is common, there is little attention on how to efficiently update large encrypted files in a remote storage with deduplication. To modify even a single bit, existing solutions require the trivial and expensive way of downloading and decrypting the large ciphertext. We initiate the study of updatable block-level message-locked encryption. We propose a provably secure construction that is efficiently updatable with O(logtextbarFtextbar) computational cost, where textbarFtextbar is the file size. It also supports proof-of-ownership, a nice feature which protects storage providers from being abused as a free content distribution network.
Zhang, Lin, Zhang, Zhenfeng, Hu, Xuexian.  2016.  UC-secure Two-Server Password-Based Authentication Protocol and Its Applications. Proceedings of the 11th ACM on Asia Conference on Computer and Communications Security. :153–164.

A two-server password-based authentication (2PA) protocol is a special kind of authentication primitive that provides additional protection for the user's password. Through a 2PA protocol, a user can distribute his low-entropy password between two authentication servers in the initialization phase and authenticate himself merely via a matching password in the login phase. No single server can learn any information about the user's password, nor impersonate the legitimate user to authenticate to the honest server. In this paper, we first formulate and realize the security definition of two-server password-based authentication in the well-known universal composability (UC) framework, which thus provides desirable properties such as composable security. We show that our construction is suitable for the asymmetric communication model in which one server acts as the front-end server interacting directly with the user and the other stays backstage. Then, we show that our protocol could be easily extended to more complicate password-based cryptographic protocols such as two-server password-authenticated key exchange (2PAKE) and two-server password-authenticated secret sharing (2PASS), which enjoy stronger security guarantees and better efficiency performances in comparison with the existing schemes.

Zhang, Caixia, Bai, Gang.  2018.  Using Hybrid Features of QR Code to Locate and Track in Augmented Reality. Proceedings of the 2018 International Conference on Information Science and System. :273–279.
Augmented Reality (AR) is a technique which seamlessly integrate virtual 3D models into the image of the real scenario in real time. Using the QR code as the identification mark, an algorithm is proposed to extract the virtual straight line of QR code and to locate and track the camera based on the hybrid features, thus it avoids the possibility of failure when locating and tracking only by feature points. The experimental results show that the method of combining straight lines with feature points is better than that of using only straight lines or feature points. Further, an AR (Augmented Reality) system is developed.
Zahilah, R., Tahir, F., Zainal, A., Abdullah, A. H., Ismail, A. S..  2017.  Unified Approach for Operating System Comparisons with Windows OS Case Study. 2017 IEEE Conference on Application, Information and Network Security (AINS). :91–96.

The advancement in technology has changed how people work and what software and hardware people use. From conventional personal computer to GPU, hardware technology and capability have dramatically improved so does the operating systems that come along. Unfortunately, current industry practice to compare OS is performed with single perspective. It is either benchmark the hardware level performance or performs penetration testing to check the security features of an OS. This rigid method of benchmarking does not really reflect the true performance of an OS as the performance analysis is not comprehensive and conclusive. To illustrate this deficiency, the study performed hardware level and operational level benchmarking on Windows XP, Windows 7 and Windows 8 and the results indicate that there are instances where Windows XP excels over its newer counterparts. Overall, the research shows Windows 8 is a superior OS in comparison to its predecessors running on the same hardware. Furthermore, the findings also show that the automated benchmarking tools are proved less efficient benchmark systems that run on Windows XP and older OS as they do not support DirectX 11 and other advanced features that the hardware supports. There lies the need to have a unified benchmarking approach to compare other aspects of OS such as user oriented tasks and security parameters to provide a complete comparison. Therefore, this paper is proposing a unified approach for Operating System (OS) comparisons with the help of a Windows OS case study. This unified approach includes comparison of OS from three aspects which are; hardware level, operational level performance and security tests.

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Yu, M., Halak, B., Zwolinski, M..  2019.  Using Hardware Performance Counters to Detect Control Hijacking Attacks. 2019 IEEE 4th International Verification and Security Workshop (IVSW). :1–6.

