A Novel Web Attack Detection System for Internet of Things via Ensemble Classification

Abstract The technology that continues to be developed by many researchers today is an automatic heart attack detection system based on an Electrocardiogram (ECG) signal. Several other studies have been carried out to build an Internet of Things (IoT) based heart abnormality detection system. Based on the analysis of related studies that have been carried out previously, several researchers have developed an ECG signal-based heart abnormality detection system using clean ECG signal data. While the reality of the concept of an IoT-based detection system, the process of recording ECG signal data on the sensor, the process of sending data to the server, and the process of retrieving data from the server are vulnerable to noise exposure. ECG signal containing noise will greatly affect the accuracy of system detection. This paper proposes the development of a noise-resistant heart condition detection system using a wavelet denoising algorithm. The process of denoising ECG signals using the Wavelet algorithm is generally able to improve the accuracy of detecting noisy ECG signals. The most significant increase in accuracy is seen in the low SNR value. The Daubechies 4 (db4) denoising algorithm is the best-performing algorithm. The ECG signal classification method uses the Artificial Neural Network (ANN) algorithm. Detection system hardware is also designed in this research using the concept based on the Internet of Things.

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SDN Based DDoS Attack Detection System by Exploiting Ensemble Classification for Cloud Computing

International Journal of Intelligent Engineering and Systems ◽

10.22266/ijies2018.1231.28 ◽

2018 ◽

Vol 11 (6) ◽

pp. 282-291

Author(s):

Sindia Vimala ◽

◽

Julia Dhas ◽

Keyword(s):

Cloud Computing ◽

Detection System ◽

Attack Detection ◽

Ensemble Classification ◽

Ddos Attack ◽

Ddos Attack Detection

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Design of Intrusion Detection System for Wormhole Attack Detection in Internet of Things

Advances in Intelligent Systems and Computing - Advanced Computing and Intelligent Engineering ◽

10.1007/978-981-15-1081-6_44 ◽

2020 ◽

pp. 513-523

Author(s):

Snehal Deshmukh-Bhosale ◽

S. S. Sonavane

Keyword(s):

Internet Of Things ◽

Intrusion Detection ◽

Intrusion Detection System ◽

Detection System ◽

Attack Detection ◽

Wormhole Attack

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False Data Detection for Fog and Internet of Things Networks

Sensors ◽

10.3390/s19194235 ◽

2019 ◽

Vol 19 (19) ◽

pp. 4235 ◽

Cited By ~ 3

Author(s):

Romano Fantacci ◽

Francesca Nizzi ◽

Tommaso Pecorella ◽

Laura Pierucci ◽

Manuel Roveri

Keyword(s):

Internet Of Things ◽

Detection System ◽

Fog Computing ◽

Data Gathering ◽

Attack Detection ◽

The Internet ◽

Data Detection ◽

Time Data ◽

Attack Tree ◽

Standard Data

The Internet of Things (IoT) context brings new security issues due to billions of smart end-devices both interconnected in wireless networks and connected to the Internet by using different technologies. In this paper, we propose an attack-detection method, named Data Intrusion Detection System (DataIDS), based on real-time data analysis. As end devices are mainly resource constrained, Fog Computing (FC) is introduced to implement the DataIDS. FC increases storage, computation capabilities, and processing capabilities, allowing it to detect promptly an attack with respect to security solutions on the Cloud. This paper also considers an attack tree to model threats and vulnerabilities of Fog/IoT scenarios with heterogeneous devices and suggests countermeasure costs. We verify the performance of the proposed DataIDS, implementing a testbed with several devices that measure different physical quantities and by using standard data-gathering protocols.

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