Application of Machine Learning for Ransomware Detection in IoT Devices

Abstract

Internet users have been faced with a lot of threats with the growth of malware around the world. Ransomware, one of the significant types of malware, encrypts sensitive information and will not release the files until the user paid a ransom. Internet of Things (IoT) structure is an extensive area of Internet-associated instruments with additional computational capabilities with storage capacities that have the potential to be harmed by ransomware developers. The IoT has been speedily becoming more intense over the earlier years, and meanwhile, in these phases, one of the weakest areas is security. Security affairs had continued to be a crucial worry for businesses, despite the efficiency and prevalence of IoT growth. Therefore, the paper presents a machine learning model to detect ransomware attacks on IoT devices. The study used Power—to track and review the power consumption in 500 ms internals of all operating processes. The paper used three devices to perform the experiments, namely: Android device, Laptop computer, and Projector. The model was used to the monitoring power consumption of IoT devices using various procedures to categorize Ransomware outside non-malicious operations. Comparing the proposed system with the existing model outperforms concerning correctness ratio, precision ratio, recall ratio, and F-measure were considered.

Full text

Application of Machine Learning
for Ransomware Detection in IoT Devices
Roseline Oluwaseun Ogundokun , Joseph Bamidele Awotunde ,
Sanjay Misra, Oluwakemi Christiana Abikoye, and Oluwafemi Folarin
Abstract Internet users have been faced with a lot of threats with the growth of
malware around the world. Ransomware, one of the significant types of malware,
encrypts sensitive information and will not release the files until the user paid a
ransom. Internet of Things (IoT) structure is an extensive area of Internet-associated
instruments with additional computational capabilities with storage capacities that
have the potential to be harmed by ransomware developers. The IoT has been speedily
becoming more intense over the earlier years, and meanwhile, in these phases, one of
the weakest areas is security. Security affairs had continued to be a crucial worry for
businesses, despite the efficiency and prevalence of IoT growth. Therefore, the paper
presents a machine learning model to detect ransomware attacks on IoT devices. The
study used Power—to track and review the power consumption in 500 ms internals
of all operating processes. The paper used three devices to perform the experiments,
namely: Android device, Laptop computer, and Projector. The model was used to the
monitoring power consumption of IoT devices using various procedures to catego-
rize Ransomware outside non-malicious operations. Comparing the proposed system
with the existing model outperforms concerning correctness ratio, precision ratio,
recall ratio, and F-measure were considered.
Keywords Internet of things ·Ransomware ·Machine learning ·Malware ·
Encryption
R. O. Ogundokun (B)
Department of Computer Science, Landmark University, Omu Aran, Kwara, Nigeria
e-mail: Ogundokun.roseline@lmu.edu.ng
J. B. Awotunde ·O. C. Abikoye
Department of Computer Science, University of Ilorin, Ilorin, Nigeria
S. Misra
Department of Electrical and Information Engineering, Covenant University, Ota, Nigeria
O. Folarin
Center for System and Information Services, Landmark University, Omu Aran, Kwara, Nigeria
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
S. Misra and A. Kumar Tyagi (eds.), Artificial Intelligence for Cyber Security: Methods,
Issues and Possible Horizons or Opportunities, Studies in Computational Intelligence
972, https://doi.org/10.1007/978-3-030-72236-4_16
393

