A_Novel_Standalone_Implementation_of_MDNN_Controller_for_DC-DC_Converter_Resilient_to_Sensor_Attacks-_A_Design_Approach.pdf

1
A Novel Standalone Implementation of MDNN
Controller for DC-DC Converter Resilient to Sensor
Attacks- A Design Approach
Venkata Siva Prasad Machina, Student Member, IEEE, Sriranga Suprabhath Koduru, Student
Member, IEEE, Sreedhar Madichetty Senior Member IEEE and, Sukumar Mishra Senior Member, IEEE
Abstract—Power electronic converters have become an integral
part of the direct current microgrid (DCMG) systems. The
efficient control of these converters will decide the performance of
the DC microgrids. With the evolution of cyber physical systems,
all these power converters are integrated into the communication
networks to achieve intelligent and smart control. Data in the
communication networks are highly vulnerable towards cyber-
attacks, leaving it unaddressed will lead to substantial economic
losses and disasters. This article proposes a standalone imple-
mentation of multi-deep neural network (MDNN) based DC-
DC converter and its application to detect false data injection
(FDI) attacks at the sensor level. A MDNN is developed with a
combination of deep neural network (DNN) and error detection
network (EDN). DNN is used as the controller to achieve closed
loop operation of DC-DC converter and EDN is used to detect and
mitigate the FDI attack. Initially, proposed scheme is executed
in MATLAB simulink platform with various disturbances at
controller level and several attack scenarios are verified with
its experimental results.
Index Terms—Cyber security, DC microgrids, Deep Neural
Networks, False data injection attacks
I. INTRODUCTION
Cyber physical systems (CPS) are considered as the in-
terconnection of control, sensors and embedded units
through the communication layer which are implemented to
obtain intelligent and smart control [1]. The most commonly
used real-time CPS in power sector is microgrid. Concept of
microgrid is introduced to integrate the renewable generation
sources, this process include many sensors and controllers that
communicates through the communication networks. As the
majority of the loads and sources are direct current (DC)
in nature, DCMG has gained more attention. DCMG has
more reliability and has lower losses compared to its coun-
terpart alternating current microgrids. The main components
of DCMG are the DC-DC converters which act as the nodes to
be controlled for maintaining constant DC bus voltage, which
is one of the regulatory measures of DCMG. The control of
these DC-DC converters in DCMG plays a important role in
bus voltage regulation and load regulation[2].
A. Motivation
The presence of communication network brings the high
risk of cyber-attacks on the system. Various malicious cyber-
attacks have been reported on the power sector till date
including false data injection (FDI) attack [3], denial of service
(DoS) [4] attacks, man in the middle (MITM) attack [5]
Fig. 1. General implementation of OSI model and possible cyber attack
locations
and replay attacks (RA) [6]. Fig. 1 shows the general open
systems interconnection (OSI) implementation and possibility
of various cyber-attacks in each layer. Among various cyber-
attacks, FDI attacks are the most predominant and notorious
attacks as they can be performed in both networking and
physical level.
In the operation of DCMG, DC-DC converters set points
will primarily depend upon the sensor values received from
the neighbouring converters. In order to destabilize the system,
the attacker tries to manipulate the sensor data passing to the
controller. The communication between the system and the
controller can be wired or wireless, in either of these cases,
the information passing through the communication link is
targeted. The attacker gains the unauthorized access to the
communication channel and tries to manipulate the sensor data
by injecting the false data. Adversaries typically orchestrate
an FDI attack by intruding on the physical security of control
devices, bypassing inefficient data detection mechanisms of
the system, and introducing measurement noises. One common
approach to effecting such attacks is to target vulnerabilities in
input validation and transport layer security for delivering false
data through techniques such as code injection and cross-site
request forgery. To overcome the FDI attacks on the sensors,
an MDNN based novel methodology has been proposed.
This article has been accepted for publication in IEEE Journal of Emerging and Selected Topics in Power Electronics. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/JESTPE.2023.3242299
© 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.See https://www.ieee.org/publications/rights/index.html for more information.
Authorized licensed use limited to: GMR Institute of Technology. Downloaded on March 15,2023 at 04:28:33 UTC from IEEE Xplore. Restrictions apply.

