Security attacks and secure routing protocols in RPL-based Internet of Things: Survey

Kamble, Arvind; Malemath, V. S.; Patil, D. Y.

doi:10.1109/etiict.2017.7977006

Cited by 71 publications

(51 citation statements)

References 11 publications

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“…Security weaknesses in the current RPL protocol standard [15] are highlighted in [42]. No protection is specified against selective forwarding attacks, sinkhole attacks, black and gray hole attacks, and version number manipulation attacks.…”

Section: B Trust Modelsmentioning

confidence: 99%

Secure Routing for MANET Connected Internet of Things Systems

Karlsson

Dooley

Pulkkis

2018

2018 IEEE 6th International Conference on Future Internet of Things and Cloud (FiCloud)

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This paper presents a contemporary review of communication architectures and topographies for MANETconnected Internet-of-Things (IoT) systems. Routing protocols for multi-hop MANETs are analyzed with a focus on the standardized Routing Protocol for Low-power and Lossy Networks. Various security threats and vulnerabilities in current MANET routing are described and security enhanced routing protocols and trust models presented as methodologies for supporting secure routing. Finally, the paper identifies some key research challenges in the emerging domain of MANET-IoT connectivity.

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Section: B Trust Modelsmentioning

confidence: 99%

Secure Routing for MANET Connected Internet of Things Systems

Karlsson

Dooley

Pulkkis

2018

2018 IEEE 6th International Conference on Future Internet of Things and Cloud (FiCloud)

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“…The goal is for the compromised node to be conveniently placed on the RPL topology, so it becomes a legitimate router of most of the packets on the network. This can be achieved by exploiting well-known RPL vulnerabilities [5] (e.g. rank attack, version number attack).…”

Section: Hypothesismentioning

confidence: 99%

Demo: Do Not Trust Your Neighbors! A Small IoT Platform Illustrating a Man-in-the-Middle Attack

Navas¹,

Bouder²,

Cuppens³

et al. 2018

Ad-Hoc, Mobile, and Wireless Networks

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This demonstration defines a small IoT wireless network that uses TI CC2538-OpenMote as hardware platform and state-of-the-art IETF network standards such as 6LoWPAN, RPL, and CoAP implemented by ContikiOS. The IoT nodes are controlled from outside the IoT network using end-to-end connectivity provided by IPv6-CoAP messages. We implement a man-in-the-middle attack that disrupts the normal behavior of the system. Our attack leverages on the inherent hierarchical routing topology of RPL-based IoT networks. The demonstration aims at highlighting the need for end-to-end source-authentication and authorization enforcement of information even inside a trusted IoT network. We also provide some insights on how these services can be offered in a IoT-friendly way.

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“…Spoofing attacks have grown exponentially in the last few years [14][15][16]. Among other areas, social networks have recently reported serious privacy and security issues [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Entropy-Based Face Recognition and Spoof Detection for Security Applications

2019

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Nowadays, cyber attacks are becoming an extremely serious issue, which is particularly important to prevent in a smart city context. Among cyber attacks, spoofing is an action that is increasingly common in many areas, such as emails, geolocation services or social networks. Identity spoofing is defined as the action by which a person impersonates a third party to carry out a series of illegal activities such as committing fraud, cyberbullying, sextorsion, etc. In this work, a face recognition system is proposed, with an application to the spoofing prevention. The method is based on the Histogram of Oriented Gradients (HOG) descriptor. Since different face regions do not have the same information for the recognition process, introducing entropy would quantify the importance of each face region in the descriptor. Therefore, entropy is added to increase the robustness of the algorithm. Regarding face recognition, our approach has been tested on three well-known databases (ORL, FERET and LFW) and the experiments show that adding entropy information improves the recognition rate significantly, with an increase over 40% in some of the considered databases. Spoofing tests has been implemented on CASIA FASD and MIFS databases, having obtained again better results than similar texture descriptors approaches.

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Security attacks and secure routing protocols in RPL-based Internet of Things: Survey

Cited by 71 publications

References 11 publications

Secure Routing for MANET Connected Internet of Things Systems

Secure Routing for MANET Connected Internet of Things Systems

Demo: Do Not Trust Your Neighbors! A Small IoT Platform Illustrating a Man-in-the-Middle Attack

Entropy-Based Face Recognition and Spoof Detection for Security Applications

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