Data security crisis in universities: identification of key factors affecting data breach incidents

Jiang, Lin; Xiao, Wei; Zhang, Chong

doi:10.1057/s41599-023-01757-0

Cited by 12 publications

(3 citation statements)

References 69 publications

(76 reference statements)

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“…The final and most crucial dimension pertains to the vulnerability of significant data, which encompasses the security, privacy, and technological risks that arise from the collection of personalized data through Internet applications, social networks, and IoT devices. These vulnerabilities are attributed to gaps in security and privacy, and the lack of established standards in BID technologies, processes, and management [95].…”

Section: Big Image Data 15vs Modelmentioning

confidence: 99%

Big Image Data 15Vs Model for Intelligent Data Ocean of Multimedia Things

Mahabadi,

Abdolazimi

2024

Preprint

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The classification of large image data using traditional methods has been based on changes in image gray features, extraction of edge and contour feature information, or conversion between image coordinate sets. However, due to the growth of new image size and big image data (BID) in real-time multimedia communication and future online big data (BD) applications, these methods have become increasingly complex and resulting in poor real-time performance. These complex methods suffer from complex algorithms, massive data communication, slow processing speed, unintelligent predictive modeling, weak data classification, limited accuracy, uncleaned usage data, combined destructive artificial and natural noise, exchanging data values over time, and exhausting updating data for accurate operation of media storage. In the era of the Internet of Multimedia Things (IoMT), most modern devices can accurately capture vast amounts of observational data in the form of valuable images, text, and acoustic recordings. This reliable data must be kept valid for local data processing at different times and for globally extracting knowledge from information at various data levels to address forthcoming challenges related to big image data. The challenges of secure communication require accurate, reliable, fast, and effective information gathering for local decision-making and global knowledge mining of BID to support cyber-physical systems for speedy data virtualization of smart city devices. Complex and uncontrollable problems persist on the outskirts of grids, fogs, and cloud networks regarding data cleansing and privacy protection for intelligent data ocean management. This study developed a new 15Vs model empirically to examine distributed big image data processing based on a modern BID connectivity approach. The model accurately extracts textured features from visible data images and overcomes a unique set of key challenges. The model introduces a strategic analyzer, uses intelligent local agents, and recruits clever global bots to provide a suitable platform for generous support of bot-oriented BD processing. This instantly forms a hierarchical data level for better data acquisition and cleansing, safe privacy protection, suitable information diffusion, and speedy knowledge extraction. Our study presents a novel definition of BID to address the ultimate challenges of data management in a high-risk environment for further big image data modeling research needs.

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Section: Big Image Data 15vs Modelmentioning

confidence: 99%

Big Image Data 15Vs Model for Intelligent Data Ocean of Multimedia Things

Mahabadi,

Abdolazimi

2024

Preprint

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“…As technology advances and more scientific processes and data are digitized, the need for robust cybersecurity measures becomes increasingly important. Cyberattacks and data breaches have been reported in higher education institutions and universities [3]. For example, in 2014 the University of Maryland network was hacked and the personal data of over 300,000 students were illegally accessed, with a cost for the university of over 6 million USD.…”

Section: Introductionmentioning

confidence: 99%

A high throughput Intrusion Detection System (IDS) to enhance the security of data transmission among research centers

Grossi,

Alfonsi,

Prandini

et al. 2023

J. Inst.

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Data breaches and cyberattacks represent a severe problem in higher education institutions and universities that can result in illegal access to sensitive information and data loss. To enhance the security of data transmission, Intrusion Prevention Systems (IPS, i.e., firewalls) and Intrusion Detection Systems (IDS, i.e., packet sniffers) are used to detect potential threats in the exchanged data. IPSs and IDSs are usually designed as software programs running on a server machine. However, when the speed of exchanged data is too high, this solution can become unreliable. In this case, IPSs and IDSs designed on a real hardware platform, such as ASICs and FPGAs, represent a more reliable solution. This paper presents a packet sniffer that was designed using a commercial FPGA development board. The system can support a data throughput of 10 Gbit/s with preliminary results showing that the speed of data transmission can be reliably extended to 100 Gbit/s. The designed system is highly configurable by the user and can enhance the data protection of information transmitted using the Ethernet protocol. It is particularly suited for the security of universities and research centers, where point-to-point network connections are dominant and large amount of sensitive data are shared among different hosts.

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“…Textual data can be found in various forms, such as news articles, blogs, social media posts, and official documents. To illustrate the magnitude of textual data on the internet, as of January 2022, Google processed more than 5.6 billion searches per day, each search encompassing text in various languages and topics [1]. The openness of information on the internet has made the compilation and management of textual data an increasingly complex task.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Spell Correction in Bahasa Indonesia: Peter Norvig, Lstm, and N-Gram

Kusuma,

Ratnasari

2023

jiko

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This study conducts a comprehensive comparison of spell-checking methods in Bahasa Indonesia, specifically focusing on three approaches: Peter Norvig's method, Long Short-Term Memory (LSTM), and N-gram. The primary metric for evaluation is the accuracy in correcting spelling errors. Notably, Peter Norvig's method outperforms the others, with N-gram following closely, and LSTM trailing behind. The study draws valuable insights that contribute to the enhancement of spelling correction accuracy in the Bahasa Indonesia language. To carry out the evaluation, the research employs SPECIL data (Spell Error Corpus for Indonesian Language), which includes documents with various error types such as insertion, deletion, transposition, and substitution. The testing dataset consists of 150 words, aligning with the 150-word corpus references from the 'Leipzig Corpora Collection' used for Peter Norvig's and N-gram methods. It's noteworthy that the LSTM method utilizes a reference dataset from SPECIL, comprising 150 data points and specifically focusing on insertion errors for the test data. This research provides valuable insights for researchers, developers, and language technology enthusiasts seeking to refine spell-checking techniques for the Bahasa Indonesia language. By leveraging diverse error types and a standardized testing dataset, the study aims to contribute to the continual improvement of spell-checking tools

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Data security crisis in universities: identification of key factors affecting data breach incidents

Cited by 12 publications

References 69 publications

Big Image Data 15Vs Model for Intelligent Data Ocean of Multimedia Things

Big Image Data 15Vs Model for Intelligent Data Ocean of Multimedia Things

A high throughput Intrusion Detection System (IDS) to enhance the security of data transmission among research centers

Comparison of Spell Correction in Bahasa Indonesia: Peter Norvig, Lstm, and N-Gram

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