2
B. Literature Review
The control techniques have evolved with the advancements
in power converters, control algorithms and computational
resources. The traditional control techniques include voltage
mode control (single loop control) , current mode control
(double loop control) [7, 8], sliding mode control (SMC)
and proportional-integral-derivative (PID) control. SMC is the
nonlinear control technique proposed to deal with the non-
linearities present in the system [9, 10]. PID control is a control
mechanism which is very popular and largely used in indus-
tries for its robustness and simple nature of implementation.
Design of PID control involves the design of controller gains
which decides the effectiveness of the system performance
[11]. PID controller gain calculation includes the modelling of
the system transfer function which is a difficult task for large
and dynamic systems. Also, the control mechanisms are model
dependent, and their performance will be degraded during the
change in converter parameters. To address this issue rule-
based technique called the fuzzy logic controller and data-
driven mechanism like neural network controller are proposed
[12]. Fuzzy logic does not depend on converter parameters, but
it requires complete knowledge of the process of control which
helps in making the rules for the controller. Artificial neural
network (ANN) controller is a data-driven approach which is
independent of the model parameters, which makes it a simple
yet effective control approach [13]. In [14] authors compared
the different control algorithms and found the ANN controller
to give better performance, with negligible overshoots and
insensitive towards load and source variations. In [15] authors
have proposed a framework to detect FDI attacks by monitor-
ing the change in properties of microgrid which are referred to
as candidate invariants. In [16] authors proposed an observer-
based FDI attack detection and mitigation technique for multi-
area power systems, in which the attack signals are estimated
by the observer and compared with the system uncertainties to
detect the attack and the load frequency control mechanism is
automatically reconfigured to mitigate the attacks. An adaptive
sensor attack detection mechanism is proposed in [17], in
which the cumulative error sum between the sensed and
estimated values is compared with the predefined threshold
values. Authors of [18] have proposed an FDI attack mitigation
mechanism based on the error value generated between the ac-
tual sensor values and the observer estimated value. The above
mentioned observer based FDI attack mitigation techniques
involves the modelling of observer and controller gains which
are dependent on the system parameters and modelling of
system transfer function. Artificial intelligence is implemented
in cyber-attack mitigation to make it independent of the model
parameters. In [19] a machine learning (ML) approach is
followed to mitigate the FDI attack on transmission lines using
classification technique, normal sensor data and faulty data are
classified before reaching the controller node. In [20] authors
proposed the FDI attack mitigation mechanism with ANN and
PI controller in which the attack sensor data from the DC-DC
converter is compared with the predicted value from the neural
network and the reference value is adjusted accordingly in the
reference tracking layer before reaching the secondary layer
of the DCMG control.
In this article, it focuses on the capability of AI in the field
of power electronics and cyber security by implementing it
for control and security purpose. Therefore, a MDNN based
control and security mechanism is developed to mitigate the
FDI attack and achieve closed loop control of the DC-DC
converter.
C. Key Contributions
1) Design of MDNN controller for closed-loop control in
the DC-DC converter.
2) Development of secure and robust MDNN controller to
detect and mitigate the FDI attacks.
3) Standalone deployment of developed technology and its
implementation using a microcontroller.
4) Eliminating the need for proportional-integral (PI) and its
comparison with developed technology.
5) Developing a model-free methodology to make a system
reliable and robust from various FDI attacks, load and
source changes.
D. Organization
The rest of the paper is organised as follows. SECTION II
discusses about the MDNN methodology, deep neural network
design, hyperparameter selection and detailed analysis of the
algorithm. SECTION III presents the MATLAB simulation
and real-time implemented hardware results to support the
proposed algorithm and SECTION IV concludes the article.
II. PROPOSED METHODOLOGY
A. Multi Deep Neural Network Controller Design
Sensor attacks will manipulate sensor data either by inject-
ing faulty data or by erasing the existing data, which in turn
affects the system operation and its stability [21]. To overcome
the effect of sensor attacks on the system, MDNN based attack
mitigation scheme is proposed, and it is shown in Fig. 2. It
involves the design of two neural networks i.e, deep neural
network (DNN) and error detection network (EDN). This
neural network combination can achieve closed-loop control,
FDI attack detection and mitigation.
To verify the proposed method, a simple DC-DC buck
converter is considered as shown in Fig. 2, where Vin, Vo are
the input voltage of the converter and output voltage of the
converter respectively. The MDNN controller design includes
the modelling of two different neural networks. The first neural
network is DNN controller, having input voltage (Vin ) and
output voltage (Vo ) as the input features and the output is the
duty (Dn ) as given in (1).
Dn = f (Vin, Vo) (1)
When the attack is launched on the output voltage sensor,
the falsified output voltage (VOf
) can be written as (2)
Vof
= Vo + E (2)

3
Fig. 2. Detailed implementation of MDNN scheme on a DC-DC Converter
Where (E) is the error generated due to the attack. Thus
generated duty can be written as (3)
Dnf
= f Vin, Vof