Code reuse techniques can circumvent existing security measures. For example, attacks such as Return Oriented Programming (ROP) use fragments of the existing code base to create an attack. Since this code is already in the system, the Data Execution Prevention methods cannot prevent the execution of this reorganised code. Existing software-based Control Flow Integrity can prevent this attack, but the overhead is enormous. Most of the improved methods utilise reduced granularity in exchange for a small performance overhead. Hardware-based detection also faces the same performance overhead and accuracy issues. Benefit from HPC's large-area loading on modern CPU chips, we propose a detection method based on the monitoring of hardware performance counters, which is a lightweight system-level detection for malicious code execution to solve the restrictions of other software and hardware security measures, and is not as complicated as Control Flow Integrity.

Yazicigil, R. T., Nadeau, P., Richman, D., Juvekar, C., Vaidya, K., Chandrakasan, A. P..  2018.  Ultra-Fast Bit-Level Frequency-Hopping Transmitter for Securing Low-Power Wireless Devices. 2018 IEEE Radio Frequency Integrated Circuits Symposium (RFIC). :176-179.

Current BLE transmitters are susceptible to selective jamming due to long dwell times in a channel. To mitigate these attacks, we propose physical-layer security through an ultra-fast bit-level frequency-hopping (FH) scheme by exploiting the frequency agility of bulk acoustic wave resonators (BAW). Here we demonstrate the first integrated bit-level FH transmitter (TX) that hops at 1$μ$s period and uses data-driven random dynamic channel selection to enable secure wireless communications with additional data encryption. This system consists of a time-interleaved BAW-based TX implemented in 65nm CMOS technology with 80MHz coverage in the 2.4GHz ISM band and a measured power consumption of 10.9mW from 1.1V supply.

Yang, Zihan, Mi, Zeyu, Xia, Yubin.  2019.  Undertow: An Intra-Kernel Isolation Mechanism for Hardware-Assisted Virtual Machines. 2019 IEEE International Conference on Service-Oriented System Engineering (SOSE). :257—2575.
The prevalence of Cloud Computing has appealed many users to put their business into low-cost and flexible cloud servers instead of bare-metal machines. Most virtual machines in the cloud run commodity operating system(e.g., linux), and the complexity of such operating systems makes them more bug-prone and easier to be compromised. To mitigate the security threats, previous works attempt to mediate and filter system calls, transform all unpopular paths into popular paths, or implement a nested kernel along with the untrusted outter kernel to enforce certain security policies. However, such solutions only enforce read-only protection or assume that popular paths in the kernel to contain almost no bug, which is not always the case in the real world. To overcome their shortcomings and combine their advantages as much as possible, we propose a hardware-assisted isolation mechanism that isolates untrusted part of the kernel. To achieve isolation, we prepare multiple restricted Extended Page Table (EPT) during boot time, each of which has certain critical data unmapped from it so that the code executing in the isolated environment could not access sensitive data. We leverage the VMFUNC instruction already available in recent Intel processors to directly switch to another pre-defined EPT inside guest virtual machine without trapping into the underlying hypervisor, which is faster than the traditional trap-and-emulate procedure. The semantic gap is minimized and real-time check is achieved by allowing EPT violations to be converted to Virtualization Exception (VE), which could be handled inside guest kernel in non-root mode. Our preliminary evaluation shows that with hardware virtualization feature, we are able to run the untrusted code in an isolated environment with negligible overhead.
Yang, Shouguo, Shi, Zhiqiang, Zhang, Guodong, Li, Mingxuan, Ma, Yuan, Sun, Limin.  2019.  Understand Code Style: Efficient CNN-Based Compiler Optimization Recognition System. ICC 2019 - 2019 IEEE International Conference on Communications (ICC). :1–6.
Compiler optimization level recognition can be applied to vulnerability discovery and binary analysis. Due to the exists of many different compilation optimization options, the difference in the contents of the binary file is very complicated. There are thousands of compiler optimization algorithms and multiple different processor architectures, so it is very difficult to manually analyze binary files and recognize its compiler optimization level with rules. This paper first proposes a CNN-based compiler optimization level recognition model: BinEye. The system extracts semantic and structural differences and automatically recognize the compiler optimization levels. The model is designed to be very suitable for binary file processing and is easy to understand. We built a dataset containing 80028 binary files for the model training and testing. Our proposed model achieves an accuracy of over 97%. At the same time, BinEye is a fully CNN-based system and it has a faster forward calculation speed, at least 8 times faster than the normal RNN-based model. Through our analysis of the model output, we successfully found the difference in assembly codes caused by the different compiler optimization level. This means that the model we proposed is interpretable. Based on our model, we propose a method to analyze the code differences caused by different compiler optimization levels, which has great guiding significance for analyzing closed source compilers and binary security analysis.
Yang, Bo, He, Suining, Chan, S.-H. Gary.  2016.  Updating Wireless Signal Map with Bayesian Compressive Sensing. Proceedings of the 19th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems. :310–317.

