literature review information systems

A Systematic Literature Review of Health Information Systems for Healthcare

Affiliations.

• 1 ICT and Society Research Group, Durban University of Technology, Durban 4001, South Africa.
• 2 Department of Information and Corporate Management, Durban University of Technology, Durban 4001, South Africa.
• 3 Centre for Transformative Agricultural and Food Systems, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Pietermaritzburg 3209, South Africa.
• PMID: 37046884
• PMCID: PMC10094672
• DOI: 10.3390/healthcare11070959

Health information system deployment has been driven by the transformation and digitalization currently confronting healthcare. The need and potential of these systems within healthcare have been tremendously driven by the global instability that has affected several interrelated sectors. Accordingly, many research studies have reported on the inadequacies of these systems within the healthcare arena, which have distorted their potential and offerings to revolutionize healthcare. Thus, through a comprehensive review of the extant literature, this study presents a critique of the health information system for healthcare to supplement the gap created as a result of the lack of an in-depth outlook of the current health information system from a holistic slant. From the studies, the health information system was ascertained to be crucial and fundament in the drive of information and knowledge management for healthcare. Additionally, it was asserted to have transformed and shaped healthcare from its conception despite its flaws. Moreover, research has envisioned that the appraisal of the current health information system would influence its adoption and solidify its enactment within the global healthcare space, which is highly demanded.

Keywords: health information system; healthcare; information system; knowledge management.

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Computer Science > Cryptography and Security

Title: large language models for cyber security: a systematic literature review.

Abstract: The rapid advancement of Large Language Models (LLMs) has opened up new opportunities for leveraging artificial intelligence in various domains, including cybersecurity. As the volume and sophistication of cyber threats continue to grow, there is an increasing need for intelligent systems that can automatically detect vulnerabilities, analyze malware, and respond to attacks. In this survey, we conduct a comprehensive review of the literature on the application of LLMs in cybersecurity (LLM4Security). By comprehensively collecting over 30K relevant papers and systematically analyzing 127 papers from top security and software engineering venues, we aim to provide a holistic view of how LLMs are being used to solve diverse problems across the cybersecurity domain. Through our analysis, we identify several key findings. First, we observe that LLMs are being applied to a wide range of cybersecurity tasks, including vulnerability detection, malware analysis, network intrusion detection, and phishing detection. Second, we find that the datasets used for training and evaluating LLMs in these tasks are often limited in size and diversity, highlighting the need for more comprehensive and representative datasets. Third, we identify several promising techniques for adapting LLMs to specific cybersecurity domains, such as fine-tuning, transfer learning, and domain-specific pre-training. Finally, we discuss the main challenges and opportunities for future research in LLM4Security, including the need for more interpretable and explainable models, the importance of addressing data privacy and security concerns, and the potential for leveraging LLMs for proactive defense and threat hunting. Overall, our survey provides a comprehensive overview of the current state-of-the-art in LLM4Security and identifies several promising directions for future research.

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Deep learning based active image steganalysis: a review

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• Published: 27 November 2023
• Volume 15 , pages 786–799, ( 2024 )

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• Punam Bedi 1 ,
• Anuradha Singhal   ORCID: orcid.org/0000-0002-0818-6597 1 &
• Veenu Bhasin 1  

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Steganalysis plays a vital role in cybersecurity in today’s digital era where exchange of malicious information can be done easily across web pages. Steganography techniques are used to hide data in an object where the existence of hidden information is also obscured. Steganalysis is the process for detection of steganography within an object and can be categorized as active and passive steganalysis. Passive steganalysis tries to classify a given object as a clean or modified object. Active steganalysis aims to extract more details about hidden contents such as length of embedded message, region of inserted message, key used for embedding, required by cybersecurity experts for comprehensive analysis. Images being a viable source of exchange of information in the era of internet, social media are the most susceptible source for such transmission. Many researchers have worked and developed techniques required to detect and alert about such counterfeit exchanges over the internet. Literature present in passive and active image steganalysis techniques, addresses these issues by detecting and unveiling details of such obscured communication respectively. This paper provides a systematic and comprehensive review of work done on active image steganalysis techniques using deep learning techniques. This review will be helpful to the new researchers to become aware and build a strong foundation of literature present in active image steganalysis using deep learning techniques. The paper also includes various steganographic algorithms, dataset and performance evaluation metrics used in literature. Open research challenges and possible future research directions are also discussed in the paper.

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Bedi, P., Singhal, A. & Bhasin, V. Deep learning based active image steganalysis: a review. Int J Syst Assur Eng Manag 15 , 786–799 (2024). https://doi.org/10.1007/s13198-023-02203-9

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Received : 25 June 2023

Revised : 02 October 2023

Accepted : 04 November 2023

Published : 27 November 2023

Issue Date : March 2024

DOI : https://doi.org/10.1007/s13198-023-02203-9

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