(3)
The DNN output Dnf
is based on the activation function (AF)
as shown in Fig. 2. However the correct duty is not the same
as the obtained duty during the attack as shown in (4).
Dn ̸= Dnf
(4)
The second neural network termed as error detection network,
is proposed to detect and mitigate the effect of FDI attacks.
(Vin ) and VError are considered as inputs for EDN. VError is
calculated as the difference between reference voltage (Vref )
and the output voltage (Vo) as given in (5). Error duty (DEDN )
corresponding to VError and Vin is the output of the EDN as
given in (6).
VError = Vref − Vof
(5)
DEDN = f (Vin, VError) (6)
The duty obtained from the EDN is the error value that is
generated because of the FDI attack, thus obtained duty is
summed up with the duty generated by the DNN controller to
obtain the correct duty as shown in (7).
Dcorrect = Dnf
+ DEDN (7)
If there is no FDI attack then E=0. output sensor voltage will
be equal to the actual output voltage and can be written as (8)
and Dnf
= Dn. Further (5) can be written as (9).
Vof
= Vo (8)
VError = Vref − Vo (9)
During steady state condition, Vref = Vo and VError = 0. If
VError = 0, then output of EDN is 0 as given by (10). In this
scenario the correct duty can be written as (11).
DEDN = 0 (10)
Dcorrect = Dn (11)
FDI attacks can be detected by monitoring the value of the
error detection network output. The large deviation of output
voltage from the reference voltage increases the value of
VError. As we are already feeding the error into the neural
networks, a separate memory element is not required inside
the EDN to detect the deviation in the output. The value of
VError corresponds to the output voltage deviation because
of FDI attacks. Therefore to mitigate the effect of FDI attack
on the final duty, a compensation duty should be generated,
depending on the error given to the EDN, DEDN is generated.
Usually the attacker tries to inject the false data with the
intention of destabilizing the system, by selecting the limit
of DEDN above which the system is affected and the value
below the threshold can be considered as the safe operating
zone. Therefore, the small values of false data and system
disturbances which doesn’t cause any harm to the system
operation are not treated as FDI attacks.
B. Training and Testing of Proposed MDNN Technique
Traditionally, data sets are created with help of historical
data, but the disadvantage is that it does not contain all the
abnormalities that possibly occur in the system. To make the
controller design robust, the data sets should contain all the
abnormalities and uncertainties during the operation of the
system [22]. In this methodology, data sets are created by
simulating the converter in the MATLAB platform; although
it is computationally expensive, it ensures a mix of normal
and abnormal data.
1) Hyperparameter selection: Hyperparameter selection
plays an important role in the effective training of the neu-
ral network model. Activation function, weight update rule,
epochs, and network architecture parameter selection have
a major effect. Activation function is primarily used to in-
troduce the non-linearity in the training process so that the
trained model performs efficiently for non-linear systems.
Among many activation functions, sigmoid(As), tanh(At), and

4
ReLU(Ar) are the basic and actively used activation functions.
The sigmoid activation function is given in (12), where z is
the input of As. Its output ranges from 0 to 1.
As (z) =
1
1 + e−z
(12)
The tanh activation function is given in (13), where z is the
input of At. Its output ranges from −1 to 1.
At (z) =
2
1 + e−2z
− 1 (13)
The activation function is given in (14), where z is the input
of Ar. Its output ranges from 0 to z.
Ar (z) =

0 for z 0
z for z ≥ 0
(14)
Weight updation policy is used to reduce the error and
increase the efficiency of the training process. When all the
training samples are fed to the model, the obtained out-
put is compared with the actual output to find the error.
The obtained error is minimized by updating the weights
in every layer. Optimizers are used as the weight updation
tools among which, stochastic gradient descent (SGD), root
mean squared propogation (RMSprop) and adaptive moment
estimation (Adam) are the weight updation rules that are often
used. SGD optimizers differs from traditional gradient descent
in loading training samples for weight updation. A detailed
explanation of SGD with mathematical analysis is provided in
further sections. RMS prop and Adam optimizers are proposed
to deal with complex data and very deep neural networks.
RMSprop and adam facilitates the smooth convergence with
less time; whereas SGD is computationally inexpensive. De-
pending upon data complexity, trade-off should be performed
between convergence time and computational burden. The per-
fect combination of hyperparameters that performs efficiently
for the dataset are chosen by evaluating the loss function,
which in this case is root mean square error (RMSE) tech-
nqiue. Feeding training data samples to the proposed network
and updating weights is considered as one epoch, multiple
epochs are performed to minimize the loss. The RMSE scores
of each combination for 50,100,150 epochs are shown in table
1.
It is observed that parameter combinations for 100 epochs
are giving satisfying results with low RMSE values. Table 1
shows the RMSE scores for hyperparameter combinations.
TABLE I
HYPERPARAMETER SELECTION
Epochs Optimizer Activation function
50
Sigmoid ReLU Tanh
Adam 0.01225 0.00052 0.00321
RMSprop 0.03446 0.00549 0.00634
SGD 0.13599 0.00081 0.00892
100
Adam 0.00807 0.00044 0.00085
RMSprop 0.02002 0.00292 0.00275
SGD 0.09289 0.00028 0.00427
150
Adam 0.00401 0.00157 0.00188
RMSprop 0.00545 0.00329 0.00412
SGD 0.02422 0.00050 0.00302
From table 1, it can be concluded that the combination
of SGD and ReLU gives the best RMSE value of 0.00028.
The model specifications considered for DNN and EDN are
tabulated in table 2.
TABLE II
NN MODEL SPECIFICATIONS
Parameters DNN EDN
Weight update rule SGD SGD
Performance metrics RMSE RMSE
Performance goal 0.03% 0.03%
Epochs 100 100
Activation function ReLU ReLU
No.of input layer nodes 2 2
No.of hidden layer 1 nodes 10 10
No.of hidden layer 2 nodes 10 10
No.of output layer nodes 1 1
The detailed procedure for training DNN and EDN models
are represented using a flowchart as shown in Fig. 3. The
Fig. 3. Flowchart for designing DNN and EDN controllers
collected data set has various input/output features which are
in different ranges, where normalization plays a vital role.
Therefore, it normalizes the complete data set has all the
variables to a common scale in order to maintain uniformity.
The scaled input value is given in (15).
Scaled V alue =
Vox − Vox|min
Vox|max − Vox|min
(15)
Where ,
Vox
is current value of input sample of a specific feature x.
Vox|min is minimum value of feature x.
Vox|max is maximum value of feature x.
The scaled data is split into three different partitions of
which 70% is for training, 15% is for validation and 15%
is for testing.
C. Algorithm
A generalized DNN model with 2 input layer nodes, 4 nodes
for the first hidden layer, 4 nodes for the second hidden layer
and 1 output layer node is shown in Fig. 4.