In a wireless system, a signal map shows the signal strength at different locations termed reference points (RPs). As access points (APs) and their transmission power may change over time, keeping an updated signal map is important for applications such as Wi-Fi optimization and indoor localization. Traditionally, the signal map is obtained by a full site survey, which is time-consuming and costly. We address in this paper how to efficiently update a signal map given sparse samples randomly crowdsourced in the space (e.g., by signal monitors, explicit human input, or implicit user participation). We propose Compressive Signal Reconstruction (CSR), a novel learning system employing Bayesian compressive sensing (BCS) for online signal map update. CSR does not rely on any path loss model or line of sight, and is generic enough to serve as a plug-in of any wireless system. Besides signal map update, CSR also computes the estimation error of signals in terms of confidence interval. CSR models the signal correlation with a kernel function. Using it, CSR constructs a sensing matrix based on the newly sampled signals. The sensing matrix is then used to compute the signal change at all the RPs with any BCS algorithm. We have conducted extensive experiments on CSR in our university campus. Our results show that CSR outperforms other state-of-the-art algorithms by a wide margin (reducing signal error by about 30% and sampling points by 20%).

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Xusheng Xiao, NEC Laboratories America, Nikolai Tillmann, Microsoft Research, Manuel Fahndrich, Microsoft Research, Jonathan de Halleux, Microsoft Research, Michal Moskal, Microsoft Research, Tao Xie, University of Illinois at Urbana-Champaign.  2015.  User-Aware Privacy Control via Extended Static-Information-Flow Analysis. Automated Software Engineering Journal. 22(3)

Applications in mobile marketplaces may leak private user information without notification. Existing mobile platforms provide little information on how applications use private user data, making it difficult for experts to validate appli- cations and for users to grant applications access to their private data. We propose a user-aware-privacy-control approach, which reveals how private information is used inside applications. We compute static information flows and classify them as safe/un- safe based on a tamper analysis that tracks whether private data is obscured before escaping through output channels. This flow information enables platforms to provide default settings that expose private data for only safe flows, thereby preserving privacy and minimizing decisions required from users. We build our approach into TouchDe- velop, an application-creation environment that allows users to write scripts on mobile devices and install scripts published by other users. We evaluate our approach by studying 546 scripts published by 194 users, and the results show that our approach effectively reduces the need to make access-granting choices to only 10.1 % (54) of all scripts. We also conduct a user survey that involves 50 TouchDevelop users to assess the effectiveness and usability of our approach. The results show that 90 % of the users consider our approach useful in protecting their privacy, and 54 % prefer our approach over other privacy-control approaches.

Xie, Yuanpeng, Jiang, Yixin, Liao, Runfa, Wen, Hong, Meng, Jiaxiao, Guo, Xiaobin, Xu, Aidong, Guan, Zewu.  2015.  User Privacy Protection for Cloud Computing Based Smart Grid. 2015 IEEE/CIC International Conference on Communications in China - Workshops (CIC/ICCC). :7–11.

The smart grid aims to improve the efficiency, reliability and safety of the electric system via modern communication system, it's necessary to utilize cloud computing to process and store the data. In fact, it's a promising paradigm to integrate smart grid into cloud computing. However, access to cloud computing system also brings data security issues. This paper focuses on the protection of user privacy in smart meter system based on data combination privacy and trusted third party. The paper demonstrates the security issues for smart grid communication system and cloud computing respectively, and illustrates the security issues for the integration. And we introduce data chunk storage and chunk relationship confusion to protect user privacy. We also propose a chunk information list system for inserting and searching data.

