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In information security, it is crucial to identify vulnerabilities in the earliest stage possible, fix them in the earliest possible time, and implement measures that would help in managing risks when it comes to digital assets. However, scalability remains a key challenge. This paper identifies common issues in existing vulnerability scanners, including system overloading, poor prioritization, and limited adaptability. To address these problems, it proposes a machine learning–based methodology that enhances reliability and adaptability by engineering key features from diverse datasets and employing a robust training process. It aims at specific risk appraisal, and standardization for the principles of ISO 27001 and its efficient usage in different conditions. The suggested machine learning model is better than the conventional scanners in so much as it produces better precision, recall, and F1-scores. It effectively detects the vulnerabilities that have been missed by other tools, decreases the number of false positives/negatives, and manages resources to reduce risk of breach and to enhance the organization’s security posture. Some of the implementation challenges that include integration of the system and the cost are examined with solutions on how it can be deployed. Research in the future will focus on the development of the model’s scalability to deal with big data and a wide range of networks, application of transfer learning in identifying threats emerging and increasing the model’s usability to IoT and cloud systems. It will also aim at enhancing the ability to implement the compliance with regulations where necessary and the development of real-time anomaly detection systems. This paper thus offers a complete solution that is cost-efficient and secure, which integrates security measures with the organization’s objectives for the prevention of vulnerabilities and management of risks. However, scalability remains a key challenge. This paper identifies common issues in existing vulnerability scanners, including system overloading, poor prioritization, and limited adaptability. To address these problems, it proposes a machine learning–based methodology that enhances reliability and adaptability by engineering key features from diverse datasets and employing a robust training process.

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Enhancing Information Security: Proactive Detection, Vulnerability Remediation, and Systematic Risk Management

  • 章节

  • 发表日期：

    2026年

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  • Minahil Azaz,
  • Syeda Um e Farwa

摘要

In information security, it is crucial to identify vulnerabilities in the earliest stage possible, fix them in the earliest possible time, and implement measures that would help in managing risks when it comes to digital assets. However, scalability remains a key challenge. This paper identifies common issues in existing vulnerability scanners, including system overloading, poor prioritization, and limited adaptability. To address these problems, it proposes a machine learning–based methodology that enhances reliability and adaptability by engineering key features from diverse datasets and employing a robust training process. It aims at specific risk appraisal, and standardization for the principles of ISO 27001 and its efficient usage in different conditions. The suggested machine learning model is better than the conventional scanners in so much as it produces better precision, recall, and F1-scores. It effectively detects the vulnerabilities that have been missed by other tools, decreases the number of false positives/negatives, and manages resources to reduce risk of breach and to enhance the organization’s security posture. Some of the implementation challenges that include integration of the system and the cost are examined with solutions on how it can be deployed. Research in the future will focus on the development of the model’s scalability to deal with big data and a wide range of networks, application of transfer learning in identifying threats emerging and increasing the model’s usability to IoT and cloud systems. It will also aim at enhancing the ability to implement the compliance with regulations where necessary and the development of real-time anomaly detection systems. This paper thus offers a complete solution that is cost-efficient and secure, which integrates security measures with the organization’s objectives for the prevention of vulnerabilities and management of risks. However, scalability remains a key challenge. This paper identifies common issues in existing vulnerability scanners, including system overloading, poor prioritization, and limited adaptability. To address these problems, it proposes a machine learning–based methodology that enhances reliability and adaptability by engineering key features from diverse datasets and employing a robust training process.