Peer to Peer Large Scale Linear Algebra Programming and Experimentations

Current trends towards the Future Internet are envisaging the conception of novel services endowed with context-aware and autonomic capabilities to improve end users’ quality of life. The Internet of Things paradigm is expected to contribute towards this ambitious vision by proposing models and mechanisms enabling the creation of networks of “smart things” on a large scale. It is widely recognized that efficient mechanisms for discovering available resources and capabilities are required to realize such vision. The contribution of this work consists in a novel discovery service for the Internet of Things. The proposed solution adopts a peer-to-peer approach for guaranteeing scalability, robustness, and easy maintenance of the overall system. While most existing peer-to-peer discovery services proposed for the IoT support solely exact match queries on a single attribute (i.e., the object identifier), our solution can handle multiattribute and range queries. We defined a layered approach by distinguishing three main aspects: multiattribute indexing, range query support, peer-to-peer routing. We chose to adopt an over-DHT indexing scheme to guarantee ease of design and implementation principles. We report on the implementation of a Proof of Concept in a dangerous goods monitoring scenario, and, finally, we discuss test results for structural properties and query performance evaluation.

Download Full-text

Detection of DDOS attacks in distributed peer to peer networks

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.7.12227 ◽

2018 ◽

Vol 7 (2.7) ◽

pp. 1051

Author(s):

Gera Jaideep ◽

Bhanu Prakash Battula

Keyword(s):

High Risk ◽

Large Scale ◽

Peer To Peer ◽

P2p Network ◽

Ddos Attacks ◽

P2p Networks ◽

Distributed Environment ◽

Trace Back ◽

Peer To Peer Networks ◽

Direct Attack

Peer to Peer (P2P) network in the real world is a class of systems that are made up of thousands of nodes in distributed environments. The nodes are decentralized in nature. P2P networks are widely used for sharing resources and information with ease. Gnutella is one of the well known examples for such network. Since these networks spread across the globe with large scale deployment of nodes, adversaries use them as a vehicle to launch DDoS attacks. P2P networks are exploited to make attacks over hosts that provide critical services to large number of clients across the globe. As the attacker does not make a direct attack it is hard to detect such attacks and considered to be high risk threat to Internet based applications. Many techniques came into existence to defeat such attacks. Still, it is an open problem to be addressed as the flooding-based DDoS is difficult to handle as huge number of nodes are compromised to make attack and source address spoofing is employed. In this paper, we proposed a framework to identify and secure P2P communications from a DDoS attacks in distributed environment. Time-to-Live value and distance between source and victim are considered in the proposed framework. A special agent is used to handle information about nodes, their capacity, and bandwidth for efficient trace back. A Simulation study has been made using NS2 and the experimental results reveal the significance of the proposed framework in defending P2P network and target hosts from high risk DDoS attacks.

Download Full-text

BestPeer++: A Peer-to-Peer Based Large-Scale Data Processing Platform

IEEE Transactions on Knowledge and Data Engineering ◽

10.1109/tkde.2012.236 ◽

2014 ◽

Vol 26 (6) ◽

pp. 1316-1331 ◽

Cited By ~ 6

Author(s):

Gang Chen ◽

Tianlei Hu ◽

Dawei Jiang ◽

Peng Lu ◽

Kian-Lee Tan ◽

...

Keyword(s):

Data Processing ◽

Large Scale ◽

Peer To Peer ◽

Large Scale Data ◽

Large Scale Data Processing ◽

Processing Platform ◽

Scale Data

Download Full-text

Unobtrusive monitoring: Statistical dissemination latency estimation in Bitcoin’s peer-to-peer network

PLoS ONE ◽

10.1371/journal.pone.0243475 ◽

2020 ◽

Vol 15 (12) ◽

pp. e0243475

Author(s):

David Mödinger ◽

Jan-Hendrik Lorenz ◽

Rens W. van der Heijden ◽

Franz J. Hauck

Keyword(s):

Bayesian Model ◽

Large Scale ◽

Risk Estimation ◽

Peer To Peer ◽

Third Party ◽

Network Resources ◽

Connection Number ◽

Peer Network ◽

Time Required ◽

Peer To Peer Network

The cryptocurrency system Bitcoin uses a peer-to-peer network to distribute new transactions to all participants. For risk estimation and usability aspects of Bitcoin applications, it is necessary to know the time required to disseminate a transaction within the network. Unfortunately, this time is not immediately obvious and hard to acquire. Measuring the dissemination latency requires many connections into the Bitcoin network, wasting network resources. Some third parties operate that way and publish large scale measurements. Relying on these measurements introduces a dependency and requires additional trust. This work describes how to unobtrusively acquire reliable estimates of the dissemination latencies for transactions without involving a third party. The dissemination latency is modelled with a lognormal distribution, and we estimate their parameters using a Bayesian model that can be updated dynamically. Our approach provides reliable estimates even when using only eight connections, the minimum connection number used by the default Bitcoin client. We provide an implementation of our approach as well as datasets for modelling and evaluation. Our approach, while slightly underestimating the latency distribution, is largely congruent with observed dissemination latencies.

Download Full-text