5
Fig. 4. A generalized DNN model with 2 input, 4 hidden-1, 4 hidden-2 and
1 output nodes
The working of the feed-forward backpropagation algorithm
is given in algorithm 1. As per the algorithm, it is clear that
total P training samples would be considered with α as the
learning rate. Here, In denotes the input layer nodes, H1n
denotes the first hidden layer nodes, H2n denotes the second
hidden layer nodes, On denotes the output layer nodes, Di are
the bias units, m1 represents the weight parameters between
In and H1n, m2 represents the weight parameters between
H1n and H2n, m3 represents the weight parameters between
H2n and On.
At the initial conditions, the learning parameters mn, Di
and α are randomly generated thus, generated data samples are
feed forwarded to the network and output will be predicted as
c
Oi. The proposed model is evaluated by using a performance
metric RMSE (16).
RMSE =
v
u
u
t 1
P
P
X
i=1

Oi − c
Oi
2
(16)
To minimize the RMSE between the predicted value and actual
value Oi, an optimized cost function is implemented as shown
in (17).
Cost (J) =
1
2P
P
X
i=1
c
Oi − Oi
2
(17)
Here Ôi can be rewritten as shown in (18)
Ôi = mT
Ai
+ Di (18)
Substituting (10) in (9), the J can be obtained as (19)
J =
1
2P
P
X
i=1
mT
Ai
+ Di

− Oi 2
(19)
The learning parameters are tuned based on stochastic gradient
descent (SGD) method as shown in algorithm 1. Time taken for
Algorithm 1: Back propagation algorithm for feed-
forward networks
Data: A set of training examples P, learning rate α
Result: Optimize weights and bias units
Initialize weights randomly;
Creating a feed-forward network with In input
nodes,H1n, H2n for two hidden layer nodes and On
output nodes;
while repeat till convergence do
while performing feed forward neural network in a
generalised way do
S1 = m1 ∗ In + D1;
H1n = f (S1);
S2 = m2 ∗ H1n + D2;
H2n = f (S2);
S3 = m3 ∗ H2n + D3;
On = f (S3) = c
Oi;
end
while Compute cost function by mean squared
error do
Cost (J) = 1
2P
PP
i=1
c
Oi − Oi
2
;
end
while update weights and biases using stochastic
gradient descent in generalised manner do
m ← m − α
|ß|
P
iϵß H(i) mT
Hi + Di − Oi

;
D ← Di − α
|ß|
P
iϵß mT
Hi + Di − Oi

;
end
end
one weight update is much less in SGD compared to normal
gradient descent (GD) as it considers only a sample portion
of training data ß from training state space [23].
In general, every node of either hidden layer or output layer
is linear sum (S) of nonlinear activation function. The weights
are updated by using back propagation algorithm that follows
chain rule. The detailed work flow is given in (20)-(22)
m3 = m3 − α

∂On
∂m3
∗
∂J
∂On

(20)
m2 = m2 − α

∂H2n
∂m2
∗
∂On
∂H2n
∗
∂J
∂On

(21)
m1 = m1 − α

∂H1n
∂m1
∗
∂H2n
∂H1n
∗
∂On
∂H2n
∗
∂J
∂On

(22)
The generalised weight update for input and hidden layer
nodes is given in (23).
m ← m −
α
ß
X
i∈ß
Hi mT
· Hi + Di − Oi

(23)
The generalised bias weight update is given in (24)
m ← m −
α
ß
X
i∈ß
mT
· Hi + Di − Oi

(24)
After several epochs, the model parameters are trained and
the trained model is then tested by using testing data-set and
validation data-set.

