scholarly journals Machine Learning in Wearable Biomedical Systems

2020 ◽  
Author(s):  
Muhammad E.H. Chowdhury ◽  
Amith Khandakar ◽  
Yazan Qiblawey ◽  
Mamun Bin Ibne Reaz ◽  
Mohammad Tariqul Islam ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Diabetic Foot ◽  
Heart Attack ◽  
Blood Pressure Monitoring ◽  
Algorithm Design ◽  
Health Condition ◽  
Attack Detection ◽  
Signal Acquisition ◽  
Biomedical Systems

Wearable technology has added a whole new dimension in the healthcare system by real-time continuous monitoring of human body physiology. They are used in daily activities and fitness monitoring and have even penetrated in monitoring the health condition of patients suffering from chronic illnesses. There are a lot of research and development activities being pursued to develop more innovative and reliable wearable. This chapter will cover discussions on the design and implementation of wearable devices for different applications such as real-time detection of heart attack, abnormal heart sound, blood pressure monitoring, gait analysis for diabetic foot monitoring. This chapter will also cover how the signals acquired from these prototypes can be used for training machine learning (ML) algorithm to diagnose the condition of the person wearing the device. This chapter discusses the steps involved in (i) hardware design including sensors selection, characterization, signal acquisition, and communication to decision-making subsystem and (ii) the ML algorithm design including feature extraction, feature reduction, training, and testing. This chapter will use the case study of the design of smart insole for diabetic foot monitoring, wearable real-time heart attack detection, and smart-digital stethoscope system to show the steps involved in the development of wearable biomedical systems.

scholarly journals Machine-Learning-Enabled DDoS Attacks Detection in P4 Programmable Networks

2021 ◽  
Vol 30 (1) ◽  
Author(s):  
Francesco Musumeci ◽  
Ali Can Fidanci ◽  
Francesco Paolucci ◽  
Filippo Cugini ◽  
Massimo Tornatore
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Transmission Control Protocol ◽  
Denial Of Service ◽  
Attack Detection ◽  
Ddos Attacks ◽  
Worst Case ◽  
Latency Reduction ◽  
Data Plane ◽  
Network Switches

Abstract Distributed Denial of Service (DDoS) attacks represent a major concern in modern Software Defined Networking (SDN), as SDN controllers are sensitive points of failures in the whole SDN architecture. Recently, research on DDoS attacks detection in SDN has focused on investigation of how to leverage data plane programmability, enabled by P4 language, to detect attacks directly in network switches, with marginal involvement of SDN controllers. In order to effectively address cybersecurity management in SDN architectures, we investigate the potential of Artificial Intelligence and Machine Learning (ML) algorithms to perform automated DDoS Attacks Detection (DAD), specifically focusing on Transmission Control Protocol SYN flood attacks. We compare two different DAD architectures, called Standalone and Correlated DAD, where traffic features collection and attack detection are performed locally at network switches or in a single entity (e.g., in SDN controller), respectively. We combine the capability of ML and P4-enabled data planes to implement real-time DAD. Illustrative numerical results show that, for all tested ML algorithms, accuracy, precision, recall and F1-score are above 98% in most cases, and classification time is in the order of few hundreds of $$\upmu \text {s}$$ μ s in the worst case. Considering real-time DAD implementation, significant latency reduction is obtained when features are extracted at the data plane by using P4 language. Graphic Abstract

scholarly journals Design a Monitoring Device for Heart-Attack Early Detection Based on Respiration Rate and Body Temperature Parameters

2021 ◽  
Vol 3 (3) ◽  
pp. 114-120
Author(s):  
Isna Fatimatuz Zahra ◽  
I Dewa Gede Hari Wisana ◽  
Priyambada Cahya Nugraha ◽  
Hayder J Hassaballah
Keyword(s):  
Body Temperature ◽  
Real Time ◽  
Respiration Rate ◽  
Heart Attack ◽  
Detection System ◽  
Warning System ◽  
Piezoelectric Sensor ◽  
Attack Detection ◽  
Sensor Data ◽  
Frequency Module