394 R. O. Ogundokun et al.
1 Introduction
In recent centuries the Internet of Things was emerged into mainstream public view
[1,2]. The IoT is a broad collection of gadgets consisting of sensors or actuators linked
via wired or wireless structures [3,4]. Over the last decade, IoT has developed rapidly,
and protection has been described as one of the weakest areas of IoT during develop-
ment [5,6]. There are currently more than six billion Internet-connected devices and a
projected 25 billion associated by 2020 [7,8]. The Internet of Things (IoT) denotes the
interconnected system of connections of intellectual instruments, radars, entrenched
processes how heterogeneous data is stored, processed and communicated [9–11]. In
the meantime, IoT is growing inefficiency and prevalence; security concerns remain
a critical concern for industries [12,13]. Internet-associated gadgets, in addition
to those installed in an IoT structure, are progressively directed by cyber-felonious
appropriated to their ubiquity [12,13] together with the capability to employ the
negotiated gadgets to additionally bout the fundamental structure [13,14].
In recent years, the prevalence of malware has increased dramatically.
Ransomware has grown into one of the most prominent strains of cybercrime [15,
16]. We see more Ransomware cases in 2017 than we have ever seen before due
to its ability to autonomously propagate across the network [12,13,17,18]. The
malevolent scheme, including adventure equipment, has constantly been regarded
as a significant device in virtual felonious [19,20]. Ransomware has been involved
into being a significant universal risk in the past two years with the FBI approxi-
mating that $1 Billion of ransoms requests were remunerated in 2016 [20–23]; thus
denotes 400% intensified from the preceding time [24–26]. During the same time-
frame, the U.S. witnessed a rise of 300% in the number of regular ransomware attacks
[27,28] and doubled the cost of the average demand for ransom [26,29,30]. For
example, in the condition of Ransomware, gadgets which can store a fair volume of
statistics (e.g., Android and iOS devices) are likely to be targeted [12,13,18,31].
Securing IoT knots in contrast to risks which includes Ransomware is, therefore, a
subject of continued concern [32–38]. While research into ransomware discovery and
alleviation is presently fresh, ransomware discovery and alleviation stay disputed.
Ransomware is, by comparison, a novel form of ad injector which makes an effort to
scramble data from a negotiated computer employing a powerful encoding procedure
[12,13,39,40]. The target will, at that moment, have to reimburse the hacker to get
the password or decryption key [21,41] (usually using bitcoins).
Botnet discovery and mitigation for IoT nodes have been described as solitary of
many investigation problems, including prospects, and still currently an investigation
subject [42–45]. Detection approaches include malware-based characteristics (for
instance, code autographs) and pursuit of malevolent behavior together with its vigor
devouring [46–49]. Several machine learning methods and systems for ransomware
detection have been suggested and implemented [20,22,50]. Dynamic computa-
tional methods, however, have drawbacks in that novel types of Ransomware can be
revamped to overtake the detection frequency through machine learning procedures
[20,51,52].

Application of Machine Learning for Ransomware Detection … 395
Hence, the paper uses machine learning techniques to perceive Ransomware on
IoT nodes based on their energy-use patterns. The proposed model grinds classify
and aggregate the power consumption of the unit into subsamples to increase the
detection rate to 98.35%.
The remaining section of this paper is systematized as thus: Sector 2 converses the
fundamental concepts of IoT, the safety issues of the IoT, and research trends in IoT
and Artificial Intelligence (A.I.) Techniques. Section 3presents overview concepts
of Ransomware, types of Ransomware and Ransomware spreading and infection.
Section 4discusses the rationale for the choice of machine learning procedures
for ransomware discovery in IoT gadgets. Section 5presents the practical case of
ransomware detection in IoT devices using Machine Learning Techniques. Finally,
Sect. 6concludes the study and addresses potential works to realize productive
utilization of machine learning aimed at perceiving Ransomware in IoT.
2 The Fundamental Concepts of IoT
Over the last year, IoT generated enormous interest in science. The concept of IoTs
seen as part of the future Internet and will consist of billions of smart things communi-
cating with each other. Some early IoT implementations in the healthcare, transporta-
tion, and automotive industries have already been developed [53,54]. IoT systems
are currently in their infancy; however, there have been several recent advances
throughout the integration of sensor objects into the cloud-based internet [53–56]. IoT
creation includes several issues, including communication networks, applications,
protocols and standards [53,57]. The terms Internet and Things refer to a worldwide
interconnected network emphasizing visual technologies, networking, information-
processing and connectivity, the latest version of interaction, which could be ICT [58].
Smart sensing and wireless communication technologies, in particular, became some
of the IoT’s components and the newest opportunities and forensic horizons appeared
[59]. Figure 1displays some of the domains where successful implementation of IoT
has been demonstrated.
IoT is triggered by Radio Frequency Identification technology [60,61], becoming
much more popular in various industries such as manufacturing and retail [62].
Disruptive technologies are the term used for technological advances that have
changed how ordinary products are done globally. The Internet of Things (IoT)
has been implemented in many aspects of daily life as part of the Disruptive Tech-
nology community. It is projected that the demand for IoT devices will rise to 20–30
billion by 2020 [63]. So far, IoT has provided a range of innovations, for example,
a remote detector network, barcodes, intelligent monitoring, RFID [64], NFC, fast
energy digital communications, cloud storage, etc. [57,65–67]. Such technology
evolutions introduce innovations to IoT [68–70].
It is projected that the demand for IoT devices will rise to 20–30 billion by
2020 [63,71–74]. The growing value of the Internet of Things (IoT) would have
a major effect on the economy, according to a study [71–74]. The IDC estimates

