IEEE Consumer Electronics Magazine - September/October 2021 - 71

of efficiency and scalability. The article " A twotier
intrusion detection framework for embedded
systems " proposes a two-tier scalable
intrusion detection framework designed and
experimented in the real-time cloud platform.
We sincerely believe that this Special Section
will be good reading for cyber security researchers
worldwide. We express our sincere thanks to
the Editor-in-Chief for allowing us to organize
this Special Section. We are also thankful to all
the authors who made this section possible and
the reviewers for their thoughtful contributions.
Figure 1. IoE security threats and AI-based possible
solutions. [DoS: Denial-of-Service; SVM: Support
Vector Machine; NN: Neural Networks; ANN: Artificial
Neural Networks; RNN: Recurrent Neural Network].
i) randomized response mechanism and ii) local
adaptive differential privacy mechanism. Finally,
the proposed mechanism is validated through
experiments and results from the analysis.
Detection and mitigation of malicious attacks
are some of the key research issues in the domain
of IoE. Implementing access control is one of the
unique concepts that cannot be resolved through
conventional security solutions. The article " AIenabled
blockchain-based access control for malicious
attacks detection and mitigation in IoE " proposes
an AI-based blockchain solution to solve the
access control issue by detecting and mitigating
malicious attacks.
Vehicular crowdsensing is another emerging
component of the Internet of Vehicles (IoV),
where security and privacy during the data collection
are major open research-problems. The
article " Secure crowdsensing in 5G Internet of
Vehicles: When deep reinforcement learning
meets blockchain " proposes a deep reinforcement
learning-enabled blockchain-based solution
for user privacy and data security. The
effectiveness of the proposed solution is validated
through extensive experiments.
Host-based intrusion detection has been
widely applied to embedded systems for scalable
IoE. However, the current standalone intrusion
detection systems are not efficient in terms
&REFERENCES
1. S. P. Mohanty, V. P. Yanambaka, E. Kougianos, and D.
Puthal, " PUFchain: A hardware-assisted blockchain for
sustainable simultaneous device and data security in
the Internet of Everything (IoE), " IEEEConsum.
Electron. Mag., vol. 9, no. 2, pp. 8-16, Mar. 2020.
2. B. Qian et al., " Orchestrating the development
lifecycle of machine learning-based IoT applications:
A taxonomy and survey, " ACMComput. Surveys,
vol. 53, no. 4, pp. 1-47, 2020.
3. A. Singla, and E. Bertino, " How deep learning is making
information securitymore intelligent, " IEEESecur.
Privacy, vol. 17, no. 3, pp. 56-65, May/Jun. 2019.
4. D. Puthal, and S. P. Mohanty, " Cybersecurity issues in
AI, " IEEEConsum. Electron. Mag., to be published,
doi: 10.1109/MCE.2021.3066828.
5. S. M. Tahsien, H. Karimipour, and P. Spachos,
" Machine learning based solutions for security of
Internet of Things (IoT): A survey, " J. Netw. Comput.
Appl., vol. 161, 2020, Art. no. 102630.
Deepak Puthal is currently an Assistant Professor
at the School of Computing, Newcastle University,
Newcastle upon Tyne, U.K. Contact him at deepak.
puthal@newcastle.ac.uk.
Amit Kumar Mishra is currently a Professor with
the Department of Electrical Engineering, University
of Cape Town, South Africa. Contact him at
akmishra@ieee.org.
Suraj Sharma is currently an Assistant Professor
with the Department of Computer Science and Engineering,
IIIT (International Institute of Technology)
Bhubaneswar, Malipada, India. Contact him at
suraj@iiit-bh.ac.in.
September/October 2021
71
http://dx.doi.org/10.1109/MCE.2021.3066828
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