Search query: network   emulation impairment injection

Related papers:

abstract: Mismatch of power supply integrity of power delivery network (PDN) between an automatic test equipment (ATE) and a customer board can lead to test failures such as overkills or underkills during semiconductor test. This paper proposes a technique to control the power supply impedance on an ATE using compensation current injection so that it emulates the impedance on a customer board. Digital filter is used to calculate compensation current waveform in real time in response to the measured power supply voltage fluctuation. Its implementation is based on the PDNs of both the ATE and the customer board. This paper introduces the filter implementation methodology for arbitrary PDN using nested feedback loop of digital filter, as well as experimental results of prototype circuits. This paper demonstrates that with the proposed current injection the power supply voltage fluctuation waveform on the ATE matches up with that on the customer board, which means that the compensation current injection successfully controls and emulates the impedance. With the proposed technique test failures caused by the impedance difference between the two environments are reduced.
author_names: Naoki Terao

Toru Nakura

Masahiro Ishida

Rimon Ikeno

Takashi Kusaka

Tetsuya Iizuka

Kunihiro Asada
title: Digitally Controlled Compensation Current Injection to ATE Power Supply for Emulation of Customer Environment
venue: J. Electron. Test.
year: 2018

abstract: We explore and demonstrate the extension of the synapse mimicking properties of memristive devices to a dysfunctional synapse as it occurs in the Alzheimer's disease (AD) pathology. The ability of memristive devices to reproduce synapse properties such as LTP, LTD, and STDP has been already widely demonstrated, and moreover, they were used for developing artificial neuron networks (perceptrons) able to simulate the information transmission in a cell network. However, a major progress would be to extend the common sense of neuromorphic device even to the case of dysfunction of natural synapses. Can memristors efficiently simulate them? We provide here evidences of the ability of emulating the dysfunctional synaptic behavior typical of the AD pathology with organic memristive devices considering the effect of the disease not only on a single synapse but also in the case of a neural network, composed by numerous synapses.
author_names: Silvia Battistoni

Victor V Erokhin

Salvatore Iannotta
title: Emulation with Organic Memristive Devices of Impairment of LTP Mechanism in Neurodegenerative Disease Pathology
venue: Neural plasticity
year: 2017

abstract: The design and development of a complex system requires an adequate methodology and efficient instrumental support in order to early detect and correct anomalies in the functional and non functional properties of the tested protocols. Among the various tools used to provide experimental support for such developments, network emulation relies on real time production of impairments on real traffic according to a communication model, either realistically or not. This paper aims at simply presenting to newcomers in network emulation (students, engineers, etc. basic principles and practices illustrated with a few commonly used tools. The motivation behind is to fill a gap in terms of introductory and pragmatic papers in this domain. The study particularly considers centralized approaches, allowing cheap and easy implementation in the context of research labs or industrial developments. In addition, an architectural model for emulation systems is proposed, defining three complementary levels, namely hardware, impairment, and model levels. With the help of this architectural framework, various existing tools are situated and described. Various approaches for modeling the emulation actions are studied, such as impairment based scenarios and virtual architectures, real time discrete simulation, and trace based systems. Those modeling approaches are described and compared in terms of services, and we study their ability to respond to various designer needs to assess when emulation is needed.
author_names: Emmanuel Lochin

Tanguy Perennou

Laurent Dairaine
title: When should I use network emulation?
venue: Ann. des Telecommunications
year: 2012

abstract: Problem based learning (PBL) is a pedagogical method that challenges students to "learn how to learn" by working cooperatively in groups to develop solutions to real world problems. Network in a Box (NiB) is a unique, engineering oriented effort to employ PBL in core computer networks curriculum. Developed as an open ended exercise in PBL, NiB sets a learning environment in which student teams design and implement a network emulator using off the shelf hardware and open source software. The aims of this article are twofold. The first goal of this article is to catalogue our efforts at San Diego State University in implementing PBL in a computer networks engineering course. The second goal of this article is to introduce the NiB project as a viable example of PBL in the field of computer networks. This project not only arms students with a good understanding of network impairments but also provides the knowledge of low level packet processing, packet creation, packet injection into the network, meeting real time processing requirements of packet processing and packet filtering. By arming students with an understanding of network impairments, a working knowledge of low level packet processing, packet creation, packet injection, and their processing requirements, NiB serves as a model introduction of PBL into the field of computer networks engineering. In this article we disseminate the product specification and system design as an educational and research utility along with an assessment of this project as perceived by students over several semesters. (c) 2009 Wiley Periodicals, Inc. Comput Appl Eng Educ 19: 433 446, 2011
author_names: Yusuf Ozturk
title: Network in a Box: Facilitating problem based learning through network emulation
year: 2011

