P2PCS - A Pure Peer-to-Peer Computing System for Large Scale Computation Problems

2011 ◽  
Author(s):  
Jigyasu Dubey ◽  
Vrinda Tokekar
Keyword(s):  
Large Scale ◽  
Computing System ◽  
Peer To Peer

scholarly journals A Survey on Machine-Learning Based Security Design for Cyber-Physical Systems

2021 ◽  
Vol 11 (12) ◽  
pp. 5458
Author(s):  
Sangjun Kim ◽  
Kyung-Joon Park
Keyword(s):  
Machine Learning ◽  
Physical System ◽  
Large Scale ◽  
Computing System ◽  
Future Research ◽  
Physical Security ◽  
Computing Systems ◽  
Physical Systems ◽  
Security Research ◽  
Simultaneous Control

A cyber-physical system (CPS) is the integration of a physical system into the real world and control applications in a computing system, interacting through a communications network. Network technology connecting physical systems and computing systems enables the simultaneous control of many physical systems and provides intelligent applications for them. However, enhancing connectivity leads to extended attack vectors in which attackers can trespass on the network and launch cyber-physical attacks, remotely disrupting the CPS. Therefore, extensive studies into cyber-physical security are being conducted in various domains, such as physical, network, and computing systems. Moreover, large-scale and complex CPSs make it difficult to analyze and detect cyber-physical attacks, and thus, machine learning (ML) techniques have recently been adopted for cyber-physical security. In this survey, we provide an extensive review of the threats and ML-based security designs for CPSs. First, we present a CPS structure that classifies the functions of the CPS into three layers: the physical system, the network, and software applications. Then, we discuss the taxonomy of cyber-physical attacks on each layer, and in particular, we analyze attacks based on the dynamics of the physical system. We review existing studies on detecting cyber-physical attacks with various ML techniques from the perspectives of the physical system, the network, and the computing system. Furthermore, we discuss future research directions for ML-based cyber-physical security research in the context of real-time constraints, resiliency, and dataset generation to learn about the possible attacks.

scholarly journals A DHT-Based Discovery Service for the Internet of Things

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Federica Paganelli ◽  
David Parlanti
Keyword(s):  
Internet Of Things ◽  
Large Scale ◽  
Future Internet ◽  
Peer To Peer ◽  
The Internet ◽  
Dangerous Goods ◽  
Discovery Service ◽  
Single Attribute ◽  
Smart Things ◽  
The Internet Of Things

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.

scholarly journals Detection of DDOS attacks in distributed peer to peer networks

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.  

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

2014 ◽  
Vol 26 (6) ◽  
pp. 1316-1331 ◽  
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

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

PLoS ONE ◽  
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.

scholarly journals Taxonomic assignment for large-scale metagenomic data on high-perfomance systems

2017 ◽  
Vol 33 (2) ◽  
pp. 119-130
Author(s):  
Vinh Van Le ◽  
Hoai Van Tran ◽  
Hieu Ngoc Duong ◽  
Giang Xuan Bui ◽  
Lang Van Tran
Keyword(s):  
High Performance Computing ◽  
Assignment Problem ◽  
High Performance ◽  
Large Scale ◽  
Computing System ◽  
Metagenomic Data ◽  
Taxonomic Assignment ◽  
High Performance Computing System ◽  
Powerful Approach ◽  
Performance Computing

Metagenomics is a powerful approach to study environment samples which do not require the isolation and cultivation of individual organisms. One of the essential tasks in a metagenomic project is to identify the origin of reads, referred to as taxonomic assignment. Due to the fact that each metagenomic project has to analyze large-scale datasets, the metatenomic assignment is very much computation intensive. This study proposes a parallel algorithm for the taxonomic assignment problem, called SeMetaPL, which aims to deal with the computational challenge. The proposed algorithm is evaluated with both simulated and real datasets on a high performance computing system. Experimental results demonstrate that the algorithm is able to achieve good performance and utilize resources of the system efficiently. The software implementing the algorithm and all test datasets can be downloaded at http://it.hcmute.edu.vn/bioinfo/metapro/SeMetaPL.html.

scholarly journals Enhanced Security In Cloud With Multi-Level Intrusion Detection System

2015 ◽  
pp. 9-12
Author(s):  
M. KUZHALISAI ◽  
G. GAYATHRI
Keyword(s):  
Cloud Computing ◽  
Intrusion Detection ◽  
Large Scale ◽  
Detection System ◽  
Computing System ◽  
Cyber Attacks ◽  
Security Level ◽  
Security Service ◽  
Cloud Computing System ◽  
System Resource

Cloud computing is a new type of service which provides large scale computing resource to each customer. Cloud Computing Systems can be easily threatened by various cyber attacks, because most of Cloud computing system needs to contain some Intrusion Detection Systems (IDS) for protecting each Virtual Machine (VM) against threats. In this case, there exists a tradeoff between the security level of the IDS and the system performance. If the IDS provide stronger security service using more rules or patterns, then it needs much more computing resources in proportion to the strength of security. So the amount of resources allocating for customers decreases. Another problem in Cloud Computing is that, huge amount of logs makes system administrators hard to analyse them. In this paper, we propose a method that enables cloud computing system to achieve both effectiveness of using the system resource and strength of the security service without trade-off between them.

Understanding pollution dynamics in large-scale peer-to-peer IPTV system

2012 ◽  
Vol 19 (8) ◽  
pp. 2203-2217 ◽  
Author(s):  
Hai-zhou Wang ◽  
Xing-shu Chen ◽  
Wen-xian Wang ◽  
Zheng-hong Hao
Keyword(s):  
Large Scale ◽  
Peer To Peer
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