A Proposal of Rule-Based Hybrid Intrusion Detection System Through Analysis of Rule-Based Supervised Classifiers

Panigrahi, Ranjit; Borah, Samarjeet; Mishra, Debahuti

doi:10.1007/978-981-15-6202-0_63

Cited by 2 publications

(2 citation statements)

References 27 publications

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“…In future research, it would be better to use machine learning that is better at image processing and recognizing bacterial shapes. [29], [30]…”

Section: Discussionmentioning

confidence: 99%

The Enhancing Accuracy in Mycobacterium Tuberculosis Counting on Acid-Fast Bacteria Sputum through Microscope Camera and Image Processing with Thresholding Method

Sholichuddin,

T.P

2024

j.teknokes

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Tuberculosis (TB) is a chronic infectious illness. Pulmonary TB is still a global threat and concern. Currently, TB is among the top 10 causes of death in the world in 2015 and it is estimated that there are around 10.4 million new cases of TB in the world. 6 countries account for around 60% of new cases in the world: India, Indonesia, China, Nigeria, Pakistan, and South Africa. Globally, the TB death rate was around 22% of total world deaths from 2000 to 2015. This study introduces an innovative tool, the "Microscope Camera for Enhanced Mycobacterium Tuberculosis Counting in Acid-Fast Bacteria Sputum through Image Processing with Thresholding Method," designed to revolutionize the examination of tuberculosis (TB) samples. In response to the global threat of Pulmonary Tuberculosis, the tool employs a state-of-the-art CMOS-HD (Complementary metal-oxide-semiconductor High Definition) camera module and leverages the Python application for efficient image processing. The primary objective is to address the limitations faced by conventional diagnostic methods, particularly the challenges associated with the Rapid Molecular Test Method due to its dependence on expensive equipment, thereby making it less feasible for implementation in resource-constrained public health centers. The study underscores the persistent threat of TB as a global health concern, emphasizing its prevalence, with approximately 10.4 million new cases worldwide. Focusing on Ziehl-Neelsen stain sputum samples, the research seeks to elevate the accuracy of TB diagnosis by proposing a streamlined approach. This involves integrating a digital microscope system with a CMOS-HD camera module and utilizing advanced image processing techniques, specifically the Python Thresholding method. In the methodology, the research outlines a comprehensive data collection and analysis process. Mechanical and block diagrams illustrate the design of the module, while the system flow diagram elucidates the sequence of steps involved in TB sample analysis. The data analysis section employs metrics such as average, percentage error, and success percentage, providing a quantitative assessment of the system's performance. The results reveal an encouraging average system accuracy of 85.30%, demonstrating the potential of the developed tool to enhance the diagnostic process. However, the study transparently acknowledges areas for improvement, particularly in addressing discrepancies between manual and system calculations. The conclusion emphasizes the tool's promising role in assisting healthcare workers in detecting and counting TB bacteria in sputum, underscoring the need for ongoing refinements to achieve a definitive 100% accuracy and eliminate the possibility of misdiagnoses.

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“…In future research, it would be better to use machine learning that is better at image processing and recognizing bacterial shapes. [29], [30]…”

Section: Discussionmentioning

confidence: 99%

The Enhancing Accuracy in Mycobacterium Tuberculosis Counting on Acid-Fast Bacteria Sputum through Microscope Camera and Image Processing with Thresholding Method

Sholichuddin,

T.P

2024

j.teknokes

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“…In Reference 76, 12 rule‐based classifiers were evaluated in a high‐class imbalance scenario to determine the best classifier that could be used as the IDS's base learner. Three class imbalance incursion datasets were evaluated to generate a class imbalance scenario.…”

Section: Intrusion Detection Systemsmentioning

confidence: 99%

A systematic literature review of intrusion detection systems in the cloud‐basedIoTenvironments

Luo¹,

Chen²,

Mohammed

2022

Concurrency and Computation

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As the number of Internet of Things (IoT) subscribers, services, and applications grows, there is a pressing need for a reliable and lightweight security solution that can be used in IoT contexts. Also, due to the open nature of cloud computing, safety concerns are always challenging. One potential solution for this problem is an intrusion detection system (IDS). An ID that utilizes a machine learning method is gaining popularity since it has the benefit of automatically updating to fight against any new form of attack. Due to the importance of IDS in cloud-based IoT, the main articles and essential techniques in this domain are examined systematically. In cloud-based IoT, IDSs are categorized into three major categories, including learning-based, pattern-based, and rule-based mechanisms. The findings illustrate that the biggest challenge in IDS is precision and detection, which many researchers are trying to improve. Also, with the rise of connected objects, the most frequently utilized centralized (cloud-based) IDS struggles with excessive latency and network overhead, leading to delayed detection of unauthorized users and unresponsiveness to assaults. The results will be valuable for academicians, and they can offer visions for future research.

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A Proposal of Rule-Based Hybrid Intrusion Detection System Through Analysis of Rule-Based Supervised Classifiers

Cited by 2 publications

References 27 publications

The Enhancing Accuracy in Mycobacterium Tuberculosis Counting on Acid-Fast Bacteria Sputum through Microscope Camera and Image Processing with Thresholding Method

The Enhancing Accuracy in Mycobacterium Tuberculosis Counting on Acid-Fast Bacteria Sputum through Microscope Camera and Image Processing with Thresholding Method

A systematic literature review of intrusion detection systems in the cloud‐basedIoTenvironments

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