IEEE Systems, Man and Cybernetics Magazine - October 2022 - 52

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IEEE Systems, Man, and Cybernetics Magazine
Special Issue on Federated Learning for Cybersecurity Management in the era of AI
I AIM AND SCOPE
science
The explosion of IoT data has shown the cloud's
limitations. In many circumstances, storing vast
amounts of data in a central place is impracticable.
These concerns prompted the development of novel
Machine Learning (ML) approaches to aid in the
resolution of privacy and enormous data volume
difficulties. Experts have presented numerous methods to
address these concerns; Federated Learning (FL) is the
most prominent. FL is the new generation of Artificial
Intelligence (AI) based on decentralized data and training,
which puts learning to the edge or on the device. The main
objective for the foundation of FL in AI is a lack of
adequate data to be stored in a centralized server and data
security via the use of local information stored through
edge devices. FL makes it possible for areas with
heterogeneity and sensitive data to exploit the benefits of
artificial intelligence.
FL may be used to test
the potential
and train not
to make
a
just on
smartphones and tablets but on any edge layer device
that has
significant
contribution to the area of cybersecurity, regardless of
the platform. FL enables autonomous cars to learn on
decentralized driver behavior worldwide or hospitals
to improve diagnostics without jeopardizing patient
data confidentiality. FL mitigates cybersecurity risks by
relocating computing to the periphery rather
than
relying on centralized data to execute the learning
process. Cyberattacks are regularly occurring in a wide
variety of applications implemented in
environments, causing most manufacturers
hesitant to embrace the IoT.
FL encapsulates the concepts
of
targeted data
gathering and reduction and mitigates a significant
portion of the systemic privacy concerns and costs
associated with conventional, centralized ML and data
Digital Object Identifier 10.1109/MSMC.2022.3211385
real-world
to be
and
overcoming
ML
methodologies.
statistical
problems.
Emerging
technologies have focused on increasing FL security
and
Additional
initiatives exist to create FL, a broad paradigm for any
decentralized
collaborative ML approaches that
preserve anonymity. In the decade ahead, FL is
projected to break boundaries across sectors and create
a network where knowledge and data may be
exchanged safely, and rewards are given evenly based
on each participant's contribution. Hence, to build
more resilient infrastructures to manage security threats
and assaults, this new investigation, sometimes stated as a
new dawn in AI, requires a more comprehensive study into
its confirmation and
applications
management in the era of AI.
II TOPICS
We welcome
researchers to share
their
latest
research findings from both academia and industry,
including but not limited to the following:
* Advanced FL protocol built on the blockchain
technology
* Efficiently maintains and deploys deep learning
models
* Novel Anti-forensics-based deep learning model
using FL
* Data
protection using AI-based information
hiding in FL
* FL-trained SMS model for spam prediction
* Secure FL for EDGE
* Enhancing MLOps and automation for FL in
Cybersecurity Management
* AI-based authentication in FL in cybersecurity
management
* Advanced FL protocols for trust, security, and
privacy for cybersecurity management
* FL and AI-based quantum computing advance
cybersecurity management
for
cybersecurity
52 IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE October 2022
IEEE Systems, Man and Cybernetics Magazine - October 2022

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