6
III. RESULTS
The proposed methodology is evaluated in MATLAB
simulink platform and on real time hardware setup . Various
test cases like reference change, source change, load change
and FDIA are implemented and their performance is compared
with the PI controller. Table 3 lists the case studies considered
for both simulation and real time.
A. Simulation results
Buck converter with MDNN controller and PI controller are
simulated in MATLAB simulink with converter specifications
are shown in Table 4. Various case studies are implemented
on both the controllers to test their performance and accuracy.
1) Case1 : Source change and load change with MDNN
and PI comparison
Input voltage for the buck converter is varied from 30 V to
50 V at 0.3 s and load is varied from 0.78 A to 0.6 A at 0.7
s. During both source change and load change scenarios, the
output voltage is maintained constant at 20 V as shown in Fig.
5. From Fig. 5 it is also observed that the transient response of
MDNN controller is better compared to PI controller. Settling
time of PI controller for source change is 50 ms and for
MDNN controller is around 5 ms. Settling time during load
change for PI controller is around 15 ms whereas for MDNN
controller it is less than 5 ms.
Fig. 5. Comparison of PI and MDNN for load and source changes in
MATLAB simulation
2) Case2: FDI attack on output voltage sensor in MDNN
and PI controllers
The ability of the controller to mitigate the FDI attack is
evaluated in this case, the proposed MDNN configuration is
tested under FDI attacks. A false additive data of 10 V is
injected at the sensor level using IPV6 protocol to the micro
controller at 0.3 s and removed from the system at 0.7 s.
The effectiveness of proposed method is compared against the
PI controller in MATLAB simulation. Simulation results are
shown in Fig. 6. With the PI controller, it observes the huge
deviation of 4 V , whereas with MDNN approach, there is no
deviation from reference value. Therefore the proposed method
works effectively even under FDIA scenarios.
3) Case3: Response of DNN controller and overall MDNN
controller for FDI attack on output voltage sensor
The performance of proposed MDNN technique is verified
by checking its generated duty. When a false data of 15 V
Fig. 6. Comparison of PI and MDNN for FDI attack in MATLAB simulaton
is injected at the output voltage sensor as shown in Fig. 7(a),
the duty response of DNN and MDNN controller are shown in
Fig. 7(b) and, Fig. 7(c) respectively. Under all these conditions,
the duty of MDNN is not deviated and its output voltage is
maintained constantly as shown in Fig. 7(d). Therefore it can
be concluded that the proposed method is working effectively.
Fig. 7. a) Output voltage sensor waveform, b) Response of DNN controller,
c) Response of MDNN controller and d) Output voltage waveform
4) Case4: Proposed MDNN technique for boost converter
To see the applicability of the proposed scheme to all con-
verters, the proposed MDNN technique is evaluated for boost
converter. The synthetic dataset is prepared and trained on the
DNN and EDN networks. Fig. 8 shows the output voltage
waveform of the boost converter during source change, load
change, FDI attack and reference changes. Source change is
performed on the boost converter at 2 s by increasing input
voltage from 20 V to 40 V , it is observed that there is no
effect on the output voltage and its constant at a reference
value of 50 V . Load change is performed at 4 s by reducing
the load from 2 A to 1 A and it maintains the constant output
voltage. The reference volatge value is changed from 50 V to
56 V at 8 s, from Fig. 8 it is observed that the settling time
of the boost converter is 0.005s and settles at 56 V without
any steady-state error. The proposed scheme is also evaluated
for FDI attacks as well; 5 V of false data is injected near the
output voltage sensor at 6 s and it can be concluded from
Fig. 8 that FDI attack has no effect on the MDNN-controlled
boost converter. From the above results, it is proved that the
proposed scheme is working efficiently for the boost converter
and can be applicable to any other converter.

7
TABLE III
CASE STUDIES FOR MATLAB SIMULATION AND HARDWARE IMPLEMENTATION
S.No Case study Implementation
1 Source change and load change with MDNN and PI comparison MATLAB simulink
2 FDI attack on output voltage sensor in MDNN and PI controllers MATLAB simulink
3 Response of DNN controller and overall MDNN controller for FDI attack on output voltage sensor MATLAB simulink
4 Proposed MDNN method for boost converter MATLAB simulink
5 Source voltage variations Real-time hardware
6 Load change Real-time hardware
7 Simultaneous source change and load change Real-time hardware
8 Reference voltage change Real-time hardware
9 Transient response of MDNN controller Real-time hardware
10 FDI attack on output voltage sensor Real-time hardware
11 Simultaneous load change and FDI attack Real-time hardware
12 Simultaneous source change and FDI attack Real-time hardware
13 Simultaneous source change, load change and FDI attack Real-time hardware
14 Time varying FDI attack Real-time hardware
15 Disturbance and attack differentiation Real-time hardware
TABLE IV
BUCK CONVERTER COMPONENT RATINGS
Component Rating
Inductor 150µH
Capacitor 2200µF
Input voltage range 30V − 50V
Output voltage 20V
Fig. 8. Output voltage wave-form of boost converter with MDNN technique
B. Hardware results
The proposed technique is validated by conducting experi-
ments on real time hardware setup as shown in Fig. 9
Fig. 9. Experimental hardware setup with DC voltage source, DC-DC
converter, micro-controller and loads
1) Case5: Source voltage variations
Initially, system has been tested for a fixed input and its duty is
generated using proposed MDNN method. The generated duty
and output voltage waveform are shown in Fig. 10. Further, it
provided an input voltage of 30 V till 1.5 s, at 1.5 s input is
suddenly raised to 50 V . The voltage is maintained till 6.2 s
and it’s changed again to 30 V and maintained. Under all the
conditions, the output voltage is maintained at 20 V as per the
given reference value and its results are shown in Fig. 11.
Fig. 10. MDNN Duty along with the output voltage waveform
Fig. 11. Input voltage and output voltage wave-forms under source voltage
variations
2) Case6: Load variations
To validate the proposed technique during dynamic loading