Xie, J., Zhang, M., Ma, Y..  2019.  Using Format Migration and Preservation Metadata to Support Digital Preservation of Scientific Data. 2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS). :1—6.

With the development of e-Science and data intensive scientific discovery, it needs to ensure scientific data available for the long-term, with the goal that the valuable scientific data should be discovered and re-used for downstream investigations, either alone, or in combination with newly generated data. As such, the preservation of scientific data enables that not only might experiment be reproducible and verifiable, but also new questions can be raised by other scientists to promote research and innovation. In this paper, we focus on the two main problems of digital preservation that are format migration and preservation metadata. Format migration includes both format verification and object transformation. The system architecture of format migration and preservation metadata is presented, mapping rules of object transformation are analyzed, data fixity and integrity and authenticity, digital signature and so on are discussed and an example is shown in detail.

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Weining Yang, Aiping Xiong, Jing Chen, Robert W. Proctor, Ninghui Li.  2017.  Use of Phishing Training to Improve Security Warning Compliance: Evidence from a Field Experiment.

The current approach to protect users from phishing attacks is to display a warning when the webpage is considered suspicious. We hypothesize that users are capable of making correct informed decisions when the warning also conveys the reasons why it is displayed. We chose to use traffic rankings of domains, which can be easily described to users, as a warning trigger and evaluated the effect of the phishing warning message and phishing training. The evaluation was conducted in a field experiment. We found that knowledge gained from the training enhances the effectiveness of phishing warnings, as the number of participants being phished was reduced. However, the knowledge by itself was not sufficient to provide phishing protection. We suggest that integrating training in the warning interface, involving traffic ranking in phishing detection, and explaining why warnings are generated will improve current phishing defense.

Wehbe, Taimour, Mooney, Vincent J., Keezer, David, Inan, Omer T., Javaid, Abdul Qadir.  2017.  Use of Analog Signatures for Hardware Trojan Detection. Proceedings of the 14th FPGAworld Conference. :15–22.
Malicious Hardware Trojans can corrupt data which if undetected may cause serious harm. We propose a technique where characteristics of the data itself are used to detect Hardware Trojan (HT) attacks. In particular, we use a two-chip approach where we generate a data "signature" in analog and test for the signature in a partially reconfigurable digital microchip where the HT may attack. This paper presents an overall signature-based HT detection architecture and case study for cardiovascular signals used in medical device technology. Our results show that with minimal performance and area overhead, the proposed architecture is able to detect HT attacks on primary data inputs as well as on multiple modules of the design.
Wang, W., Xuan, S., Yang, W., Chen, Y..  2019.  User Credibility Assessment Based on Trust Propagation in Microblog. 2019 Computing, Communications and IoT Applications (ComComAp). :270—275.

Nowadays, Microblog has become an important online social networking platform, and a large number of users share information through Microblog. Many malicious users have released various false news driven by various interests, which seriously affects the availability of Microblog platform. Therefore, the evaluation of Microblog user credibility has become an important research issue. This paper proposes a microblog user credibility evaluation algorithm based on trust propagation. In view of the high consumption and low precision caused by malicious users' attacking algorithms and manual selection of seed sets by establishing false social relationships, this paper proposes two optimization strategies: pruning algorithm based on social activity and similarity and based on The seed node selection algorithm of clustering. The pruning algorithm can trim off the attack edges established by malicious users and normal users. The seed node selection algorithm can efficiently select the highly available seed node set, and finally use the user social relationship graph to perform the two-way propagation trust scoring, so that the low trusted user has a lower trusted score and thus identifies the malicious user. The related experiments verify the effectiveness of the trustworthiness-based user credibility evaluation algorithm in the evaluation of Microblog user credibility.

Wang, P., Lin, W. H., Chao, W. J., Chao, K. M., Lo, C. C..  2015.  Using Dynamic Taint Approach for Malware Threat. 2015 IEEE 12th International Conference on e-Business Engineering. :408–416.

