Secure Distribution of Protected Content in Information-Centric Networking

Bilal, Muhammad; Pack, Sangheon

doi:10.1109/jsyst.2019.2931813

Cited by 26 publications

(9 citation statements)

References 40 publications

(76 reference statements)

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“…Muhammed et al [36] presented a new scheme called the secure distribution of protected content (SDPC) to ensure that only authenticated consumers can access the content. Furthermore, the paper presented all relevant security issues in the ICN by performing a formal analysis using BAN logic Scyther implementation.…”

Section: Threats To the Icnmentioning

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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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: Threats To the Icnmentioning

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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“…Such decisions will depend a lot on whether PD-ICN nodes will be realized as standalone routers, or will be software driven hosted at a datacenter/IXP. Open Questions: Different privacy preserving naming schemes already exists based on different encryption schemes, such as, hash chain [58] and symmetric encryption [3,48], nevertheless, due to the complex functionality of PD-ICN, two open question need to be answered. (1) How to construct a general purpose naming scheme for personal data that allows all the different functionalities that PD-ICN provides?…”

Section: Data Layer (Dl)mentioning

confidence: 99%

“…Open Questions: Data encryption in ICN networks is heavily depended on the Naming and Routing scheme [3,48,53,58]. In addition, in PD-ICN we also have to deal with additional constrains.…”

Section: Data Layer (Dl)mentioning

confidence: 99%

What do information centric networks, trusted execution environments, and digital watermarking have to do with privacy, the data economy, and their future?

Laoutaris

Iordanou

2021

SIGCOMM Comput. Commun. Rev.

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What if instead of having to implement controversial user tracking techniques, Internet advertising & marketing companies asked explicitly to be granted access to user data by name and category, such as Alice→Mobility→05-11-2020? The technology for implementing this already exists, and is none other than the Information Centric Networks (ICN), developed for over a decade in the framework of Next Generation Internet (NGI) initiatives. Beyond named access to personal data, ICN's in-network storage capability can be used as a substrate for retrieving aggregated, anonymized data, or even for executing complex analytics within the network, with no personal data leaking outside. In this opinion article we discuss how ICNs combined with trusted execution environments and digital watermarking, can be combined to build a personal data overlay inter-network in which users will be able to control who gets access to their personal data, know where each copy of said data is, negotiate payments in exchange for data, and even claim ownership, and establish accountability for data leakages due to malfunctions or malice. Of course, coming up with concrete designs about how to achieve all the above will require a huge effort from a dedicated community willing to change how personal data are handled on the Internet. Our hope is that this opinion article can plant some initial seeds towards this direction.

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“…Re-encryption technology supports a proxy transferring decryption authority without decrypting ciphertext [24]. On this basis, homomorphic re-encryption supports homomorphic operation on ciphertext, which is more suitable for data dissemination in networks with special needs.…”

Section: Homomorphic Re-encryptionmentioning

confidence: 99%

Improved Cloud-Assisted Privacy-Preserving Profile-Matching Scheme in Mobile Social Networks

Zou

Chai

Sha

et al. 2020

Security and Communication Networks

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Due to the transparency of the wireless channel, users in multiple-key environment are vulnerable to eavesdropping during the process of uploading personal data and re-encryption keys. Besides, there is additional burden of key management arising from multiple keys of users. In addition, profile matching using inner product between vectors cannot effectively filter out users with ulterior motives. To tackle the above challenges, we first improve a homomorphic re-encryption system (HRES) to support a single homomorphic multiplication and arbitrarily many homomorphic additions. The public key negotiated by the clouds is used to encrypt the users’ data, thereby avoiding the issues of key leakage and key management, and the privacy of users’ data is also protected. Furthermore, our scheme utilizes the homomorphic multiplication property of the improved HRES algorithm to compute the cosine result between the normalized vectors as the standard for measuring the users’ proximity. Thus, we can effectively improve the social experience of users.

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Secure Distribution of Protected Content in Information-Centric Networking

Cited by 26 publications

References 40 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

What do information centric networks, trusted execution environments, and digital watermarking have to do with privacy, the data economy, and their future?

Improved Cloud-Assisted Privacy-Preserving Profile-Matching Scheme in Mobile Social Networks

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