8
conditions, load change scenario is implemented on the real-
time hardware setup by varying the load. A load of 0.75 A
is maintained till 3.2 s and suddenly reduced to 0.5 A and
maintained till 4.8 s. Again the load is changed to 0.41 A and
maintained till 6.5 s and changed to 0.6 A. Under all load
changing conditions, the output voltage is maintained at 20 V
and its results are shown in Fig. 12.
Fig. 12. Output current and output voltage wave-forms under variable load
conditions
3) Case7: Simultaneous source change and load change
The performance of the proposed method during the simul-
taneous load change and source change scenarios is evaluated.
Initially, input voltage is maintained at 30 V and suddenly
raised to 50 V at 4.2 s;load is initially at 0.78 A and suddenly
reduced to 0.6 A at 4.2 s. Even though there is a significant
change in both source and load at the same time, MDNN
controller effectively maintains the output voltage at 20 V as
shown in Fig. 13
Fig. 13. Output voltage waveform for simultaneous load and source changes
4) Case8: Reference voltage change
Output voltage of the buck converter should follow the
reference voltage given by the user. In all the above cases, the
MDNN controller response for constant reference voltage is
observed. In this case reference voltage of the buck converter
is varied and the response of MDNN controller is verified.
Output voltage waveform for the reference voltage changes is
shown in Fig. 14. Initially, the reference voltage is maintained
at 20 V , and then increased to 25 V . From 25 V it is reduced
to 15 V and then again increased to 30 V . Finally it is reduced
to maintain at 20V . It is observed from the Fig. 14 that the
all the reference changes are reflected in the output voltage
waveform and the changes occurred without any delay.
Fig. 14. Output voltage wave-form for reference voltage variations
5) Case9: Transient response of MDNN controller
The controller efficiency is measured by analyzing the
transient response. When the reference voltage is changed,
the time taken by the controller to reflect the reference
voltage at the output voltage of the buck converter denotes the
adaptiveness of the control algorithm. Fig. 15 shows the output
voltage waveform of the buck converter when the reference
value is varied from 20 V to 28 V . It is observed that the
controller settling time for the reference voltage change is 10
ms. Peak overshoot of 15% is obtained. Similarly, the settling
time of the output voltage for the reference change of 28V to
20 V is shown in Fig. 16 , settling time obtained is 10 ms.
Fig. 15. Transient response of output voltage with increase in reference
voltage
Fig. 16. Transient response of output voltage with decrease in reference
voltage
6) Case10: FDI attack on output voltage sensor
FDI attack is performed near the output voltage sensor
and the ability of MDNN controller to mitigate the attack
is analyzed. Fig. 17(a) denotes the output voltage sensor
waveform, we can observe that the FDI attack of 5 V is
performed on the sensor at 10 s by changing value from 20 V
to 25 V . At 47 s another FDI attack of 5 V is performed to
make the sensor value 30 V . Later the FDI attack is performed

9
by injecting the value of -5 V at 65 s and 82 s and changing
the sensor value back to original value of 20V . During multiple
FDI scenarios it is observed from the Fig. 17(b) that the output
voltage remained constant at desired value of 20 V .
Fig. 17. Output voltage sensor data and output voltage wave-form during
FDI attack
7) Case11: Simultaneous load change and FDI attack
In this case, simultaneous load change and FDI attack is
performed and the output voltage is monitored. Load change
of 0.5 A and FDI attack of 10 V is performed at 120 s as
shown in Fig. 18(a)(b). From Fig. 18(c) it can be observed that
during disturbance and attack scenarios, the response of the
controller is very efficient, as it maintains reference voltage of
20V .
Fig. 18. Output voltage wave-form during simultaneous load change and FDI
attack
8) Case12: Simultaneous source change and FDI attack
FDI attack along with source change is performed simul-
taneously and the response of the controller is analysed.
Disturbance of 10 V is performed on input voltage by reducing
from 50 V to 40 V and FDI attack of 20 V is launched on
output voltage sensor at 6.5 s as shown in Fig. 19(a)(b). During
these disturbance conditions, the output voltage is maintained
constant at reference value of 20 V as shown in Fig. 19(c).
9) Case13: Simultaneous source change, load change and
FDI attack
In this case all the possible disturbances are considered
to test the controller performance. FDI attack of 10 V is
performed on sensor by falsifying sensor value from 20 V
to 30 V , load is changed from 0.5 A to 1.3 A and source is
varied from 50 V to 40 V at 7 s as shown in Fig. 20(a)(b)(c)
Fig. 19. Output voltage wave-form during simultaneous source change and
FDI attack
respectively. In-spite of system disturbances and FDI attack at
same instance, the converter is able to maintain the constant
reference voltage of 20 V as shown in Fig. 20(d).
Fig. 20. Output voltage wave-form during simultaneous source change, load
change and FDI attack
10) Case14: Time varying FDI attack
In all the above cases a constant FDI attack is considered, to
test robustness of the proposed methodology time varying FDI
attack is considered. Time varying FDI removes the chance of
any predictability in FDI attack. The time varying FDI attack
is given in (25). During initial 1 s, there is no FDI attack and
the output voltage of 20 V is observed, from 1 s time varying
FDI attack is implemented as shown in Fig. 21(a). The output
voltage of the system is maintained at 20 V during the time
varying FDI attacks.
VF DI = 5 (sin(2πt)) + 10 (25)
11) Case15: Disturbance and attack differentiation
To distinguish between the FDI attack and system disturbance
like load changes, load current wave-form and output volt-
age sensor wave-form are monitored. From Fig. 22(a) it is
observed that FDI attack of +10 V at 32 sand -10 V at
55 s is performed on the sensor. Load is changed from 0.5
A to 1.25 A at 68 s and again reduced to 0.5 A at 83 s
as shown in Fig. 22(b). It is observed that the load current
wave-form is unchanged during FDI and it is changing during
actual load change. By monitoring the load current wave-form
FDI attacks and system disturbances can be distinguished.