396 R. O. Ogundokun et al.
Fig. 1 Internet of things
that IoT devices have future earnings of $8.9 trillion [75,76]. Companies take note
and invest in this region. With IoT’s rising effect, we can see a rise in demand for
qualified professionals to support this business. Universities have begun introducing
IoT related courses in computing majors to meet this need, and many credential
awarding organizations have started.
The linking of physical objects to the Internet enables remote sensor data to
be accessed, and the physical environment monitored from a distance [77,78]. A
smart entity, the Internet of Things building block is just another word embedded
in the internet network [79–81]. Radio Frequency Identification is another tech-
nology pointing in the same direction as IoT. Radio Frequency Identification system
is an evolution of overwhelmingly powerful optical barcodes used on many everyday
goods. It allows an intelligent, low-cost electromagnetic identification tag to be added
to the label so that a product’s identity can be decoded from a distance [81]. The
tagged item is a smart object by adding more information into the I.D. tags. IoT inno-
vation does not in any groundbreaking new technology but rather in the ubiquitous
deployment of intelligent products.
For several years the notions of intelligent computers, tablets, intelligent automo-
biles, smart homes, smart cities called a digital planet have been advocated [82–84].
To date, several different and sometimes disjoint researchers have investigated how
these are done. Five such influential research groups are the IoT, personal computing,
ubiquitous computing (P.C.), remote device networks, and, most recently, virtual-
physical systems. Nonetheless, in both of these fields, as technology and solutions
advance, the convergence and integration of concepts and investigating questions is
growing. Most computer science, electronic engineering, and electrical engineering
are interested in the smart world view. Greater connections will promote development
in these groups [82–84].