Acute myocardial infarction, commonly referred to as a heart attack, is the most common cause of sudden death where a monitoring tool is needed that is equipped with a system that can notify doctors to take immediate action. The purpose of this study was to design a heart attack detection device through indicators of vital human signs. The contribution of this research is that the system works in real-time, has more parameters, uses wireless, and is equipped with a system to detect indications of a heart attack. In order for wireless monitoring to be carried out in real-time and supported by a detection system, this design uses a radio frequency module as data transmission and uses a warning system that is used for detection. Respiration rate was measured using the piezoelectric sensor, and body temperature was measured using the DS18B20 temperature sensor. Processing of sensor data is done with ESP32, which is displayed wirelessly by the HC-12 module on the PC. If an indication of a heart attack is detected in the parameter value, the tool will activate a notification on the PC. In every indication of a heart attack, it was found that this design can provide notification properly. The results showed that the largest respiratory error value was 4%, and the largest body temperature error value was 0.55%. The results of this study can be implemented in patients who have been diagnosed with heart attack disease so that it can facilitate monitoring the patient's condition.

scholarly journals A Real-time LAN/WAN and Web Attack Prediction Framework Using Hybrid Machine Learning Model

2018 ◽  
Vol 7 (3.12) ◽  
pp. 1128
Author(s):  
Mohammad Arshad ◽  
Md. Ali Hussain
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Real Time ◽  
Learning Model ◽  
Attack Detection ◽  
Buffer Size ◽  
Learning Classifier ◽  
Network Intrusion ◽  
Machine Learning Model ◽  
Tools And Techniques

Real-time network attacks have become an increasingly serious issue to LAN/WAN security in recent years. As the size of the network flow increases, it becomes difficult to pre-process and analyze the network packets using the traditional network intrusion detection tools and techniques. Traditional NID tools and techniques require high computational memory and time to process large number of packets in incremental manner due to limited buffer size. Web intrusion detection is also one of the major threat to real-time web applications due to unauthorized user’s request to web server and online databases. In this paper, a hybrid real-time LAN/WAN and Web IDS model is designed and implemented using the machine learning classifier. In this model, different types of attacks are detected and labelled prior to train the machine learning model. Future network packets are predicted using the trained machine learning classifier for attack prediction. Experimental results are simulated on real-time LAN/WAN network and client-server web application for performance analysis. Simulated results show that the proposed machine learning based attack detection model is better than the traditional statistical and rule based learning models in terms of time, detection rate are concerned.  

scholarly journals The Heart Attack Detection by ESP8266 Data Communication at a Real Time to Avoid Sudden Death

2019 ◽  
Vol 1235 ◽  
pp. 012044
Author(s):  
Poltak Sihombing ◽  
Mangasa Manullang ◽  
Dahlan Sitompul ◽  
Imelda Sri Dumayanti
Keyword(s):  
Sudden Death ◽  
Real Time ◽  
Heart Attack ◽  
Data Communication ◽  
Attack Detection

scholarly journals DDoS Attack Detection and Classification using Machine Learning Models with Real-Time Dataset Created

2021 ◽  
Vol 9 (5) ◽  
pp. 145-153
Author(s):  
Harrsheeta Sasikumar
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Denial Of Service ◽  
Attack Detection ◽  
Machine Learning Algorithms ◽  
Detection Accuracy ◽  
Ddos Attack ◽  
Real Time Traffic ◽  
Server Breakdown ◽  
Networked Devices

Distributed Denial of Service (DDoS) attack is one of the common attack that is predominant in the cyber world. DDoS attack poses a serious threat to the internet users and affects the availability of services to legitimate users. DDOS attack is characterized by the blocking a particular service by paralyzing the victim’s resources so that they cannot be used to legitimate purpose leading to server breakdown. DDoS uses networked devices into remotely controlled bots and generates attack. The proposed system detects the DDoS attack and malware with high detection accuracy using machine learning algorithms. The real time traffic is generated using virtual instances running in a private cloud. The DDoS attack is detected by considering the various SNMP parameters and classifying using machine learning technique like bagging, boosting and ensemble models. Also, the various types of malware on the networked devices are prevent from being used as a bot for DDOS attack generation.

scholarly journals Wearable Real-Time Heart Attack Detection and Warning System to Reduce Road Accidents

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2780 ◽  
Author(s):  
Muhammad E. H. Chowdhury ◽  
Khawla Alzoubi ◽  
Amith Khandakar ◽  
Ridab Khallifa ◽  
Rayaan Abouhasera ◽  
...  
Keyword(s):  
Real Time ◽  
Heart Attack ◽  
The United States ◽  
Attack Detection ◽  
The Other ◽  
Support Vector ◽  
Linear Classification ◽  
Road Accidents ◽  
Time Frequency ◽  
St Elevation