abstract: In recent years, Deep Neural Networks have been increasingly adopted by a wide range of applications characterized by high reliability requirements, such as aerospace and automotive. In this paper, we propose an FPGA based platform for emulating faults in the architecture of DNNs. The approach exploits the reconfigurability of FPGAs to mimic faults affecting the hardware implementing DNNs. The platform allows the emulation of various kinds of fault models enabling the possibility to adapt to different types, devices, and architectures. In this work, a fault injection campaign has been performed on a convolutional layer of AlexNet, demonstrating the feasibility of the platform. Furthermore, the errors induced in the layer are analyzed with respect to the impact on the whole network inference classification.
author_names: Corrado De Sio

Sarah Azimi

Luca Sterpone
title: An Emulation Platform for Evaluating the Reliability of Deep Neural Networks
venue: 2020 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT)
year: 2020

abstract: Code injection attacks against server and client applications have become the primary method of malware spreading. A promising approach for the detection of previously unknown code injection attacks at the network level, irrespective of the particular exploitation method used or the vulnerability being exploited, is to identify the malicious code that is part of the attack vector, also known as shellcode. Initial implementations of this approach attempt to identify the presence of shellcode in network inputs using detection algorithms based on static code analysis. However, static analysis cannot effectively handle malicious code that employs advanced obfuscation methods such as anti disassembly tricks or self modifying code, and thus these detection methods can be easily evaded. In this dissertation we present network level emulation, a generic code injection attack detection method based on dynamic code analysis using emulation. Our prototype attack detection system, called Nemu, uses a CPU emulator to dynamically analyze valid instruction sequences in the inspected traffic. Based on runtime behavioral heuristics, the system identifies inherent patterns exhibited during the execution of the shellcode, and thus can detect the presence of malicious code in arbitrary inputs. We have developed heuristics that cover the most widely used shellcode types, including self decrypting and non self contained polymorphic shellcode, plain or metamorphic shellcode, and memory scanning shellcode. Network level emulation does not rely on any exploit or vulnerability specific signatures, which allows the detection of previously unknown attacks. At the same time, the actual execution of the attack code on a CPU emulator makes the detector robust to evasion techniques like indirect jumps and self modifications. Furthermore, each input is inspected autonomously, which makes the approach effective against targeted attacks. Our experimental evaluation with publicly available shellcode construction engines, attack toolkits, and real attacks captured in the wild, shows that Nemu is more robust to obfuscation techniques compared to previous proposals, while it can effectively detect a broad range of different shellcode implementations without any prior exploit specific information. At the same time, extensive testing using benign generated and real data did not produce any false positives. To assess the effectiveness of our approach under realistic conditions we deployed Nemu in several production networks. Over the course of more than one year of continuous operation, Nemu detected more than 1.2 million attacks against real systems. We provide a thorough analysis of the captured attacks, focusing on the structure and operation of the shellcode, as well as the overall attack activity in relation to the different targeted services. The large and diverse set of the detected attacks combined with the zero false positive rate over the whole monitoring period demonstrate the effectiveness and practicality of our approach. Finally, we identify challenges faced by existing network trace anonymization schemes for safely sharing attack traces that contain self decrypting shellcode. To alleviate this problem, we present an anonymization method that identifies and properly sanitizes sensitive information contained in the encrypted part of the shellcode that is otherwise not exposed on the wire. Thesis Advisor: Prof. Evangelos Markatos
author_names: Michalis Polychronakis
title: Generic Detection of Code Injection Attacks using Network level Emulation
year: 2009

