Big Data Analysis Methods Based on Machine Learning to Ensure Information Security

Veselska, Olga; Зюбина, Руслана; Finenko, Yuriy; Nikodem, Joanna

doi:10.1016/j.procs.2021.09.033

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…The premise of linear regression is that parameters affect function f in a linear fashion. The linear dependence does not, however, always rely on a free variable x [33].…”

Section: Supervised ML Algorithmsmentioning

confidence: 99%

Detection of IoT Botnet Cyber Attacks using Machine Learning

Khaleefah

Al-Mashhadi

2023

IJCAI

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As of 2018, the number of online devices has outpaced the global human population, a trend expected to surge towards an estimated 80 billion devices by 2024. With the growing ubiquity of Internet of Things (IoT) devices, securing these systems and the data they exchange has become increasingly complex, especially with the escalating frequency of IoT botnet attacks (IBA). The extensive data quantity and pervasive availability provided by these devices present a lucrative prospect for potential hackers, further escalating cybersecurity risks. Hence, one of the paramount challenges concerning IoT is ensuring its security. The primary objective of this research project is the development of a robust, machine learning algorithm-based model capable of detecting and mitigating botnet-based intrusions within IoT networks. The proposed model tackles the prevalent security issue posed by malicious bot activities. To optimize the model's performance, it was trained using the BoT-IoT dataset, employing a diverse range of machine learning methodologies, including linear regression, logistic regression, K-Nearest Neighbor (KNN), and Support Vector Machine (SVM) models. The efficacy of these models was evaluated using the F-measure, yielding results of 98.0%, 99.0%, 99.0%, and 99.0% respectively. These outcomes substantiate the models' capacity to accurately distinguish between normal and malicious network activities.Povzetek: Razvit je model strojnega učenja za zaznavanje in ublažitev napadov IoT botnetov, ki se je izkazal na domeni BoT-IoT.

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“…The premise of linear regression is that parameters affect function f in a linear fashion. The linear dependence does not, however, always rely on a free variable x [33].…”

Section: Supervised ML Algorithmsmentioning

confidence: 99%

Detection of IoT Botnet Cyber Attacks using Machine Learning

Khaleefah

Al-Mashhadi

2023

IJCAI

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“…Then, these are distributed to the power service enterprises in the corresponding area, and the data is analyzed to obtain the needs of the user group, and then the corresponding plan is formulated according to the amount of resources existing in different areas. Finally, by analyzing these data, we can get the consumer behavior of user groups, and then realize the information sharing between service enterprises and customers [6].…”

Section: Power Work Order Analysis Based On Big Data Clusteringmentioning

confidence: 99%

Research on the improvement of rural power service quality based on big data analysis method

Wang

et al. 2022

Fifth International Conference on Mechatronics and Computer Technology Engineering (MCTE 2022)

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Rural power service is an important support for realizing urban-rural integration, and it is of great significance to promote regional economic development and improve people's living standards. However, with the increasing maturity of Internet technology and information and communication means in the era of big data and the popularization of intelligent terminal equipment in rural areas, people's demand for services is increasing day by day, and the quality of customer service has become critical for power grid companies. Rural power service quality is an important part of power grid companies, and its development and progress play a pivotal role in China's national economy and social stability. The improvement of rural power service quality in the era of big data is a new problem in the current development process of power grid companies. It is necessary to combine modern information technology, artificial intelligence technology and information processing methods on the existing basis to effectively analyze and improve user needs and changes in customer experience, so as to provide more accurate and high-quality services for the majority of power supply enterprises. This paper first analyzes the establishment and empowerment of service quality evaluation indicators, and then analyzes the development status of rural power service quality, and puts forward specific plans to improve our country's rural power transmission capacity and promote the healthy and sustainable development of power grid enterprises in the era of big data. Secondly, based on the service work order, the work order classification is carried out, and the work order classification method and the rural power transmission service quality in the era of big data are analyzed. By analyzing the classification of work orders, we evaluate the service quality of rural power transmission in our country, and propose a service quality evaluation method, which can promote the healthy and sustainable development of China's power industry.

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Computer Network Security Analysis Based on Deep Learning Algorithm

Chen

Mazin

2022

Application of Intelligent Systems in Multi-Modal Information Analytics

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Big Data Analysis Methods Based on Machine Learning to Ensure Information Security

Cited by 3 publications

References 9 publications

Detection of IoT Botnet Cyber Attacks using Machine Learning

Detection of IoT Botnet Cyber Attacks using Machine Learning

Research on the improvement of rural power service quality based on big data analysis method

Computer Network Security Analysis Based on Deep Learning Algorithm

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