scholarly journals DVPTCP: A Delay-Driven Virtual Parallel TCP for High-Speed and Lossy Networks

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 99746-99753 ◽  
Author(s):  
Bo Su ◽  
Xianliang Jiang ◽  
Guang Jin ◽  
Aman Ma
Keyword(s):  
High Speed ◽  
Lossy Networks ◽  
Parallel Tcp

scholarly journals An Efficient Deep Learning-Based Detection and Classification System for Cyber-Attacks in IoT Communication Networks

2020 ◽  
Author(s):  
Qasem Abu Al-Haija ◽  
Charles McCurry ◽  
Saleh Zein-Sabatto
Keyword(s):  
Deep Learning ◽  
Communication Networks ◽  
Classification System ◽  
High Speed ◽  
Performance Metrics ◽  
Confusion Matrix ◽  
Primary Standard ◽  
Cyber Attacks ◽  
Rapid Expansion ◽  
Lossy Networks

With the rapid expansion of intelligent resource-constrained devices and high-speed communication technologies, Internet of Things (IoT) has earned a wide recognition as the primary standard for low-power lossy networks (LLNs). Nevertheless, IoT infrastructures are vulnerable to cyber-attacks due to the constraints in computation, storage, and communication capacity of the endpoint devices. From one side, the majority of newly developed cyber-attacks are formed by slightly mutating formerly established cyber-attacks to produce a new attack tending to be treated as a normal traffic through the IoT network. From the other side, the influence of coupling the deep learning techniques with cybersecurity field has become a recent inclination of many security applications due to their impressive performance. In this paper, we provide a comprehensive development of a new intelligent and autonomous deep learning-based detection and classification system for cyber-attacks in IoT communication networks leveraging the power of convolutional neural networks, abbreviated as (IoT-IDCS-CNN). The proposed IoT-IDCS-CNN makes use of the high-performance computing employing the robust CUDA based Nvidia GPUs and the parallel processing employing the high-speed I9-Cores based Intel CPUs. In particular, the proposed system is composed of three subsystems: Feature Engineering subsystem, Feature Learning subsystem and Traffic classification subsystem. All subsystems are developed, verified, integrated, and validated in this research. To evaluate the developed system, we employed the NSL-KDD dataset which includes all the key attacks in the IoT computing. The simulation results demonstrated more than 99.3% and 98.2% of cyber-attacks’ classification accuracy for the binary-class classifier (normal vs anomaly) and the multi-class classifier (five categories) respectively. The proposed system was validated using k-fold cross validation method and was evaluated using the confusion matrix parameters (i.e., TN, TP, FN, FP) along with other classification performance metrics including precision, recall, F1-score, and false alarm rate. The test and evaluation results of the IoT-IDCS-CNN system outperformed many recent machine-learning based IDCS systems in the same area of study.

scholarly journals An Efficient Deep Learning-Based Detection and Classification System for Cyber-Attacks in IoT Communication Networks

2020 ◽  
Author(s):  
Qasem Abu Al-Haija ◽  
Saleh Zein-Sabatto
Keyword(s):  
Deep Learning ◽  
Communication Networks ◽  
Classification System ◽  
High Speed ◽  
Performance Metrics ◽  
Confusion Matrix ◽  
Primary Standard ◽  
Cyber Attacks ◽  
Rapid Expansion ◽  
Lossy Networks

With the rapid expansion of intelligent resource-constrained devices and high-speed communication technologies, Internet of Things (IoT) has earned a wide recognition as the primary standard for low-power lossy networks (LLNs). Nevertheless, IoT infrastructures are vulnerable to cyber-attacks due to the constraints in computation, storage, and communication capacity of the endpoint devices. From one side, the majority of newly developed cyber-attacks are formed by slightly mutating formerly established cyber-attacks to produce a new attack tending to be treated as a normal traffic through the IoT network. From the other side, the influence of coupling the deep learning techniques with cybersecurity field has become a recent inclination of many security applications due to their impressive performance. In this paper, we provide a comprehensive development of a new intelligent and autonomous deep learning-based detection and classification system for cyber-attacks in IoT communication networks leveraging the power of convolutional neural networks, abbreviated as (IoT-IDCS-CNN). The proposed IoT-IDCS-CNN makes use of the high-performance computing employing the robust CUDA based Nvidia GPUs and the parallel processing employing the high-speed I9-Cores based Intel CPUs. In particular, the proposed system is composed of three subsystems: Feature Engineering subsystem, Feature Learning subsystem and Traffic classification subsystem. All subsystems are developed, verified, integrated, and validated in this research. To evaluate the developed system, we employed the NSL-KDD dataset which includes all the key attacks in the IoT computing. The simulation results demonstrated more than 99.3% and 98.2% of cyber-attacks’ classification accuracy for the binary-class classifier (normal vs anomaly) and the multi-class classifier (five categories) respectively. The proposed system was validated using k-fold cross validation method and was evaluated using the confusion matrix parameters (i.e., TN, TP, FN, FP) along with other classification performance metrics including precision, recall, F1-score, and false alarm rate. The test and evaluation results of the IoT-IDCS-CNN system outperformed many recent machine-learning based IDCS systems in the same area of study.

