| 1. |
- Aboelwafa, Mariam M. N., et al.
(författare)
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A Machine-Learning-Based Technique for False Data Injection Attacks Detection in Industrial IoT
- 2020
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Ingår i: IEEE Internet of Things Journal. - Piscataway : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4662. ; 7:9, s. 8462-8471
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 2. |
- Gebremichael, Teklay, 1985-, et al.
(författare)
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Security and Privacy in the Industrial Internet of Things : Current Standards and Future Challenges
- 2020
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Ingår i: IEEE Access. - Piscataway, N.J. : IEEE. - 2169-3536. ; 8, s. 152351-152366
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Tidskriftsartikel (refereegranskat)abstract
- The Internet of Things (IoT) is rapidly becoming an integral component of the industrial market in areas such as automation and analytics, giving rise to what is termed as the Industrial IoT (IIoT). The IIoT promises innovative business models in various industrial domains by providing ubiquitous connectivity, efficient data analytics tools, and better decision support systems for a better market competitiveness. However, IIoT deployments are vulnerable to a variety of security threats at various levels of the connectivity and communications infrastructure. The complex nature of the IIoT infrastructure means that availability, confidentiality and integrity are difficult to guarantee, leading to a potential distrust in the network operations and concerns of loss of critical infrastructure, compromised safety of network end-users and privacy breaches on sensitive information. This work attempts to look at the requirements currently specified for a secure IIoT ecosystem in industry standards, such as Industrial Internet Consortium (IIC) and OpenFog Consortium, and to what extent current IIoT connectivity protocols and platforms hold up to the standards with regard to security and privacy. The paper also discusses possible future research directions to enhance the security, privacy and safety of the IIoT.
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| 3. |
- Pankaczi, Lilla, 1991-, et al.
(författare)
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Enhancing the Security of ISO/IEC 14443-3 and 4 RFID Authentication Protocols through Formal Analysis
- 2023
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Ingår i: 2023 IEEE International Conference on Omni-Layer Intelligent Systems, COINS 2023. - : IEEE. - 9798350346473 - 9798350346480
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Konferensbidrag (refereegranskat)abstract
- Due to cyber attacks targeting RFID systems, this paper briefly summarizes parts 3 and 4 of the ISO/IEC 14443 standard, which specify the initialization, selection, and trans-mission protocols in high-frequency RFID smart-card and reader communication. The communication has been modeled, and two experiments have been performed using a security protocol ana-lyzer tool called Scyther. The protocol verification results shows that implementing Random UID can prevent many RFID attacks, such as eavesdropping and replay attacks and successfully protect the cardholder's privacy. © 2023 IEEE.
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| 4. |
- Tirumaladass, Virinchi, et al.
(författare)
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Deep learning-based Electromagnetic Side-Channel Analysis for the Investigation of IoT Devices
- 2020
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Ingår i: Proceedings of the 2nd International Conference on Inventive Research in Computing Applications, ICIRCA 2020. - Piscataway : IEEE. - 9781728153742 ; , s. 150-156
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Konferensbidrag (refereegranskat)abstract
- The boom of Internet of Things (IoT) devices has brought along new security concerns which earlier were not thought of. This has expanded the potential for digital forensic investigators to gather rich evidence from these sources. The data on these IoT devices is not easily accessible due to the lack of proper techniques to investigate such devices. This paper presents a sophisticated non-invasive method to investigate IoT devices. The software activities running on a device can be inspected by observing the electromagnetic radiation emitted from the devices during the process. The objective of this project is to evaluate if it is possible to classify the software activities being run on an IoT device by performing an electromagnetic side-channel analysis (EM-SCA). This paper presents a methodology for analyzing the EM side-channels and classifying encryption algorithms being run on a Raspberry Pi 4. This work demonstrates that the cryptographic encryptions can be classified with over 95% accuracy by using deep neural network-based classifiers. © 2020 IEEE.
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