Application of Machine Learning for Ransomware Detection … 397
2.1 The Security Issues of the Internet of Things
Internet of Things (IoT) is a modern concept incorporating elements and technologies
that came arising out of diverse perspectives [74]. Worldwide estimate, omnipresent
estimate, Online Network rule of conduct, identifying automation, networking
automation including entrenched devices are combined to create a network in which
the physical and expert system planets converge and are constantly communicating
in a symbolic manner [74,85]. The intelligent entity is the building block of the IoT
dream [14,79,80].
The IoT was initially anticipated in the preceding centenary, though attraction has
improved beyond the late 20 years [86], and several possible benefits exist. A recent
analysis found that by 2025 IoT would possess a probable fiscal effect of $3.9tn to
$11tn yearly [86–88]. Forecasting on the number of IoT gadgets that are associated
differs widely. A commonly cited figure is arising out of Cisco, which predicts fifty
billion items together with gadgets been associated in 2020 [89,90].
Although the IoT holds a great deal of promise, several security concerns
have been reported [91–93]. The Open Platform Application Protection Project
(OWASP) listed the 10 highest ranked affairs affecting IoT gadgets [94–99]. These
security problems are defined thus: apprehensive network communication; inade-
quate verification and authorization; apprehensive system facilities; nonexistence
of transport scrambling; confidentiality concerns; apprehensive cloud interchange;
apprehensive mobile communication; inadequate device arrangements; apprehen-
sive program/firmware; in addition to weak environmental protection [96–99]. This
is reverberated by work started by H.P. Fortify, and the results have been shown in
Fig. 2in general, deduced 70% of the extremely utilized IoT gadgets encompasses
safety vulnerabilities, including a typical amount of 25 safety issues are observed for
each system.
Health, confidentiality, and evidence security are essential apprehensions of
Internet of Things (IoT) product manufacturers [65,97]. Therefore, while there isn’t a
huge extent of dependability as concerns safety requirements, a little protection stage
is not suitable to household facilities concerning people’s protection and well-being
[97–101].
Each connected computer in IoT may be a possible gateway within the IoT
substructure or individual figures [37,102–107]. From initial design to operating
services, security must be discussed across the IoT lifecycle [108–110]. Data secu-
rity, privacy, and confidence are crucial research issues in the IoT scenario [92,111,
112]. Figure 3shows the pressures for IoT security. Old-style protection instru-
ments would be incompetent to completely support IoT gadgets because the greatest
part of the gadgets possesses cordless restrictions, including inadequate possessions;
nevertheless, they need additional possessions [92].

398 R. O. Ogundokun et al.
Fig. 2 Device-surface IoT safety susceptibilities. Source H.P. Fortify
Fig. 3 Design of security apprehensions in an internet of things milieu

Application of Machine Learning for Ransomware Detection … 399
2.2 Research Trends in IoT and Artificial Intelligence (A.I.)
Techniques
Technology is still a gateway to human society [113–115]. Human society has under-
gone three radical changes until now. Agricultural technology made the first revo-
lution; these innovations were the new age of human civilization started with colo-
nization [116,117]. The 2nd breakthrough was made with the locomotive, which
shifted the power source from animals to machines [118–120]. The industrial age
had its transformations, beginning with engine power [121,122]. With the aid of
large-scale production systems just like a conveyor belt, the dimension of manufac-
turing had transformed dramatically [123,124]. Once the substantial affluence is
met, the humanoid pursuit progresses to the subsequent stage of society [125–127].
More than 20 years have passed since information technology made the last revo-
lution. It is time to plan for the next revolution after the Neolithic revolt, the manu-
facturing revolt and the technology revolt, dubbed ‘the fourth wave.’ [117,128].
It can often be referred to as the ‘4th industrialization (industry 4.0)’ concerning
heat-powered machines, mass production systems, and I.T. [129–131]. Technologies
converge not only with communities but also with every human being in industry 4.0,
according to the World Economic Forum [124,132,133]. Technologies that make
integration possible, therefore, become the main innovations.
Machine intelligence is a technology developed to reflects, reason and handle
information. Smartness with A.I. can be done in several different services [134,
135]. Amenities ought to identify the setting’s framework where they are installed
to be smart and to decide astronomically what to do in the given situation to achieve
the objective. Primary issues in A.I. include improving the depth of knowledge
through symbolic and numerical approaches, rationalizing knowledge base, implicit
knowledge and applying knowledge base to intelligent services [136,137]. Recent
developments in machine learning techniques have triggered significant growth in
creativity and technology [138,139].
AI-based techniques and strategies are expected to be gradually incorporated into
the IoT Stack’s various IoT architectural layers and to create an application for
artificial intelligence of things for IoT’s next-generation [140]. The existing segment
of the Artificial Intelligence (A.I.) industry is segmented and has no shared vision of
where A.I. can help humans, defined by most operations relying on a line approach
to solutions. In IoT, Artificial Intelligence could be used to improve privacy, protect
resources and minimize theft. It’s not quite clear how to use Artificial Intelligence
for the good of man globally.
Today, A.I. includes the understanding and application of different computational
tools, including the collection of data and simulation, data management (including
language processing, machine learning), but also analysis through analytics and visu-
alization of IoT system [55,141] and the development of potential IoT applications
for A.I. of things (AIoT). AI-based includes understanding and applying various