scholarly journals An Efficient Deep-Learning-Based Detection and Classification System for Cyber-Attacks in IoT Communication Networks

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2152
Author(s):  
Qasem Abu Al-Haija ◽  
Saleh Zein-Sabatto
Keyword(s):  
Deep Learning ◽  
Communication Networks ◽  
Classification System ◽  
High Speed ◽  
Confusion Matrix ◽  
Primary Standard ◽  
False Negative ◽  
Cyber Attacks ◽  
Rapid Expansion ◽  
Lossy Networks

With the rapid expansion of intelligent resource-constrained devices and high-speed communication technologies, the Internet of Things (IoT) has earned wide recognition as the primary standard for low-power lossy networks (LLNs). Nevertheless, IoT infrastructures are vulnerable to cyber-attacks due to the constraints in computation, storage, and communication capacity of the endpoint devices. From one side, the majority of newly developed cyber-attacks are formed by slightly mutating formerly established cyber-attacks to produce a new attack that tends to be treated as normal traffic through the IoT network. From the other side, the influence of coupling the deep learning techniques with the cybersecurity field has become a recent inclination of many security applications due to their impressive performance. In this paper, we provide the comprehensive development of a new intelligent and autonomous deep-learning-based detection and classification system for cyber-attacks in IoT communication networks that leverage the power of convolutional neural networks, abbreviated as IoT-IDCS-CNN (IoT based Intrusion Detection and Classification System using Convolutional Neural Network). The proposed IoT-IDCS-CNN makes use of high-performance computing that employs the robust Compute Unified Device Architectures (CUDA) based Nvidia GPUs (Graphical Processing Units) and parallel processing that employs high-speed I9-core-based Intel CPUs. In particular, the proposed system is composed of three subsystems: a feature engineering subsystem, a feature learning subsystem, and a traffic classification subsystem. All subsystems were developed, verified, integrated, and validated in this research. To evaluate the developed system, we employed the Network Security Laboratory-Knowledge Discovery Databases (NSL-KDD) dataset, which includes all the key attacks in IoT computing. The simulation results demonstrated a greater than 99.3% and 98.2% cyber-attack classification accuracy for the binary-class classifier (normal vs. anomaly) and the multiclass classifier (five categories), respectively. The proposed system was validated using a K-fold cross-validation method and was evaluated using the confusion matrix parameters (i.e., true negative (TN), true positive (TP), false negative (FN), false positive (FP)), along with other classification performance metrics, including precision, recall, F1-score, and false alarm rate. The test and evaluation results of the IoT-IDCS-CNN system outperformed many recent machine-learning-based IDCS systems in the same area of study.

Microfabrication research at the National Submicron Facility

1983 ◽  
Vol 41 ◽  
pp. 86-89
Author(s):  
E.D. Wolf
Keyword(s):  
Integrated Circuits ◽  
Particle Physics ◽  
High Speed ◽  
New Technology ◽  
High Energy ◽  
High Energy Particle ◽  
New Science ◽  
Wide Range ◽  
Intermediate Domain ◽  
Circuit Function

Most microelectronics devices and circuits operate faster, consume less power, execute more functions and cost less per circuit function when the feature-sizes internal to the devices and circuits are made smaller. This is part of the stimulus for the Very High-Speed Integrated Circuits (VHSIC) program. There is also a need for smaller, more sensitive sensors in a wide range of disciplines that includes electrochemistry, neurophysiology and ultra-high pressure solid state research. There is often fundamental new science (and sometimes new technology) to be revealed (and used) when a basic parameter such as size is extended to new dimensions, as is evident at the two extremes of smallness and largeness, high energy particle physics and cosmology, respectively. However, there is also a very important intermediate domain of size that spans from the diameter of a small cluster of atoms up to near one micrometer which may also have just as profound effects on society as “big” physics.