10
Fig. 21. Output voltage wave-form for time varying FDI attack
Fig. 22(c) represents the output voltage waveform during FDI
attacks and load changes.
Fig. 22. Difference between FDI attack and load change
C. Hardware deployment comparison
Standalone implementation of the proposed MDNN tech-
nique is performed by deploying the algorithm into micro-
controller. Initially the data set was prepared under normal
working conditions and false data injection conditions for a
realistic DC-DC converter in MATLAB. Thus obtained data
is saved in comma-separated values (CSV format) and then
pre-processed to remove any repeated data. Processed data is
trained using the Neural network toolbox which is available
in MATLAB 2022a. The trained simulation control algorithm
is deployed into an ATMEGA2560 microcontroller with the
supported hardware package available in MATLAB, which
makes the controller to work in standalone mode. It is observed
that the execution time of the proposed algorithm is 0.6
ms and the algorithm is occupying 21 KB memory. The
same process is repeated for PI controller making the system
standalone and it is found that the average execution time is
0.1 ms and it occupies the memory of 23 KB. Although the
execution time of MDNN algorithm is increased compared to
PI controller, ability of the MDNN to mitigate the FDI attacks,
resiliency towards parameter changes and system dynamics,
makes it superior to traditional PI controllers.
IV. CONCLUSION
The proposed methodology achieves the control of DC-
DC converter and mitigation of FDI attacks solely using
the deep neural network technique. In this article initially
MDNN technique is implemented on buck converter and
various cases are has been implemented. Later to generalise
the proposed MDNN scheme to various DC-DC converters,
the boost converter is simulated and evaluated. The results of
MDNN performance on boost converter and buck converter
show that the proposed scheme can be implemented for various
DC-DC converters. MDNN technique is compared with the
existing traditional control and mitigation techniques and it
was observed that the proposed methodology yields better
results and improved performance of the converter in terms
of voltage deviation correction during faulty scenarios. Real-
time deployment of the control algorithm is performed on a
microcontroller; both MDNN and PI controller algorithms are
deployed individually and it was found that the MDNN con-
troller occupies less memory and is more robust at mitigating
FDI attacks. This methodology can be incorporated into DC
microgrid scenarios and can be made more reliable and robust
with the help of reinforcement learning.
V. ACKNOWLEDGEMENT
This work is supported by Science and Engineering
Research Board (SERB) under start-up research grant
SRG/2020/000269 sponsored to Dr. Sreedhar Madichetty
REFERENCES
[1] N. Jazdi. Cyber physical systems in the context of
industry 4.0. In 2014 IEEE International Conference on
Automation, Quality and Testing, Robotics, pages 1–4,
2014.
[2] Ires Iskender and Naci Genc. Power electronic converters
in dc microgrid. In Microgrid Architectures, Control and
Protection Methods, pages 115–137. Springer, 2020.
[3] Qi Wang, Wei Tai, Tang Yi, and Ming Ni. A review of
the false data injection attack against the cyber physical
power system. IET Cyber-Physical Systems: Theory
Applications, 4, 06 2019.
[4] Yubin Shen, Minrui Fei, and Dajun Du. Cyber security
study for power systems under denial of service attacks.
Transactions of the Institute of Measurement and Con-
trol, 41(6):1600–1614, 2019.
[5] Patrick Wlazlo, Abhijeet Sahu, Zeyu Mao, Hao Huang,
Ana Goulart, Katherine Davis, and Saman Zonouz. Man-
in-the-middle attacks and defence in a power system
cyber-physical testbed. IET Cyber-Physical Systems:
Theory Applications, 6(3):164–177, 2021.
[6] Yilin Mo and Bruno Sinopoli. Secure control against
replay attacks. In 2009 47th annual Allerton conference
on communication, control, and computing (Allerton),
pages 911–918. IEEE, 2009.
[7] Seddik Bacha, Iulian Munteanu, Antoneta Iuliana Bratcu,
et al. Power electronic converters modeling and control.
Advanced textbooks in control and signal processing,
454(454), 2014.
[8] Lloyd Dixon. Average current mode control of switching
power supplies. In Unitrode Power Supply Design

