IfNot: An approach towards mitigating interest flooding attacks in Named Data Networking of Things

Bilgili, Sedat; Demir, Alper Kamil; Alam, Shahid

doi:10.1016/j.iot.2024.101076

Cited by 3 publications

(2 citation statements)

References 37 publications

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“…These types of attacks aim to disrupt the network functionality of the ICN by flooding it with a large number of illegitimate interest messages to overwhelm the network's infrastructure. These attacks may cause network congestion, resource depletion, DoS, and increased energy consumption in IoT devices [21].…”

Section: Routing-related Attacksmentioning

confidence: 99%

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Detection of DoS Attacks for IoT in Information-Centric Networks Using Machine Learning: Opportunities, Challenges, and Future Research Directions

Bukhowah,

Aljughaiman,

Rahman

2024

Electronics

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The Internet of Things (IoT) is a rapidly growing network that shares information over the Internet via interconnected devices. In addition, this network has led to new security challenges in recent years. One of the biggest challenges is the impact of denial-of-service (DoS) attacks on the IoT. The Information-Centric Network (ICN) infrastructure is a critical component of the IoT. The ICN has gained recognition as a promising networking solution for the IoT by supporting IoT devices to be able to communicate and exchange data with each other over the Internet. Moreover, the ICN provides easy access and straightforward security to IoT content. However, the integration of IoT devices into the ICN introduces new security challenges, particularly in the form of DoS attacks. These attacks aim to disrupt or disable the normal operation of the ICN, potentially leading to severe consequences for IoT applications. Machine learning (ML) is a powerful technology. This paper proposes a new approach for developing a robust and efficient solution for detecting DoS attacks in ICN-IoT networks using ML technology. ML is a subset of artificial intelligence (AI) that focuses on the development of algorithms. While several ML algorithms have been explored in the literature, including neural networks, decision trees (DTs), clustering algorithms, XGBoost, J48, multilayer perceptron (MLP) with backpropagation (BP), deep neural networks (DNNs), MLP-BP, RBF-PSO, RBF-JAYA, and RBF-TLBO, researchers compare these detection approaches using classification metrics such as accuracy. This classification metric indicates that SVM, RF, and KNN demonstrate superior performance compared to other alternatives. The proposed approach was carried out on the NDN architecture because, based on our findings, it is the most used one and has a high percentage of various types of cyberattacks. The proposed approach can be evaluated using an ndnSIM simulation and a synthetic dataset for detecting DoS attacks in ICN-IoT networks using ML algorithms.

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Section: Routing-related Attacksmentioning

confidence: 99%

“…Sedat et al [21] provided a practical implementation of NDN-based IoT using the IfNoT mechanism to mitigate IFA. However, the paper mentions that in addition to their study, only one other study presents how to mitigate IFA in ICN-IoT.…”

Section: Related Work On Icn-based Iot With Machine Learning Technolo...mentioning

confidence: 99%

Detection of DoS Attacks for IoT in Information-Centric Networks Using Machine Learning: Opportunities, Challenges, and Future Research Directions

Bukhowah,

Aljughaiman,

Rahman

2024

Electronics

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Mitigation of Interest Flooding Attack and Controlled Packet Propagation in Named Internet of Vehicles

Umar Sattar,

Asif Rehman,

Arsalan

et al. 2024

IEEE Access

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Mathematical Models for Named Data Networking Producer Mobility Techniques: A Review

Azamuddin,

Mohd Aman,

Sallehuddin

et al. 2024

Mathematics

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One promising paradigm for content-centric communication is Named Data Networking (NDN), which revolutionizes data delivery and retrieval. A crucial component of NDN, producer mobility, presents new difficulties and opportunities for network optimization. This article reviews simulation strategies designed to improve NDN producer mobility. Producer mobility strategies have developed due to NDN data access needs, and these methods optimize data retrieval in dynamic networks. However, assessing their performance in different situations is difficult. Moreover, simulation approaches offer a cost-effective and controlled setting for experimentation, making them useful for testing these technologies. This review analyzes cutting-edge simulation methodologies for NDN producer mobility evaluation. These methodologies fall into three categories: simulation frameworks, mobility models, and performance metrics. Popular simulation platforms, including ns-3, OMNeT++, and ndnSIM, and mobility models that simulate producer movement are discussed. We also examine producer mobility performance indicators, such as handover data latency, signaling cost, and total packet loss. In conclusion, this comprehensive evaluation will help researchers, network engineers, and practitioners understand NDN producer mobility modeling approaches. By knowing these methodologies’ strengths and weaknesses, network stakeholders may make informed NDN solution development and deployment decisions, improving content-centric communication in dynamic network environments.

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IfNot: An approach towards mitigating interest flooding attacks in Named Data Networking of Things

Cited by 3 publications

References 37 publications

Detection of DoS Attacks for IoT in Information-Centric Networks Using Machine Learning: Opportunities, Challenges, and Future Research Directions

Detection of DoS Attacks for IoT in Information-Centric Networks Using Machine Learning: Opportunities, Challenges, and Future Research Directions

Mitigation of Interest Flooding Attack and Controlled Packet Propagation in Named Internet of Vehicles

Mathematical Models for Named Data Networking Producer Mobility Techniques: A Review

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