Identification of Attack Traffic Using Machine Learning in Smart IoT Networks

Shafiq, Muhammad; Nazir, Shah; Yu, Xiangzhan

doi:10.1155/2022/9804596

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…IoT devices are widely employed in smart applications, including travel, healthcare, and smart cities [1]. The ubiquity of IoT devices and the massive volume of data these devices generate are the root causes of IoT security vulnerabilities [2].…”

Section: Introductionmentioning

confidence: 99%

LPCOCN: A Layered Paddy Crop Optimization-Based Capsule Network Approach for Anomaly Detection at IoT Edge

et al. 2022

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Cyberattacks have increased as a consequence of the expansion of the Internet of Things (IoT). It is necessary to detect anomalies so that smart devices need to be protected from these attacks, which must be mitigated at the edge of the IoT network. Therefore, efficient detection depends on the selection of an optimal IoT traffic feature set and the learning algorithm that classifies the IoT traffic. There is a flaw in the existing anomaly detection systems because the feature selection algorithms do not identify the most appropriate set of features. In this article, a layered paddy crop optimization (LPCO) algorithm is suggested to choose the optimal set of features. Furthermore, the use of smart devices generates tremendous traffic, which can be labelled as either normal or attack using a capsule network (CN) approach. Five network traffic benchmark datasets are utilized to evaluate the proposed approach, including NSL KDD, UNSW NB, CICIDS, CSE-CIC-IDS, and UNSW Bot-IoT. Based on the experiments, the presented approach yields assuring results in comparison with the existing base classifiers and feature selection approaches. Comparatively, the proposed strategy performs better than the current state-of-the-art approaches.

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Section: Introductionmentioning

confidence: 99%

LPCOCN: A Layered Paddy Crop Optimization-Based Capsule Network Approach for Anomaly Detection at IoT Edge

et al. 2022

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“…Unfortunately, traditional methods are not able to handle the unique challenges of this type of traffic. In order to effectively address these issues, new techniques are required [2]- [4].…”

mentioning

confidence: 99%

A Hybrid Feature Selection Approach based on Random Forest and Particle Swarm Optimization for IoT Network Traffic Analysis

Lavate¹,

Srivastava²

2023

IJEER

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The complexity and volume of network traffic has increased significantly due to the emergence of the “Internet of Things” (IoT). The classification accuracy of the network traffic is dependent on the most pertinent features. In this paper, we present a hybrid feature selection method that takes into account the optimization of Particle Swarms (PSO) and Random Forests. The data collected by the security firm, CIC-IDS2017, contains a large number of attacks and traffic instances. To improve the classification accuracy, we use the framework's RF algorithm to identify the most important features. Then, the PSO algorithm is used to refine the selection process. According to our experiments, the proposed method performed better than the other methods when it comes to the classification accuracy. It achieves a ~99.9% accuracy when using a hybrid of Random Forest and PSO. The hybrid approach also helps improve the model's performance. The suggested method can be utilized by security analysts and network administrators to identify and prevent attacks on the IoT.

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Machine Learning-based Traffic Classification and Channel Allocation in IoT: A Survey

Lavate,

Srivastava

2023

2023 7th International Conference on Computing, Communication, Control and Automation (ICCUBEA)

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Identification of Attack Traffic Using Machine Learning in Smart IoT Networks

Cited by 4 publications

References 9 publications

LPCOCN: A Layered Paddy Crop Optimization-Based Capsule Network Approach for Anomaly Detection at IoT Edge

LPCOCN: A Layered Paddy Crop Optimization-Based Capsule Network Approach for Anomaly Detection at IoT Edge

A Hybrid Feature Selection Approach based on Random Forest and Particle Swarm Optimization for IoT Network Traffic Analysis

Machine Learning-based Traffic Classification and Channel Allocation in IoT: A Survey

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