400 R. O. Ogundokun et al.
computational techniques, including data analysis and processing and data manage-
ment [134,142,143]. It also involves algorithms, insightful learning and interpreta-
tion of natural language by modelling and simulation, among others [144,145]. A.I.
methods are gradually integrated into the architectural layers of IoT and IoT Layer
[55,140]. Then build A.I. of things (AIoT) applications for the Internet of things
applications in the coming years. They are implementing AI-based technologies to
process IoT data for automated strategic decisions in different IoT architectural layers
introduces sensibly integrated IoT environments that could be merged with emerging
technologies (A.I., bots, automation, AR/VR) to create cognitive solutions through
a cognitive IoT partnership system [140,146] as results of high available computing
capacities. The IoT and Deep learning (DL) has been commonly used in Personal-
ized Healthcare (P.H.) applications [147–149]. In general, these systems are a series
of interconnected IoT tools for predictive analysis, diagnosis, remote monitoring,
preventive analysis and, in some cases, surgery [147,150–152]. Through develop-
ments in IoT, machine intelligence, and networking technology, cloud computing
provides a new approach to the issue by partially or fully processing the data at the
edge of a control network [153,154].
3 Overview Concepts of Ransomware
Ransomware as-a-service archetypal aiding smooth accessibility and arrangement
has to turn out to be a substantial universal risk, and the probable aimed at huge returns
forming a practical felonious enterprise archetypal. Ransomware is an extortion-
based malware attacker; it locks/encrypts the data on the victim’s machine and asks
for a ransom (money) payable in Bitcoin, thus, name ransomware [155,156]. From
the last four to five years, some high-profile ransomware attack was reported and
got attention. Due to the WannaCry attack, which affected the British Health care
system, this type of attack got attention [157–159]. Ransomware tries to coerce
currency from its target by scrambling archives and requesting reimbursement in
exchange for recovery [21,23]. Typically aimed archives are the victim’s Archives
files, electronic mail, images, and melody. Gradually, Ransomware poses a threat to
the security of the information infrastructure. Unlawful deployment of Ransomware
has inflicted millions of dollars of monetary damage on end-users and businesses
[155]. Malware intrusion into end-user devices and subsequent theft of their network
or data, Ransomware has emerged as a danger that the cyber-security community
needs urgent attention and containment. It now accounts for nearly $1 billion in
damages [23,160–166].
With an estimated 7 billion devices worldwide in 2018 and a rise of over 22 billion
devices by 2025 [70,167], the Internet of Things (IoT) affects all aspects of life.
Ransomware is malware designed for one particular purpose: to block access to
computers or files before a sum of money is charged, usually in bitcoin [28,168,169].
The average payment for Ransomware ranges from $500 to $5000, but payments
were recorded as high as $150,000 [28,169]. Ransomware costs to companies and

Application of Machine Learning for Ransomware Detection … 401
Fig. 4 Ransomware lifecycle
customers alike were projected at about $8 billion, and the cost rises to $20 billion by
2021 [170]. Ransomware is a lucrative industry for criminals and has risen by 56%
from the end of 2016 to the beginning of 2018 [171–173] to no surprise. Cybercrim-
inals will have new ways to reveal company vulnerabilities and extort money from
customers with the steady growth of IoT apps [174–177]. Ransomware’s success has
created a unique cybercriminal ecosystem. Consequently, this study aims to provide
a detailed understanding of the threat of Ransomware in IoT devices, address recent
detection techniques used and use a machine learning approach to detect it in IoT
devices. The positive attack on Ransomware has significant financial consequences,
which are fueled by many sophisticated enablers such as encryption technology,
cyber currency, and accessibility.
3.1 Ransomware Lifecycle
Due to the rapid growth of its attacks and the development of new variants able
to circumvent antivirus and anti-, Ransomware has attracted considerable attention
from cybersecurity experts in recent years [176,178]. There are seven stages in the
ransomware lifecycle, as shown in Fig. 4[176]. The development stage also includes
refining the codes at the end of each cycle to improve the Ransomware’s potency.
3.2 Taxonomy of IoT Security
Figure 5shows the taxonomy built based on different criteria, including threats,
requirements, IEEE standards, deployment levels, and technologies.

