Analyzing Threats of IoT Networks Using SDN Based Intrusion Detection System (SDIoT-IDS)

Wani, Azka; Revathi, S.

doi:10.1007/978-981-10-8660-1_41

Cited by 10 publications

(10 citation statements)

References 8 publications

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“…In our previous work [7], we have introduced an IDS for IoT based on SDN and machine learning. The backward propagation algorithm has been used in the classifier.…”

Section: Related Workmentioning

confidence: 99%

“…Such IDS does not need human intervention for operation and does not require the system to be altered [6]. Deploying such an IDS for a system with limited capabilities is quite suitable An software-defined networking (SDN)-based intrusion detection and prevention system for IoT is introduced as an enhancement to our previous work [7]. The mechanism uses the features of SDN to design a proactive system for intrusion detection in the IoT network.…”

Section: Introductionmentioning

confidence: 99%

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SDN‐based intrusion detection system for IoT using deep learning classifier (IDSIoT‐SDL)

Wani

Revathi

Khaliq

2021

CAAI Trans on Intel Tech

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The participation of ordinary devices in networking has created a world of connected devices rapidly. The Internet of Things (IoT) includes heterogeneous devices from every field. There are no definite protocols or standards for IoT communication, and most of the IoT devices have limited resources. Enabling a complete security measure for such devices is a challenging task, yet necessary. Many lightweight security solutions have surfaced lately for IoT. The lightweight security protocols are unable to provide an optimum protection against prevailing powerful threats in cyber world. It is also hard to deploy any traditional security protocol on resource-constrained IoT devices. Softwaredefined networking introduces a centralized control in computer networks. SDN has a programmable approach towards networking that decouples control and data planes. An SDN-based intrusion detection system is proposed which uses deep learning classifier for detection of anomalies in IoT. The proposed intrusion detection system does not burden the IoT devices with security profiles. The proposed work is executed on the simulated environment. The results of the simulation test are evaluated using various matrices and compared with other relevant methods. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…In our previous work [7], we have introduced an IDS for IoT based on SDN and machine learning. The backward propagation algorithm has been used in the classifier.…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

SDN‐based intrusion detection system for IoT using deep learning classifier (IDSIoT‐SDL)

Wani

Revathi

Khaliq

2021

CAAI Trans on Intel Tech

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“…The research [145] shows the pons and cons of different IDS and IPS systems in the context of cloud computing. The author in [126] presents an IDS system SDIoT-IDS based on SDN for the IoT devices such that the maximum load is taken off from the edge devices. The system is tested only with few attacks like flood attack and ICMP based attacks.…”

Section: Wani and Revathi [126] Bpnn Nsl-kdd Dataset Ryu Controllermentioning

confidence: 99%

SDN based Intrusion Detection and Prevention Systems using Manufacturer Usage Description: A Survey

Mazhar¹,

Salleh²,

Hossain³

et al. 2020

IJACSA

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“…A mitigation system based on rate-limiting model in Contiki Operating System (OS) proves efficient to identify UDP Flood attacks [14] but fails to work well in TCP. Early detection modules of Flooding attacks are developed by using Software Defined Networking (SDN) but the model lacked practical testing in real-time scenarios [15] [16]. Table 2 elucidates other cyber-attacks against this layer along with the security measures.…”

Section: Transport Layermentioning

confidence: 99%

“…SDN based IDS for monitoring activity [16]. Dynamic Anomaly Detection module by learning attack behaviour [15].…”

Section: Attackmentioning

confidence: 99%

Security Framework for IoT Devices against Cyber-attacks

Tabassum¹,

Lebda²

2019

6th International Conference on Computer Science, Engineering and Information Technology (CSEIT-2019)

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Internet of Things (IoT) is the interconnection of heterogeneous smart devices through the Internet with diverse application areas. The huge number of smart devices and the complexity of networks has made it impossible to secure the data and communication between devices. Various conventional security controls are insufficient to prevent numerous attacks against these information-rich devices. Along with enhancing existing approaches, a peripheral defence, Intrusion Detection System (IDS), proved efficient in most scenarios. However, conventional IDS approaches are unsuitable to mitigate continuously emerging zero-day attacks. Intelligent mechanisms that can detect unfamiliar intrusions seems a prospective solution. This article explores popular attacks against IoT architecture and its relevant defence mechanisms to identify an appropriate protective measure for different networking practices and attack categories. Besides, a security framework for IoT architecture is provided with a list of security enhancement techniques.

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Analyzing Threats of IoT Networks Using SDN Based Intrusion Detection System (SDIoT-IDS)

Cited by 10 publications

References 8 publications

SDN‐based intrusion detection system for IoT using deep learning classifier (IDSIoT‐SDL)

SDN‐based intrusion detection system for IoT using deep learning classifier (IDSIoT‐SDL)

SDN based Intrusion Detection and Prevention Systems using Manufacturer Usage Description: A Survey

Security Framework for IoT Devices against Cyber-attacks

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