Vacuum System to Minimize the Specimen Contamination of High-Performance EM

1977 ◽  
Vol 35 ◽  
pp. 68-69
Author(s):  
N. Yoshimura ◽  
K. Shirota ◽  
T. Etoh
Keyword(s):  
Electron Microscope ◽  
High Speed ◽  
High Performance ◽  
High Vacuum ◽  
Vacuum System ◽  
Pump System ◽  
Pumping System ◽  
Diffusion Pump ◽  
Almost All ◽  
Cascade Type

One of the most important requirements for a high-performance EM, especially an analytical EM using a fine beam probe, is to prevent specimen contamination by providing a clean high vacuum in the vicinity of the specimen. However, in almost all commercial EMs, the pressure in the vicinity of the specimen under observation is usually more than ten times higher than the pressure measured at the punping line. The EM column inevitably requires the use of greased Viton O-rings for fine movement, and specimens and films need to be exchanged frequently and several attachments may also be exchanged. For these reasons, a high speed pumping system, as well as a clean vacuum system, is now required. A newly developed electron microscope, the JEM-100CX features clean high vacuum in the vicinity of the specimen, realized by the use of a CASCADE type diffusion pump system which has been essentially improved over its predeces- sorD employed on the JEM-100C.

The on-line use of histogram data for high-speed correction and alignment of electron microscope images

1984 ◽  
Vol 42 ◽  
pp. 556-557
Author(s):  
William Krakow
Keyword(s):  
Power Spectrum ◽  
High Speed ◽  
Direct Memory Access ◽  
Digital Television ◽  
Contrast Transfer Function ◽  
The Past ◽  
High Resolution Tem ◽  
On Line ◽  
Correction Of Astigmatism ◽  
The Fourier Transform

In the past few years on-line digital television frame store devices coupled to computers have been employed to attempt to measure the microscope parameters of defocus and astigmatism. The ultimate goal of such tasks is to fully adjust the operating parameters of the microscope and obtain an optimum image for viewing in terms of its information content. The initial approach to this problem, for high resolution TEM imaging, was to obtain the power spectrum from the Fourier transform of an image, find the contrast transfer function oscillation maxima, and subsequently correct the image. This technique requires a fast computer, a direct memory access device and even an array processor to accomplish these tasks on limited size arrays in a few seconds per image. It is not clear that the power spectrum could be used for more than defocus correction since the correction of astigmatism is a formidable problem of pattern recognition.

Structural changes in silicon-on-insulator material during post-implantation annealing

1986 ◽  
Vol 44 ◽  
pp. 736-737
Author(s):  
C. O. Jung ◽  
S. J. Krause ◽  
S.R. Wilson
Keyword(s):  
Integrated Circuits ◽  
Oxide Layer ◽  
High Speed ◽  
Structural Changes ◽  
Device Fabrication ◽  
Silicon On Insulator ◽  
High Quality ◽  
Radiation Hardened ◽  
Post Implantation ◽  
Very High

Silicon-on-insulator (SOI) structures have excellent potential for future use in radiation hardened and high speed integrated circuits. For device fabrication in SOI material a high quality superficial Si layer above a buried oxide layer is required. Recently, Celler et al. reported that post-implantation annealing of oxygen implanted SOI at very high temperatures would eliminate virtually all defects and precipiates in the superficial Si layer. In this work we are reporting on the effect of three different post implantation annealing cycles on the structure of oxygen implanted SOI samples which were implanted under the same conditions.

An atomic-resolution microscope study of Al contracts on GaAs

1986 ◽  
Vol 44 ◽  
pp. 726-727
Author(s):  
Z. Liliental-Weber ◽  
C. Nelson ◽  
R. Ludeke ◽  
R. Gronsky ◽  
J. Washburn
Keyword(s):  
High Speed ◽  
Schottky Contacts ◽  
Detailed Knowledge ◽  
The Past ◽  
Semiconductor Interfaces ◽  
Deposited Metal ◽  
Microwave Applications ◽  
Free Interfaces ◽  
Potential Use

The properties of metal/semiconductor interfaces have received considerable attention over the past few years, and the Al/GaAs system is of special interest because of its potential use in high-speed logic integrated optics, and microwave applications. For such materials a detailed knowledge of the geometric and electronic structure of the interface is fundamental to an understanding of the electrical properties of the contact. It is well known that the properties of Schottky contacts are established within a few atomic layers of the deposited metal. Therefore surface contamination can play a significant role. A method for fabricating contamination-free interfaces is absolutely necessary for reproducible properties, and molecularbeam epitaxy (MBE) offers such advantages for in-situ metal deposition under UHV conditions

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