402 R. O. Ogundokun et al.
Fig. 5 Taxonomy of IoT security [92]
3.2.1 Threats
In IoT, systems material can be exposed or lost. So, it is essential to protect personal
uprightness, keys and credentials [92,179,180]. Subsequently, vulnerable IoT apps,
together with their gadgets, reveal entrenched ownership procedures that might
be effortlessly copied illegally or scrutinized [181], intellectual property can be
compromised. To avoid discovering unknown bugs, it is recommended that reverse
engineering, study, or manipulation of the code is usually made more difficult for
hackers.
3.2.2 Requirements
Strategies for authenticity are used to ensure the continuity and reliability of the data.
Digital signatures and hash functions are used to ensure data integrity. Besides, data
concealment must be maintained in the direction of storage position and the system
route in the IoT system. It applies to shield information from unauthorized access
and divulgation. An IoT system, for example, does not disclose the sensor reading
to its surroundings.

Application of Machine Learning for Ransomware Detection … 403
3.2.3 IEEE Standards
The standardized IEEE P1363 specifies irregular encoding procedures, such as math-
ematical essentials for concealed password creation. Additionally, it employs a very
mathematical foundation in consideration of the cryptosystem structure.
3.2.4 Deployment Level
Safeguarding of devices or facilities is a significant issue, and the implementation
of superlative routine which includes confining outside computer connections, deac-
tivating impressionable gadgets/endpoints from undeviating online network entree,
certifying that only particularized networks are allowed, safe loading (exploiting
passwords) and safe firmware, implementing system verification in everylink facility,
Revamping and repairing devices on O.S., creating whitelisting links, and introducing
stable key exchanges [180]. External and system protection solutions are efficient for
segregating delicate shreds of evidence. The amenity contributor has to acquire and
generate certificates of affirmation [182]. Cloud protection and IT-industries require
special attention. Most IoT created data is stored in the cloud [183].
Motivation-Rational for choosing Machine Learning techniques for
Ransomware detection in IoT devices
Artificial Intelligence systems provide tremendous advantages, which can be mali-
ciously used. Extremely focused and difficult attacks in friendly carriers [184], such
as DeepLocker, demonstrated a deliberate use of Artificial Internet for negative
purposes [185,186]. Over the past years, artificial intelligence (A.I.) technologies
have progressed rapidly, and their capabilities have extended into several domains
[187]. From smart governance, smart buildings, smart transportation, and smart grids
to smart “anything,” A.I. turns the flood of data into actionable information [134].
These A.I. technologies are important to internet security by gathering large quanti-
ties of amounts of information and then process easily to detect malicious patterns
and anomalous behaviors [188–194].
Therefore, conventional information gathering, management, and analysis
methods might not operate on this scale [195,196]. Also, the complexity of the
data produced by IoT provides a different front for the existing data processing
mechanisms [197–200]. Personal security and data protection are two of the main
considerations in the company implementation of IoT facilities and implementations
[102,198]. The shortcomings of the IoT systems and the world in which they work
present additional security problems for software and systems [102,201,202]. To
present, protection and confidentiality problems in the IoT domain have been thor-
oughly investigated from various perspectives such as communication protection,
data security, privacy, architecture security, authentication, malware detection, etc.
[203–205]. Therefore, this study is based on ML’s applications in providing IoT
protection and privacy services again Ransomware.

