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A Machine-Learning-...
A Machine-Learning-Based Technique for False Data Injection Attacks Detection in Industrial IoT
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- Aboelwafa, Mariam M. N. (author)
- Electronics and Communications Engineering Department, American University in Cairo, New Cairo, Egypt
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- Seddik, Karim G. (author)
- Electronics and Communications Engineering Department, American University in Cairo, New Cairo, Egypt
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- Eldefrawy, Mohamed Hamdy, 1981- (author)
- Högskolan i Halmstad,Centrum för forskning om inbyggda system (CERES),Halmstad University, Sweden
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- Gadallah, Yasser (author)
- Electronics and Communications Engineering Department, American University in Cairo, New Cairo, Egypt
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- Gidlund, Mikael, 1972- (author)
- Mittuniversitetet,Institutionen för informationssystem och –teknologi,Communication Systems and Networks (CSN),Department of Information Systems and Technology, Mid Sweden University, Sundsvall, Sweden
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(creator_code:org_t)
- Piscataway : Institute of Electrical and Electronics Engineers (IEEE), 2020
- 2020
- English.
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In: IEEE Internet of Things Journal. - Piscataway : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4662. ; 7:9, s. 8462-8471
- Related links:
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https://doi.org/10.1...
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Abstract
Subject headings
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- The accelerated move toward the adoption of the Industrial Internet-of-Things (IIoT) paradigm has resulted in numerous shortcomings as far as security is concerned. One of the IIoT affecting critical security threats is what is termed as the false data injection (FDI) attack. The FDI attacks aim to mislead the industrial platforms by falsifying their sensor measurements. FDI attacks have successfully overcome the classical threat detection approaches. In this article, we present a novel method of FDI attack detection using autoencoders (AEs). We exploit the sensor data correlation in time and space, which in turn can help identify the falsified data. Moreover, the falsified data are cleaned using the denoising AEs (DAEs). Performance evaluation proves the success of our technique in detecting FDI attacks. It also significantly outperforms a support vector machine (SVM)-based approach used for the same purpose. The DAE data cleaning algorithm is also shown to be very effective in recovering clean data from corrupted (attacked) data. © 2014 IEEE.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Datorsystem (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Computer Systems (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorteknik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Engineering (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Kommunikationssystem (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Communication Systems (hsv//eng)
Keyword
- Correlation
- Support vector machines
- Security
- Training
- Noise reduction
- Feature extraction
- Autoencoders (AEs)
- false data injection (FDI) attacks
- Industrial Internet-of-Things (IIoT) security
- machine learning (ML)
- support vector machine (SVM)
Publication and Content Type
- ref (subject category)
- art (subject category)
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