Most existing approaches focus on examining the values are dangerous for information flow within inter-suspicious modules of cloud applications (apps) in a host by using malware threat analysis, rather than the risk posed by suspicious apps were connected to the cloud computing server. Accordingly, this paper proposes a taint propagation analysis model incorporating a weighted spanning tree analysis scheme to track data with taint marking using several taint checking tools. In the proposed model, Android programs perform dynamic taint propagation to analyse the spread of and risks posed by suspicious apps were connected to the cloud computing server. In determining the risk of taint propagation, risk and defence capability are used for each taint path for assisting a defender in recognising the attack results against network threats caused by malware infection and estimate the losses of associated taint sources. Finally, a case of threat analysis of a typical cyber security attack is presented to demonstrate the proposed approach. Our approach verified the details of an attack sequence for malware infection by incorporating a finite state machine (FSM) to appropriately reflect the real situations at various configuration settings and safeguard deployment. The experimental results proved that the threat analysis model allows a defender to convert the spread of taint propagation to loss and practically estimate the risk of a specific threat by using behavioural analysis with real malware infection.

Wang, Kai, Zhao, Yude, liu, Shugang, Tong, Xiangrong.  2018.  On the urgency of implementing Interest NACK into CCN: from the perspective of countering advanced interest flooding attacks. IET Networks. 7:136–140.
Content centric networking (CCN) where content/named data as the first entity has become one of the most promising architectures for the future Internet. To achieve better security, the Interest NACK mechanism is introduced into CCN; however, it has not attracted enough attention and most of the CCN architectures do not embed Interest NACK until now. This study focuses on analysing the urgency of implementing Interest NACK into CCN, by designing a novel network threat named advanced interest flooding attack (AIFA) to attack CCN, which can not only exhaust the pending interest table (PIT) resource of each involved router just as normal interest flooding attack (IFA), but also keep each PIT entry unexpired until it finishes, making it harder to detect and more harmful when compared with the normal IFA. Specifically, the damage of AIFA on CCN architecture with and without Interest NACK is evaluated and analysed, compared with normal IFA, and then the urgency of implementing Interest NACK is highlighted.
Wang, Gang, Zhang, Xinyi, Tang, Shiliang, Zheng, Haitao, Zhao, Ben Y..  2016.  Unsupervised Clickstream Clustering for User Behavior Analysis. Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. :225–236.

Online services are increasingly dependent on user participation. Whether it's online social networks or crowdsourcing services, understanding user behavior is important yet challenging. In this paper, we build an unsupervised system to capture dominating user behaviors from clickstream data (traces of users' click events), and visualize the detected behaviors in an intuitive manner. Our system identifies "clusters" of similar users by partitioning a similarity graph (nodes are users; edges are weighted by clickstream similarity). The partitioning process leverages iterative feature pruning to capture the natural hierarchy within user clusters and produce intuitive features for visualizing and understanding captured user behaviors. For evaluation, we present case studies on two large-scale clickstream traces (142 million events) from real social networks. Our system effectively identifies previously unknown behaviors, e.g., dormant users, hostile chatters. Also, our user study shows people can easily interpret identified behaviors using our visualization tool.

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Villalobos, J. J., Rodero, Ivan, Parashar, Manish.  2017.  An Unsupervised Approach for Online Detection and Mitigation of High-Rate DDoS Attacks Based on an In-Memory Distributed Graph Using Streaming Data and Analytics. Proceedings of the Fourth IEEE/ACM International Conference on Big Data Computing, Applications and Technologies. :103–112.

A Distributed Denial of Service (DDoS) attack is an attempt to make an online service, a network, or even an entire organization, unavailable by saturating it with traffic from multiple sources. DDoS attacks are among the most common and most devastating threats that network defenders have to watch out for. DDoS attacks are becoming bigger, more frequent, and more sophisticated. Volumetric attacks are the most common types of DDoS attacks. A DDoS attack is considered volumetric, or high-rate, when within a short period of time it generates a large amount of packets or a high volume of traffic. High-rate attacks are well-known and have received much attention in the past decade; however, despite several detection and mitigation strategies have been designed and implemented, high-rate attacks are still halting the normal operation of information technology infrastructures across the Internet when the protection mechanisms are not able to cope with the aggregated capacity that the perpetrators have put together. With this in mind, the present paper aims to propose and test a distributed and collaborative architecture for online high-rate DDoS attack detection and mitigation based on an in-memory distributed graph data structure and unsupervised machine learning algorithms that leverage real-time streaming data and analytics. We have successfully tested our proposed mechanism using a real-world DDoS attack dataset at its original rate in pursuance of reproducing the conditions of an actual large scale attack.

