Ensemble classification using traffic flow metrics to predict distributed denial of service scope in the Internet of Things (IoT) networks

Rambabu, Kalathiripi; Nalla, Venkatram

doi:10.1016/j.compeleceng.2021.107444

Cited by 12 publications

(6 citation statements)

References 10 publications

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“…The country and society are also constantly exploring how to provide more and better services for the disabled to meet their survival and development needs. The disabled public service cloud platform is a service platform constructed to provide more efficient and convenient services for disabled groups [ 23 , 24 ].…”

Section: Discussionmentioning

confidence: 99%

Construction of a Public Service Cloud Platform for Disabled People Based on the Big Data Management Model of the Internet of Things

Liu

Chang

2022

Computational Intelligence and Neuroscience

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With the continuous development of social economy and the continuous improvement of people’s ideological level, the state and social people have given more and more care and assistance to the disadvantaged group of disabled persons. The construction of a public service cloud platform for the disabled is one of the important measures to protect the human rights of the disabled and improve the lives of the disabled. According to the statistics of the China Disabled Persons’ Federation, since the twentieth century, with the acceleration of the equalization of basic public services and the establishment of urban and rural social security systems, a practical and effective public service system for the disabled has made leaps and bounds. The construction of the cloud platform for public services for the disabled is an important branch of the construction of the public service system for the disabled. However, due to the late development of the construction of the cloud platform for public services for the disabled in China, it is not perfect, and there are still some problems to be solved. This paper mainly studies the public service cloud platform for the disabled based on the big data management model of the Internet of Things. Based on this research theme, this paper conducts a service response efficiency test for the disabled provided by the disabled public service cloud platform based on the big data management model of the Internet of Things. It concluded that the public service cloud platform for the disabled based on the big data management model of the Internet of Things has improved the response efficiency of providing services for the disabled by 57%.

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Section: Discussionmentioning

confidence: 99%

Construction of a Public Service Cloud Platform for Disabled People Based on the Big Data Management Model of the Internet of Things

Liu

Chang

2022

Computational Intelligence and Neuroscience

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“…Rambabu and Venkatram [37] proposed an ensemble classification using traffic flow metrics for DDoS attacks in IoT networks. Using cross-validation, they addressed the importance of the ensemble approach towards DDoS defense accuracy with fewer false alarms.…”

Section: Relevant Studiesmentioning

confidence: 99%

SUKRY: Suricata IDS with Enhanced kNN Algorithm on Raspberry Pi for Classifying IoT Botnet Attacks

Syamsuddin¹,

Barukab

2022

Electronics

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The focus of this research is the application of the k-Nearest Neighbor algorithm in terms of classifying botnet attacks in the IoT environment. The kNN algorithm has several advantages in classification tasks, such as simplicity, effectiveness, and robustness. However, it does not perform well in handling large datasets such as the Bot-IoT dataset, which represents a huge amount of data about botnet attacks on IoT networks. Therefore, improving the kNN performance in classifying IoT botnet attacks is the main concern in this study by applying several feature selection techniques. The whole research process was conducted in the Rapidminer environment using three prebuilt feature selection techniques, namely, Information Gain, Forward Selection, and Backward Elimination. After comparing accuracy, precision, recall, F1 score and processing time, the combination of the kNN algorithm and the Forward Selection technique (kNN-FS) achieves the best results among others, with the highest level of accuracy and the fastest execution time among others. Finally, kNN-FS is used in developing SUKRY, which stands for Suricata IDS with Enhanced kNN Algorithm on Raspberry Pi.

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“…[6] tackled encrypted application traffic classification, integrating attention mechanisms with spatiotemporal features. Similarly, [7] applied CNN combined with an Ant-Lion Optimizer (ALO) and Self-Organizing Map (SOM), achieving remarkable results, especially with encrypted traffic. [8] brought CNNs into the Internet of Things (IoT) traffic domain, stressing the efficacy of combining CNN with Recurrent Neural Network (RNN).…”

Section: Introductionmentioning

confidence: 99%

6G Traffic Prediction with a Novel Parallel Convolutional Neural Networks Architecture and Matrix Format Method Integration

Bolivar,

N K,

et al. 2024

JMC

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In the evolving world of wireless communication, sixth generation (6G) networks represent a significant leap forward. Beyond its high-speed and reliable communication, 6G integrates Artificial Intelligence (AI), making networks intelligent entities. This elevates the infrastructure of smart cities and other ecosystems. A critical factor in 6G's success is real-time traffic analysis. As 6G aims to interconnect billions of devices, it faces unprecedented traffic patterns. Practical traffic analysis ensures optimal performance, resource distribution, and energy efficiency. It also supports the network in handling vital sectors like healthcare and transportation by anticipating congestion and prioritizing crucial data. However, traditional traffic analysis techniques designed for earlier generations cannot accommodate 6G's demands. With 6G's integration of diverse technologies, understanding traffic becomes more challenging. Recent advancements have incorporated deep learning architectures, notably Convolutional Neural Networks (CNNs), for traffic analysis. While these models show potential, adapting them to 6G's specifics remains challenging. This research presents a unique parallel CNN architecture for 6G traffic prediction. It converts network data into an image using the Matrix Format Method (MFM), making it suitable for CNN processing. This innovation addresses the limitations of traditional methods and meets 6G's requirements. Compared to other models, our parallel CNN architecture highlights enhanced performance, promising increased traffic prediction accuracy. It also paves the way for improved resource allocation, energy management, and quality of service in 6G environments.

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Ensemble classification using traffic flow metrics to predict distributed denial of service scope in the Internet of Things (IoT) networks

Cited by 12 publications

References 10 publications

Construction of a Public Service Cloud Platform for Disabled People Based on the Big Data Management Model of the Internet of Things

Construction of a Public Service Cloud Platform for Disabled People Based on the Big Data Management Model of the Internet of Things

SUKRY: Suricata IDS with Enhanced kNN Algorithm on Raspberry Pi for Classifying IoT Botnet Attacks

6G Traffic Prediction with a Novel Parallel Convolutional Neural Networks Architecture and Matrix Format Method Integration

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