404 R. O. Ogundokun et al.
4 Practical Case of Ransomware Detection in IoT Devices
Using Machine Learning Techniques
The Ransomware comes accompanying the goal to tackle and govern computer
systems and elementary substructures; thus, they are exceptional and improved mali-
cious programs. The threats are majorly pointed at straightforwardly or circuitously
assembling coinage from preys requesting for ransoms in trading for decoding
secured passwords [206].
4.1 Modeling the Power Consumption
The universal energy devouring of a gadget can be modeled as the quantity of the
energy devouring of each solitary gadget constituent. The level of approximation of
the model is given both by the precision of the measurements for the single compo-
nents and by the completeness of the component set. It is said that a good approx-
imation of a complete set of components allowing precise measures of the power
consumption of a smartphone is: CPU, Screen, Wi-Fi, Cellular radio, GPS, Flash
memory, GPU, Bluetooth, Audio amplifier. This modeling leads to the formulation
of the equation shown below, where Ciis the energy devouring of the ith gadget
constituent at a specified cordless dimension.
C=
i
Ci(1)
Assumed an action scheduled the gadget (for example document conveyance)
the universal devouring is modeled on the point that the quantity of the aids of the
whole hardware constituents connected to the schedule and of the aids to power
devouring that are self-governing as of from the same action (considered as “corrupt
devouring”). Thus, universal consumption C is defined as:
C=
i
(fi+gi)=B+P8(2)
where
fiis the ith component of the base consumption;
giis the activity for the ith component associated with a precise autho-
rized/malevolent consumption;
B=
i
fiis the base consumption of the complete constituents;
P8=
i
giis universal consumption associated with precise action?
The consumption footprint of a particular operation (Ps) can be extracted by calcu-
lating the amount of the base consumption of all components (B) and by subtracting

Application of Machine Learning for Ransomware Detection … 405
such value from the global one calculated in a given moment. In practical terms,
calculating global power consumption requires creating a proper software measure-
ment module for each hardware device capable of capturing its unique characteristics.
The first contribution to that task was given in Curti et al. [207], where a model for
the Wi-Fi component was defined correspondent software module was provided.
4.2 Refining the Model for the Wi-Fi Consumption
Just as shown in the preceding segment, the general model isolates the energy contri-
butions of diverse hardware constituents, which ought to be independently deter-
mined by ad hoc units. The process of populating the sum with the appropriate
contributions has been started in [207,208], where a model for the Wi-Fi has been
provided and, through experiments, the value set of the model parameters have been
empirically assessed. In fact, in Eq. 2, while all the f components may be considered
negligible compared to the power consumption due to the specific activity, it is not
possible to assume that all the g components but the Wi-Fi are null. It is instead
necessary to get to this formulation: Thus, the global consumption C is defined as:
C=
i
(fi+gi)=B+Ps+Gw+GC(3)
where
fiis the ith component of the base consumption;
giis the activity for the ith component associated with a precise autho-
rized/malevolent consumption;
B=
i
fias previously defined, the base consumption of the complete
components;
Ps=
i
giis the global consumption associated with the specific activity of
authorized/malevolent for all hardware components except Ww−FCand CPU?
4.3 The CPU Consumption
CCPU =If∗Vf∗T(4)
where If is the current absorbed by the CPU at operating frequency f,Vff, is the
voltage needed at operating frequency f, and T is the duration of the time frame in
which we assume the operating frequency is constant. While the model is general,
it contains parameters whose values are specific for single hardware, namely Ifand
Vf.