Viet, Hung Nguyen, Van, Quan Nguyen, Trang, Linh Le Thi, Nathan, Shone.  2018.  Using Deep Learning Model for Network Scanning Detection. Proceedings of the 4th International Conference on Frontiers of Educational Technologies. :117-121.

In recent years, new and devastating cyber attacks amplify the need for robust cybersecurity practices. Preventing novel cyber attacks requires the invention of Intrusion Detection Systems (IDSs), which can identify previously unseen attacks. Many researchers have attempted to produce anomaly - based IDSs, however they are not yet able to detect malicious network traffic consistently enough to warrant implementation in real networks. Obviously, it remains a challenge for the security community to produce IDSs that are suitable for implementation in the real world. In this paper, we propose a new approach using a Deep Belief Network with a combination of supervised and unsupervised machine learning methods for port scanning attacks detection - the task of probing enterprise networks or Internet wide services, searching for vulnerabilities or ways to infiltrate IT assets. Our proposed approach will be tested with network security datasets and compared with previously existing methods.

van Thuan, D., Butkus, P., van Thanh, D..  2014.  A User Centric Identity Management for Internet of Things. IT Convergence and Security (ICITCS), 2014 International Conference on. :1-4.

In the future Internet of Things, it is envisioned that things are collaborating to serve people. Unfortunately, this vision could not be realised without relations between things and people. To solve the problem this paper proposes a user centric identity management system that incorporates user identity, device identity and the relations between them. The proposed IDM system is user centric and allows device authentication and authorization based on the user identity. A typical compelling use case of the proposed solution is also given.

van der Veen, Rosa, Hakkerainen, Viola, Peeters, Jeroen, Trotto, Ambra.  2018.  Understanding Transformations Through Design: Can Resilience Thinking Help? Proceedings of the Twelfth International Conference on Tangible, Embedded, and Embodied Interaction. :694–702.
The interaction design community increasingly addresses how digital technologies may contribute to societal transformations. This paper aims at understanding transformation ignited by a particular constructive design research project. This transformation will be discussed and analysed using resilience thinking, an established approach within sustainability science. By creating a common language between these two disciplines, we start to identify what kind of transformation took place, what factors played a role in the transformation, and which transformative qualities played a role in creating these factors. Our intention is to set out how the notion of resilience might provide a new perspective to understand how constructive design research may produce results that have a sustainable social impact. The findings point towards ways in which these two different perspectives on transformation the analytical perspective of resilience thinking and the generative perspective of constructive design research - may become complementary in both igniting and understanding transformations.
Vaarandi, R., Pihelgas, M..  2014.  Using Security Logs for Collecting and Reporting Technical Security Metrics. Military Communications Conference (MILCOM), 2014 IEEE. :294-299.

During recent years, establishing proper metrics for measuring system security has received increasing attention. Security logs contain vast amounts of information which are essential for creating many security metrics. Unfortunately, security logs are known to be very large, making their analysis a difficult task. Furthermore, recent security metrics research has focused on generic concepts, and the issue of collecting security metrics with log analysis methods has not been well studied. In this paper, we will first focus on using log analysis techniques for collecting technical security metrics from security logs of common types (e.g., Network IDS alarm logs, workstation logs, and Net flow data sets). We will also describe a production framework for collecting and reporting technical security metrics which is based on novel open-source technologies for big data.
 

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Uzhga-Rebrov, O., Kuleshova, G..  2020.  Using Singular Value Decomposition to Reduce Dimensionality of Initial Data Set. 2020 61st International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS). :1–4.
The purpose of any data analysis is to extract essential information implicitly present in the data. To do this, it often seems necessary to transform the initial data into a form that allows one to identify and interpret the essential features of their structure. One of the most important tasks of data analysis is to reduce the dimension of the original data. The paper considers an approach to solving this problem based on singular value decomposition (SVD).