scholarly journals Real-Time Cyber-Physical False Data Attack Detection in Smart Grids Using Neural Networks

2017 ◽  
Author(s):  
Erik M. Ferragut ◽  
Jason Laska ◽  
Mohammed M. Olama ◽  
Ozgur Ozmen
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Smart Grids ◽  
Attack Detection

Real-Time Smart Grids Control for Preventing Cascading Failures and Blackout using Neural Networks: Experimental Approach for N-1-1 Contingency

2016 ◽  
Vol 17 (6) ◽  
pp. 703-716 ◽  
Author(s):  
Sina Zarrabian ◽  
Rabie Belkacemi ◽  
Adeniyi A. Babalola
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Smart Grids ◽  
Intelligent Systems ◽  
Large Scale ◽  
Frequency Control ◽  
Test System ◽  
Selection Strategy ◽  
Control Approach ◽  
Contingency Condition

Abstract In this paper, a novel intelligent control is proposed based on Artificial Neural Networks (ANN) to mitigate cascading failure (CF) and prevent blackout in smart grid systems after N-1-1 contingency condition in real-time. The fundamental contribution of this research is to deploy the machine learning concept for preventing blackout at early stages of its occurrence and to make smart grids more resilient, reliable, and robust. The proposed method provides the best action selection strategy for adaptive adjustment of generators’ output power through frequency control. This method is able to relieve congestion of transmission lines and prevent consecutive transmission line outage after N-1-1 contingency condition. The proposed ANN-based control approach is tested on an experimental 100 kW test system developed by the authors to test intelligent systems. Additionally, the proposed approach is validated on the large-scale IEEE 118-bus power system by simulation studies. Experimental results show that the ANN approach is very promising and provides accurate and robust control by preventing blackout. The technique is compared to a heuristic multi-agent system (MAS) approach based on communication interchanges. The ANN approach showed more accurate and robust response than the MAS algorithm.

scholarly journals Locate-Then-Detect: Real-time Web Attack Detection via Attention-based Deep Neural Networks

2019 ◽  
Author(s):  
Tianlong Liu ◽  
Yu Qi ◽  
Liang Shi ◽  
Jianan Yan
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Web Application ◽  
Deep Neural Networks ◽  
Attack Detection ◽  
Detection Accuracy ◽  
Learning Approaches ◽  
Web Traffic ◽  
Sql Injection ◽  
Web Attacks

Web attacks such as Cross-Site Scripting and SQL Injection are serious Web threats that lead to catastrophic data leaking and loss. Because attack payloads are often short segments hidden in URL requests/posts that can be very long, classical machine learning approaches have difficulties in learning useful patterns from them. In this study, we propose a novel Locate-Then-Detect (LTD) system that can precisely detect Web threats in real-time by using attention-based deep neural networks. Firstly, an efficient Payload Locating Network (PLN) is employed to propose most suspicious regions from large URL requests/posts. Then a Payload Classification Network (PCN) is adopted to accurately classify malicious regions from suspicious candidates. In this way, PCN can focus more on learning malicious segments and highly increase detection accuracy. The noise induced by irrelevant background strings can be largely eliminated. Besides, LTD can greatly reduce computational costs (82.6% less) by ignoring large irrelevant URL content. Experiments are carried out on both benchmarks and real Web traffic. The LTD outperforms an HMM-based approach, the Libinjection system, and a leading commercial rule-based Web Application Firewall. Our method can be efficiently implemented on GPUs with an average detection time of about 5ms and well qualified for real-time applications.

Real Time Face Driven Speech Animation Using Neural Networks in with Expressions

2019 ◽  
Vol 7 (5) ◽  
pp. 781-786
Author(s):  
K. Rajasekhar ◽  
C. Usharani ◽  
A. Mrinalini
Keyword(s):  
Neural Networks ◽  
Real Time

Real-time Detection of Aortic Valve in Echocardiography using Convolutional Neural Networks

2020 ◽  
Vol 16 (5) ◽  
pp. 584-591 ◽  
Author(s):  
Muhammad Hanif Ahmad Nizar ◽  
Chow Khuen Chan ◽  
Azira Khalil ◽  
Ahmad Khairuddin Mohamed Yusof ◽  
Khin Wee Lai
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Heart Disease ◽  
Aortic Valve ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Valvular Heart Disease ◽  
Detection System ◽  
Processing Unit ◽  
Real Time Detection

Background: Valvular heart disease is a serious disease leading to mortality and increasing medical care cost. The aortic valve is the most common valve affected by this disease. Doctors rely on echocardiogram for diagnosing and evaluating valvular heart disease. However, the images from echocardiogram are poor in comparison to Computerized Tomography and Magnetic Resonance Imaging scan. This study proposes the development of Convolutional Neural Networks (CNN) that can function optimally during a live echocardiographic examination for detection of the aortic valve. An automated detection system in an echocardiogram will improve the accuracy of medical diagnosis and can provide further medical analysis from the resulting detection. Methods: Two detection architectures, Single Shot Multibox Detector (SSD) and Faster Regional based Convolutional Neural Network (R-CNN) with various feature extractors were trained on echocardiography images from 33 patients. Thereafter, the models were tested on 10 echocardiography videos. Results: Faster R-CNN Inception v2 had shown the highest accuracy (98.6%) followed closely by SSD Mobilenet v2. In terms of speed, SSD Mobilenet v2 resulted in a loss of 46.81% in framesper- second (fps) during real-time detection but managed to perform better than the other neural network models. Additionally, SSD Mobilenet v2 used the least amount of Graphic Processing Unit (GPU) but the Central Processing Unit (CPU) usage was relatively similar throughout all models. Conclusion: Our findings provide a foundation for implementing a convolutional detection system to echocardiography for medical purposes.

Real Time early Multi Stage Attack Detection

2021 ◽  
Author(s):  
Yuvraj Sanjayrao Takey ◽  
Sai Gopal Tatikayala ◽  
Satyanadha Sarma Samavedam ◽  
P R Lakshmi Eswari ◽  
Mahesh Uttam Patil
Keyword(s):  
Real Time ◽  
Attack Detection ◽  
Multi Stage
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