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Detailed Case Studies

Abuimara, Tareq (author)
Kopányi, Attila (author)
Rouleau, Jean (author)
Universite Laval
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Kang, Ye (author)
Monash University
Sonta, Andrew (author)
Ecole Polytechnique Federale de Lausanne (EPFL),Swiss Federal Institute of Technology in Lausanne (EPFL)
Derbas, Ghadeer (author)
Bergische Universität Wuppertal
Jin, Quan, 1983 (author)
Chalmers tekniska högskola,Chalmers University of Technology
O'brien, William (author)
Carleton University
Gunay, Burak (author)
Carleton University
Carrizo, Juan Sebastián (author)
Bukovszki, Viktor (author)
Reith, András (author)
Gosselin, Louis (author)
Universite Laval
Zhou, Jenny (author)
Monash University
Dougherty, Thomas (author)
Stanford University
Jain, Rishee (author)
Stanford University
Voss, Karsten (author)
Bergische Universität Wuppertal
Mitic, Tugcin Kirant (author)
Bergische Universität Wuppertal
Wallbaum, Holger, 1967 (author)
Chalmers tekniska högskola,Chalmers University of Technology
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 (creator_code:org_t)
2023
2023
English.
In: Occupant-Centric Simulation-Aided Building Design. - 9781000865752 ; , s. 257-367
  • Book chapter (other academic/artistic)
Abstract Subject headings
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  • Wireless body area networks (WBANs) are one of the key technologies that support the development of pervasive health monitoring (remote patient monitoring systems), which has attracted more attention in recent years. These WBAN applications requires stringent security requirements as they are concerned with human lives. In the recent scenario of the corona pandemic, where most of the healthcare providers are giving online services for treatment, DDoS attacks become the major threats over the internet. This chapter particularly focusses on detection of DDoS attack using machine learning algorithms over the healthcare environment. In the process of attack detection, the dataset is preprocessed. After preprocessing the dataset, the cleaned dataset is given to the popular classification algorithms in the area of machine learning namely, AdaBoost, J48, k-NN, JRip, Random Committee and Random Forest classifiers. Those algorithms are evaluated independently and the results are recorded. Results concluded that J48 outperform with accuracy of 99.98% with CICIDS dataset and random forest outperform with accuracy of 99.917, but it takes the longest model building time. Depending on the evaluation performance the appropriate classifier is selected for further DDoS detection at real-time.

Subject headings

NATURVETENSKAP  -- Data- och informationsvetenskap -- Medieteknik (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Media and Communication Technology (hsv//eng)
SAMHÄLLSVETENSKAP  -- Ekonomi och näringsliv -- Företagsekonomi (hsv//swe)
SOCIAL SCIENCES  -- Economics and Business -- Business Administration (hsv//eng)
NATURVETENSKAP  -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Datorsystem (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Computer Systems (hsv//eng)

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