abstract: Western medicine (WM) has certain limitations in terms of treating acute cerebral infarction (ACI) while tonic traditional Chinese medicine injections (TCMIs) have been shown to have obvious clinical effects as an adjunct to WM for ACI. However, most randomized controlled trials (RCTs) to date have not performed direct comparisons of efficacy among tonic TCMIs. This study designed a Bayesian network meta analysis (NMA) to explore the therapeutic effect of tonic TCMIs on ACI. A comprehensive search of RCTs of TCMIs combined with WM for ACI was conducted using electronic databases for studies dated from the start date of each database until February 2020. Stata 13.0 and ADDIS 1.16.7 software were used to plot and analyze the data. Sixty six RCTs with a total of 5,989 patients involving 7 kinds of tonic TCMIs were included. Among TCMIs, Shenfu injection (SFI) WM ranked first in terms of improving clinical efficacy and the activities of daily living (ADLs) rating and reducing interleukin 6 (IL 6) and tumor necrosis factor a (TNF a) levels. While Ciwujia injection (CI) WM was the best choice for reducing neurological impairment and the high cut viscosity of whole blood (HCV) Shenmai injection (SI) WM had the greatest effects in terms of decreasing the levels of low cut viscosity of whole blood (LCV) fibrinogen (FIB) and plasma viscosity (PV) Based on the cluster analysis of the clinical efficacy and the neurological impairment, CI WM and Shenqifuzheng (SQI) WM were the best options for treating ACI. With respect to adverse drug reactions (ADRs) 35 RCTs did not monitor ADRs during treatment. In conclusion, tonic TCMIs could assist WM in benefiting patients with ACI. However, due to the limitations of the current study, strict monitoring of ADRs and data from high quality RCTs will be required in future to verify the advantage of TCMIs.
author_names: Dongrui Zhou

Liandi Xie

Yao Wang

Shuang Wu

Fengzhi Liu

Shuangshuang Zhang

Rui-jia Liu

Lingqun Zhu
title: Clinical Efficacy of Tonic Traditional Chinese Medicine Injection on Acute Cerebral Infarction: A Bayesian Network Meta Analysis
venue: Evidence based complementary and alternative medicine eCAM
year: 2020

abstract: Water injection is one of the robust techniques to maintain the reservoir pressure and produce trapped oil from oil reservoirs and improve an oil recovery factor. However, incompatibility between injected water and reservoir water causes an unflavored issue named "scale deposition." Owing to the deposited scales, effective permeability of a reservoir reduced, and pore throats might be plugged. To determine formation damage owing to scale deposition during a water injection process, two well known machine learning methods, least squares support vector machine (LSSVM) and artificial neural network (ANN) are employed in the present paper. To improve the performance of the LSSVM method, a metaheuristic optimization algorithm, genetic algorithm (GA) is used. The constructed LSSVM model is examined using real formation damage data samples experimentally measured, which was reported in the literature. According to the obtained outputs of the above models, LSSVM has a high performance based on the correlation coefficient, and infinitesimal uncertainty based on a relative error between the model predictions and the corresponding actual data samples was less than 15% Outcomes from this study indicate the useful application of the LSSVM approach in the prediction of permeability reduction due to scale deposition, and it can lead to a better and more reliable understanding of formation damage effects through water flooding without expensive laboratory measurements.
author_names: Mohammad Ali Ahmadi

Zhangxin Chen
title: Machine learning based models for predicting permeability impairment due to scale deposition
venue: Journal of Petroleum Exploration and Production Technology
year: 2020

author_names: Mikhael Polukhronakes
title: Generic detection of code injection attacks using network level emulation
year: 2009

abstract: Emulation of tactical wireless networks in a laboratory environment provides a cost effective solution for development tests and serves as a risk reduction for formal operational tests. However, laboratory based emulation of tactical Mobile Ad Hoc Networks (MANETs) is particularly challenging due to the complex nature of the underlying environment. Unlike point to point systems, emulation of MANETs requires the execution of multiple channels in parallel. Furthermore, tactical wireless channels are susceptible to impairments such as fading, Doppler, multipath, and jamming, which further complicates the ability to replicate such environments in a controlled laboratory setting. In this paper, we evaluate a previously developed Digital Wireless Channel Emulator (DWCE) by comparing field test results with data collected in an emulated environment for a 29 node tactical MANET. We present our approach for obtaining relevant metrics from the field and describe our methodology for comparison from both a qualitative and quantitative perspective.
author_names: Scott Buscemi

Steven W Boyd
title: A Comparative Analysis of Lab Based Network Emulation with Field Results for Large Scale MANETs
venue: 2014 IEEE Military Communications Conference
year: 2014