Current Trends in Cyber Security CSS522
Group 2
Application of Deep Learning in relation to Cyber
Security
S/N Paper title
Impact
Contribution
Limitation
Future
impact/work
The paper
provides a
comprehensive
survey on the
application of
Deep Learning
(DL) in smart
grids and
It identifies the
challenges and
open issues in the
existing research
works, providing
insights for future
research
directions in the
The paper does
not provide a
comprehensive
analysis of the
scalability and
feasibility of DL
approaches in
real-world smart
The findings and
insights from this
paper can
contribute to the
advancement of DL
techniques in the
context of smart
grids, leading to
1a
1b
2a
2b
3a
3b
4a
4b
5a
Deep Learning
for intelligent
demand
response and
smart girds: A
comprehensiv
e survey
demand
response,
addressing the
challenges and
issues in the
transmission of
electricity
through the
traditional grid.
The paper also
highlights the
challenges
presented in
existing research
works and
identifies
important issues
and potential
directions in the
use of DL for
smart grids and
demand
response.[1]
5b
Blockchain for
deep learning:
Review and
open
challenges
Integrating
blockchain
technology with
deep learning
can address
these issues and
provide benefits
such as
operational
transparency,
traceability,
reliability,
security, and
trusted data
provenance.
Blockchain can
be used in
decision-making
systems, such as
deep
reinforcement
use of DL for
smart grids and
demand
response.[1]
The paper fills the
gap in the existing
surveys by
providing a
comprehensive
review specifically
on the use of DL
for demand
response in smart
grids, which was
previously
missing. [2]
Devised a
taxonomy to
categorize and
classify the
existing literature
related to
blockchain-based
deep learning
frameworks.
Presented
important
research
challenges that
need to be
addressed to
develop highly
efficient, robust,
and secure deep
learning
frameworks.[2]
grid
implementations
.[18]
It does not delve
into the
potential
drawbacks or
limitations of DL
algorithms in
terms of
computational
complexity,
training data
requirements,
and
interpretability
of results.[1]
The paper does
not provide a
comprehensive
evaluation or
comparison of
the existing
blockchainbased deep
learning
frameworks. It
only compares
them based on
four parameters,
which may not
cover all
relevant
aspects.[22]
The paper does
not provide
empirical
evidence or case
more efficient and
reliable demand
response systems,
improved energy
management, and
enhanced grid
stability.[1]
The comprehensive
survey presented in
the paper serves as
a valuable resource
for researchers and
practitioners in the
field, guiding them
in the development
and
implementation of
DL-based solutions
for smart grids and
demand response.
The paper provides
a comprehensive
review of the
existing literature
on the integration
of blockchain with
deep learning,
categorizing and
classifying it based
on various
parameters. This
work can serve as a
valuable resource
for researchers and
practitioners
interested in
exploring the
intersection of
blockchain and
deep learning.
learning and
swarm robotics,
to assist in
making decisions
based on data
collected on the
blockchain[2]
Sub-group summary
studies to
support the
claims made
about the
benefits and
impacts of
integrating
blockchain with
deep
learning.[23][4]
The paper also
highlights the
strengths and
weaknesses of
existing blockchainbased deep
learning
frameworks and
identifies important
research challenges
that need to be
addressed to
develop efficient,
robust, and secure
deep learning
frameworks. This
can guide future
research efforts in
addressing these
challenges and
improving the
integration of
blockchain and
deep learning.[2]