Heart attack is one of the leading causes of human death worldwide. Every year, about 610,000 people die of heart attack in the United States alone—that is one in every four deaths—but there are well understood early symptoms of heart attack that could be used to greatly help in saving many lives and minimizing damages by detecting and reporting at an early stage. On the other hand, every year, about 2.35 million people get injured or disabled from road accidents. Unexpectedly, many of these fatal accidents happen due to the heart attack of drivers that leads to the loss of control of the vehicle. The current work proposes the development of a wearable system for real-time detection and warning of heart attacks in drivers, which could be enormously helpful in reducing road accidents. The system consists of two subsystems that communicate wirelessly using Bluetooth technology, namely, a wearable sensor subsystem and an intelligent heart attack detection and warning subsystem. The sensor subsystem records the electrical activity of the heart from the chest area to produce electrocardiogram (ECG) trace and send that to the other portable decision-making subsystem where the symptoms of heart attack are detected. We evaluated the performance of dry electrodes and different electrode configurations and measured overall power consumption of the system. Linear classification and several machine algorithms were trained and tested for real-time application. It was observed that the linear classification algorithm was not able to detect heart attack in noisy data, whereas the support vector machine (SVM) algorithm with polynomial kernel with extended time–frequency features using extended modified B-distribution (EMBD) showed highest accuracy and was able to detect 97.4% and 96.3% of ST-elevation myocardial infarction (STEMI) and non-ST-elevation MI (NSTEMI), respectively. The proposed system can therefore help in reducing the loss of lives from the growing number of road accidents all over the world.

Preemptive RMP-driven ELM crash suppression automated by a real-time machine-learning classifier in KSTAR

2021 ◽  
Author(s):  
Giwook Shin ◽  
Hyunsun Hahn ◽  
Minwoo Kim ◽  
Sang-Hee Hahn ◽  
WonHa Ko ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Control Algorithm ◽  
Algorithm Design ◽  
Mode Transition ◽  
Experimental Demonstration ◽  
Time Machine ◽  
Learning Classifier ◽  
Edge Localized Mode ◽  
Localized Mode

Abstract Suppression or mitigation of edge-localized mode (ELM) crashes is necessary for ITER. The strategy to suppress all the ELM crashes by the resonant magnetic perturbation (RMP) should be applied as soon as the first low-to-high confinement (L-H) transition occurs. A control algorithm based on real-time machine learning (ML) enables such an approach: it classifies the H-mode transition and the ELMy phase in real-time and automatically applies the preemptive RMP. This paper reports the algorithm design, which is now implemented in the KSTAR plasma-control system, and the corresponding experimental demonstration of typical high-δ KSTAR H-mode plasmas. As a result, all initial ELM crashes are suppressed with an acceptable safety factor at the edge (q95) and with RMP field adjustment. Moreover, the ML-driven ELM-crash-suppression discharges remain stable without further degradation due to the regularization of the plasma pedestal.

scholarly journals Real-Time DDoS Attack Detection System Using Big Data Approach

2021 ◽  
Vol 13 (19) ◽  
pp. 10743
Author(s):  
Mazhar Javed Awan ◽  
Umar Farooq ◽  
Hafiz Muhammad Aqeel Babar ◽  
Awais Yasin ◽  
Haitham Nobanee ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Real Time ◽  
Denial Of Service ◽  
Attack Detection ◽  
Testing Time ◽  
Ddos Attacks ◽  
Dos Attacks ◽  
Ddos Attack ◽  
Data Framework

Currently, the Distributed Denial of Service (DDoS) attack has become rampant, and shows up in various shapes and patterns, therefore it is not easy to detect and solve with previous solutions. Classification algorithms have been used in many studies and have aimed to detect and solve the DDoS attack. DDoS attacks are performed easily by using the weaknesses of networks and by generating requests for services for software. Real-time detection of DDoS attacks is difficult to detect and mitigate, but this solution holds significant value as these attacks can cause big issues. This paper addresses the prediction of application layer DDoS attacks in real-time with different machine learning models. We applied the two machine learning approaches Random Forest (RF) and Multi-Layer Perceptron (MLP) through the Scikit ML library and big data framework Spark ML library for the detection of Denial of Service (DoS) attacks. In addition to the detection of DoS attacks, we optimized the performance of the models by minimizing the prediction time as compared with other existing approaches using big data framework (Spark ML). We achieved a mean accuracy of 99.5% of the models both with and without big data approaches. However, in training and testing time, the big data approach outperforms the non-big data approach due to that the Spark computations in memory are in a distributed manner. The minimum average training and testing time in minutes was 14.08 and 0.04, respectively. Using a big data tool (Apache Spark), the maximum intermediate training and testing time in minutes was 34.11 and 0.46, respectively, using a non-big data approach. We also achieved these results using the big data approach. We can detect an attack in real-time in few milliseconds.

Sign in / Sign up

Export Citation Format

Share Document