The Era of Internet of Things: Towards better security using machine learning

Abdulla, Husain; Al-Raweshidy, Hamed S.; Awad, Wasan Shaker

doi:10.1109/itikd56332.2023.10099608

Cited by 2 publications

(2 citation statements)

References 46 publications

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“…Organisations need to implement security measures at every stage to solve this, such as container security, stringent code review procedures, and the adoption of safe DevOpspractises. By doing this, they develop a thorough defence strategy that covers the full ML lifetime [4] [5].…”

Section: Figure 1: Systematic View Of Proposed Modelmentioning

confidence: 99%

Securing Machine Learning Ecosystems: Strategies for Building Resilient Systems

Dhabliya,

Rizvi,

Dhablia

et al. 2024

E3S Web Conf.

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In today's data-driven environment, protecting machine learning ecosystems has taken on critical importance. Organisations are relying more and more on AI and ML models to guide important decisions and operations, which have led to an increase in system vulnerabilities. The critical need for techniques to create resilient machine learning (ML) systems that can withstand changing threats is discussed in this study.Data protection is an important component of securing ML environments. Every part of the process, from data preprocessing through model deployment, needs to be secured. In order to reduce potential vulnerabilities, this incorporates code review procedures, safe DevOps practises, and container security.System resilience is vitally dependent on on-going monitoring and anomaly detection. Organisations can respond quickly to security problems by detecting deviations from normal behaviour early on and adjusting their defences as necessary.A strong incident response plan is essential. To protecting machine learning ecosystems necessitates a comprehensive strategy that includes monitoring, incident response, model security, pipeline security, and data protection. By implementing these tactics, businesses may create robust machine learning (ML) systems that can endure the changing threat landscape, protect their data, and guarantee the validity of their AI-driven decision-making processes.

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Section: Figure 1: Systematic View Of Proposed Modelmentioning

confidence: 99%

Securing Machine Learning Ecosystems: Strategies for Building Resilient Systems

Dhabliya,

Rizvi,

Dhablia

et al. 2024

E3S Web Conf.

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“…Due to its granular nature, bottom ash can sometimes serve as a substitute for sand in concrete applications [1]. However, fly ash has become a common substitute for cement in concrete during the last few years [2]. It is known that substituting fly ash for cement reduces the initial development of strength because of its low calcium content.…”

Section: Introductionmentioning

confidence: 99%

Experimental and Statistical Study of Flexural Strength in Concrete using Novel Kaolinite Coal

Ayampalayam Nanjappan,

Manogaran,

Alphonse

et al. 2024

E3S Web Conf.

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The purpose of this research is to measure the flexural strength of both traditional and newly modified concrete beams with kaolinite coal added. Two sets of eighteen samples each were ready for the data collection. Two sets of specimensare prepared: 18 specimens of one set using modified concrete that has been treated with kaolinite coal, and the other set of 18 specimens using traditional concrete beams. The preparation and examination of the samples involved the use of flexural strength test equipment. The beam specimen size was taken as 100 x 100 x 500mm. The SPSS software version 29 has been utilized to conduct the independent sample T-test. The statistical analysis included Levene's test for equality of variances and a t-test for equality of means to assess the flexural strength of two groups. Levene's test revealed a significant difference in variances between the groups (F=2.909, p=0.097). Subsequently, the t-test assuming equal variances indicated a highly significant difference in means (t=-16.954, df=34, p=0.000). The mean difference in flexural strength was -1.43578, with a standard error of 0.08469 and a 95% confidence interval ranging from -1.60788 to -1.26367. The t-test assuming unequal variances corroborated the significant difference in means (t=-16.954, df=29.822, p=0.000), with a consistent mean difference and confidence interval. The modified concrete beam sample with kaolinite coal added has a mean flexural strength of 4.75 N/mm2, while the standard concrete beam sample has a mean flexural strength of 2.73 N/mm2.

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The Era of Internet of Things: Towards better security using machine learning

Cited by 2 publications

References 46 publications

Securing Machine Learning Ecosystems: Strategies for Building Resilient Systems

Securing Machine Learning Ecosystems: Strategies for Building Resilient Systems

Experimental and Statistical Study of Flexural Strength in Concrete using Novel Kaolinite Coal

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