A Deep Learning Approach for Classifying Network Connected IoT Devices Using Communication Traffic Characteristics

Chowdhury, Rajarshi Roy; Idris, Azam Che; Abas, Pg Emeroylariffion

doi:10.1007/s10922-022-09716-x

Cited by 11 publications

(3 citation statements)

References 51 publications

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“…IP Spoofing: IP spoofing allows attackers to impersonate legitimate sources, leading to potential security breaches. IP spoofing at the TCP/IP internet layer involves the manipulation of packet headers to alter the source IP address, making the traffic appear as though it is coming from a different, trusted source rather than the attacker's actual location [64], [65]. Figure 4 depicts how IP spoofing attacks are perpetrated.…”

Section: Figure 3 Dos Attackmentioning

confidence: 99%

Performance, privacy and security issues at the TCP internet layer

Ogweno Jeremiah okeyo

2024

World J. Adv. Res. Rev.

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The rapid growth of internet-based services and applications has revolutionized the way we communicate, interact, and conduct business. However, this widespread connectivity also brings forth numerous challenges related to performance and privacy security at the internet layer. This paper aims to provide a comprehensive analysis of the various issues that arise in this context, highlighting their impact on user experience, data protection, and network efficiency, to address performance concerns; the research paper investigates the factors affecting internet Layer. Through extensive research studies, the paper examines the impact of network congestion, routing inefficiencies, and protocol limitations on the overall performance of internet-based services. This research delves into the vulnerabilities and risks associated with the internet layer. It investigates the potential threats posed by malicious actors, including hackers, data breaches, and unauthorized access to sensitive information. The findings of this research contribute to a comprehensive understanding of the interplay between performances and privacy security issues at the internet layer. By identifying the key challenges and potential solutions, this study provides valuable insights for network administrators, service providers, policymakers, and end-users to optimize performance while ensuring robust privacy and security measures. Ultimately, this research aims to foster a safer and more efficient internet ecosystem for all stakeholders.

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Section: Figure 3 Dos Attackmentioning

confidence: 99%

Performance, privacy and security issues at the TCP internet layer

Ogweno Jeremiah okeyo

2024

World J. Adv. Res. Rev.

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“…This method can be used in developing ML techniques to detect attacks. R. R. Chowdhury et al [24] present a deep learning method that can be used to identify the presence of IoT devices in a network traffic. It is able to perform high-precision calculations even in scenarios involving traffic congestion and noise.…”

Section: ░ 2 Related Workmentioning

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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“…It plays a crucial role in protecting network security, enhancing network reliability, safeguarding privacy, and facilitating device management, among other critical aspects. Therefore, identifying IoT devices is of paramount importance in improving network security [10].…”

Section: Introductionmentioning

confidence: 99%

IoT Device Identification Based on Network Traffic

Gu,

Zhang,

Tang

et al. 2023

Preprint

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With the rapid development of Internet of Things (IoT) technology, the widespread deployment and heterogeneity of IoT devices bring new challenges to device identification. For network administrators, understanding the IoT devices connected to the network is very important. However, there are still some issues with existing IoT device identification methods, including the dependence on a large number of features, vulnerable features (such as IP addresses), and payloads. In response to these issues, an IoT device identification method based on network traffic is proposed in this paper. The method utilizes network traffic data of IoT devices over a short period of time, extracts protocol statistical features and flow-level statistical features, and thus achieves the identification of IoT device categories and device types. The proposed method was evaluated on two publicly available datasets, and the results demonstrate that the proposed method has superiority and can accurately and effectively identify devices.

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A Deep Learning Approach for Classifying Network Connected IoT Devices Using Communication Traffic Characteristics

Cited by 11 publications

References 51 publications

Performance, privacy and security issues at the TCP internet layer

Performance, privacy and security issues at the TCP internet layer

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

IoT Device Identification Based on Network Traffic

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