LDEPTH: A low diameter hierarchical P2P network architecture

Rahimi, Nick; Sinha, Koushik; Gupta, Bidyut; Rahimi, Shahram; Debnath, Narayan C.

doi:10.1109/indin.2016.7819275

Cited by 19 publications

(14 citation statements)

References 10 publications

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“…In most of the structured P2P networks, nodes pick random IDs when they enter the network and the P2P system does not monitor the ID assignments, which allows an attacker to obtain as many as IDs it desires and use them to compromise the network. However, Linear Diophantine Equation (LDE)-based P2P limits the number of logical addresses per single resource type [19]. This technique prevents users from acquiring various fake identities.…”

Section: Attacks On P2p Routing Protocolsmentioning

confidence: 99%

Untitled

2020

IJNSA

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Peer-to-Peer (P2P) overlay networks wide adoption has also created vast dangers due to the millions of users who are not conversant with the potential security risks. Lack of centralized control creates great risks to the P2P systems. This is mainly due to the inability to implement proper authentication approaches for threat management. The best possible solutions, however, include encryption, utilization of administration, implementing cryptographic protocols, avoiding personal file sharing, and unauthorized downloads. Recently a new non-DHT based structured P2P system is very suitable for designing secured communication protocols. This approach is based on Linear Diophantine Equation (LDE) [1]. The P2P architectures based on this protocol offer simplified methods to integrate symmetric and asymmetric cryptographies' solutions into the P2P architecture with no need of utilizing Transport Layer Security (TLS), and its predecessor, Secure Sockets Layer (SSL) protocols.

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Section: Attacks On P2p Routing Protocolsmentioning

confidence: 99%

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2020

IJNSA

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“…Este mecanismo proposto baseia-se em uma pontuação de confiança em cada operação realizada obtenção ou alocação de dados na rede DHT. Esta abordagem,é desvantajosa, visto que um nó pode ser malicioso, mas se comportar como um nó confiável na rede [Rahimi et al 2016] e em determinado momento denegrir o comportamento da rede. Diante disso, até que este nó se torne não confiável poderá ainda se comunicar com os demais participantes.…”

Section: Trabalhos Relacionadosunclassified

“…As redes baseadas em distributed hash table (DHT) estão cada vez mais em evidência para implementação de diversos serviços [Rahimi et al 2016]. Esta atenção especial se deve as suas características como tolerância a falhas, alta disponibilidade, escalabilidade, resiliência entre outros.…”

Section: Introductionunclassified

Um serviço para prover autenticação e revogação de nós na rede DHT

Garcia¹,

Dias²,

Rizzetti³

2019

Anais Da XVII Escola Regional De Redes De Computadores (ERRC 2019)

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“…To give an example which will be expanded later. Most Intrusion Detection Systems (specifically referring to Network Intrusion Detection Systems) like Snort or Suricata rely on catching adversarial activity to be trained against that activity [7,8]. What happens if the activity is not caught though?…”

Section: An Arms Racementioning

confidence: 99%

Adversarial Machine Learning: Difficulties in Applying Machine Learning to Existing Cybersecurity Systems

Rahimi¹,

Maynor²,

Gupta³

EPiC Series in Computing

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Machine learning is an attractive tool to make use of in various areas of computer science. It allows us to take a hands-off approach in various situations where previously manual work was required. One such area machine learning has not yet been applied entirely successfully is cybersecurity. The issue here is that most classical machine learning models do not consider the possibility of an adversary purposely attempting to mislead the machine learning system. If the possibility that incoming data will be deliberately crafted to mislead and break the machine learning system, these systems are useless in a cybersecurity setting. Taking this into account may allow us to modify existing security systems and introduce the power of machine learning to them.

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LDEPTH: A low diameter hierarchical P2P network architecture

Cited by 19 publications

References 10 publications

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Um serviço para prover autenticação e revogação de nós na rede DHT

Adversarial Machine Learning: Difficulties in Applying Machine Learning to Existing Cybersecurity Systems

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