11
Seminar Handbook, pages 5–1. Unitrode Corporation
Merrimack, NH, USA, 1990.
[9] Yue Zhao, Wei Qiao, and Daihyun Ha. A sliding-
mode duty-ratio controller for dc/dc buck converters with
constant power loads. IEEE Transactions on industry
Applications, 50(2):1448–1458, 2013.
[10] Vadim Utkin. Sliding mode control of dc/dc convert-
ers. Journal of the Franklin Institute, 350(8):2146–2165,
2013.
[11] Hang Wu, Weihua Su, and Zhiguo Liu. Pid controllers:
Design and tuning methods. In 2014 9th IEEE Con-
ference on industrial electronics and applications, pages
808–813. IEEE, 2014.
[12] K. Bendaoud, S. Krit, M. Kabrane, H. Ouadani,
M. Elaskri, K. Karimi, H. Elbousty, and L. Elmaimouni.
Fuzzy logic controller (flc): Application to control dc-
dc buck converter. In 2017 International Conference on
Engineering MIS (ICEMIS), pages 1–5, 2017.
[13] Weizhen Dong, Shuhui Li, Xingang Fu, Zhongwen Li,
Michael Fairbank, and Yixiang Gao. Control of a buck
dc/dc converter using approximate dynamic program-
ming and artificial neural networks. IEEE Transactions
on Circuits and Systems I: Regular Papers, 68(4):1760–
1768, 2021.
[14] Nishtha Bajoria, Pragya Sahu, RK Nema, and Savita
Nema. Overview of different control schemes used for
controlling of dc-dc converters. In 2016 International
Conference on Electrical Power and Energy Systems
(ICEPES), pages 75–82. IEEE, 2016.
[15] Omar Ali Beg, Taylor T. Johnson, and Ali Davoudi. De-
tection of false-data injection attacks in cyber-physical dc
microgrids. IEEE Transactions on Industrial Informatics,
13(5):2693–2703, 2017.
[16] Kaishun Xiahou, Yang Liu, and QH Wu. Decentralized
detection and mitigation of multiple false data injection
attacks in multiarea power systems. IEEE Journal of
Emerging and Selected Topics in Industrial Electronics,
3(1):101–112, 2021.
[17] Francis Akowuah and Fanxin Kong. Real-time adaptive
sensor attack detection in autonomous cyber-physical
systems. In 2021 IEEE 27th Real-Time and Embedded
Technology and Applications Symposium (RTAS), pages
237–250. IEEE, 2021.
[18] Sreedhar Madichetty and Sukumar Mishra. Cyber attack
detection and correction mechanisms in a distributed dc
microgrid. IEEE Transactions on Power Electronics,
37(2):1476–1485, 2022.
[19] Peng Xun, Peidong Zhu, Zhenyu Zhang, Pengshuai Cui,
and Yinqiao Xiong. Detectors on edge nodes against
false data injection on transmission lines of smart grid.
Electronics, 7(6):89, 2018.
[20] Mohammad Reza Habibi, Hamid Reza Baghaee, Tomis-
lav Dragičević, and Frede Blaabjerg. False data injec-
tion cyber-attacks mitigation in parallel dc/dc converters
based on artificial neural networks. IEEE Transactions
on Circuits and Systems II: Express Briefs, 68(2):717–
721, 2020.
[21] Ahmed Khoumsi. Sensor and actuator attacks of cyber-
physical systems: A study based on supervisory control
of discrete event systems. In 2019 8th International
Conference on Systems and Control (ICSC), pages 176–
182. IEEE, 2019.
[22] Andreas Venzke, Daniel K Molzahn, and Spyros Chatzi-
vasileiadis. Efficient creation of datasets for data-driven
power system applications. Electric Power Systems
Research, 190:106614, 2021.
[23] Nikhil Ketkar. Stochastic gradient descent. In Deep
learning with Python, pages 113–132. Springer, 2017.

A_Novel_Standalone_Implementation_of_MDNN_Controller_for_DC-DC_Converter_Resilient_to_Sensor_Attacks-_A_Design_Approach.pdf

Recommended

Recommended

More Related Content

Similar to A_Novel_Standalone_Implementation_of_MDNN_Controller_for_DC-DC_Converter_Resilient_to_Sensor_Attacks-_A_Design_Approach.pdf

Similar to A_Novel_Standalone_Implementation_of_MDNN_Controller_for_DC-DC_Converter_Resilient_to_Sensor_Attacks-_A_Design_Approach.pdf (20)

Recently uploaded

Recently uploaded (20)

A_Novel_Standalone_Implementation_of_MDNN_Controller_for_DC-DC_Converter_Resilient_to_Sensor_Attacks-_A_Design_Approach.pdf