406 R. O. Ogundokun et al.
4.4 The Long Short-Term Memory (LSTM)
There are four neural network layers in the framework of LSTM that interact to
delete or add information to the cell state. The input tier vector xt, each accepts
three inputs, ht−1the output of the preceding tier and Ct−1value in the cell from
the previous cycle. The first step in LSTM is to determine how much preceding
information must be stored in the cell. The forgotten gate layer is defined by the
sigmoid layer. The sigmoid activation function (σ)takes xtand ht−1returns a value
between 0 and 1 as output, multiplied by the value of Ct−1. A production of 1 for
the sigmoid will maintain the previous value completely, and it will be replaced by
a value 0. LSTM’s structure comprises four neural network layers, networking to
delete or add information to the cell’s value from the previous process.
ft=σWfht−1,xt+bf(5)
The next move is to change the value of the memory cells. It has two parts and the
first is a sigmoid layer that determines which values are modified, and the second is
a tanh layer (δ)that is a hyperbolic tangent function that returns values between 1
and −1. The tanh layer generates Ctthe vector for new candidate values.
it=σWiht−1,xt+bi(6)
Ct=tanhWCht−1,xt+bC(7)
The old ct −1 state is multiplied by f t, which forgets the values we agreed on in
the previous step. The result will then be appended it∗ct. The consequence is the
new nominee value that will be stored in the cell. We were gathering new information
in the cell at this point.
The final step is the current-cycle output ht. The output is a filtered, cell-state
version of the value. Next, we run the sigmoid layer to determine which part of the
cell state we will be outputting. The cell-state value is given to the tanh function, the
output of which is multiplied by the sigmoid layer output. The production is then
moved to the next LSTM device within the network.
Ot=σWoht−1,xt+bo(8)
ht=Ot∗tanh(Ct)(9)

Application of Machine Learning for Ransomware Detection … 407
5 Proposed Method
The power usage of targeted applications is required for developing a ransomware
energy consumption for IoT devices. The study used Power—to track and review
the power consumption in 500 ms internals of all operating processes [13,18,208].
At a given model interval of each phase, Power-generated a log file containing an
arrangement of generated power practice in the direction of an accustomed experi-
ment intermission. The paper used three devices to perform the experiments, namely:
Android device, Laptop computer, and Projector. Some common applications such as
Gmail (version 9.6.83), Facebook (version 99.0.0.26.69), Google Chrome (version
53.0.2785.124), YouTube (version 11.39.56), and Skype (version 7.20.0.411) have
been installed to collect Ransomware and good-ware energy usage logs, and four
models of Ransomware have been running on all computers.
Power-Tutor was used to track and document the power consumption system
for 10 min when running Ransomware and the applications separately. Good-ware,
defined as running programs, imaged real-world use of user interactions. This process
was repetitive five times per device; thus, we obtained 10repeation ×3devices =30
energy custom models for separate applications, including Ransomware. The Power
employed for the entire gadgets has been planned to a limited extent because the CPU
of each device has its specification for power users to make a reasonable evaluation.
Across all controlled processes on the machines, the CPU power usage has been
standardized to [0, 1], where 1 provides the maximum CPU power utilization and 0
implies no power consumption. Codes were inscribed to implement logfiles, deduce,
and standardize control practice rates, together with creating row-standardized data
to extract and normalize power usage values. What row consists of a mark (i.e.,
good-ware or Ransomware) besides a structured energy consumption arrangement
for five minutes of operation.
Models of power consumption are divided into submodels to resolve a wide distri-
bution of characteristics before using categorization procedures towards classifying
the sticky tag of the submodels. It was assumed that a fixed window size (interval)
divides the power consumption models when the process began and pushes them
onward, beginning from the preliminary opinion of every illustration, thus adding a
novel submodel towards a collection of submodels in every stage. The model set defi-
nition receives, aperture extent w together with the directory created for the submodel
was described in algorithm 1. The submodel (Aperture) is a period-rate. Therefore,
for 6 Power-Tutor intervals it includes w =10 of the submodel, its time-length is 10
=5000 ms.

408 R. O. Ogundokun et al.
Algorithm I
Input: Model set
Input: aperture extent w
Output: Submodel set DB
DB ← { }
for i ← 1- n do
i ← 1
while (l + w), length(model) do
Appned (Label, Pi, …, Pi + w) to D.B.
l ← l + 1
end
end
return D.B.
={Model1,Model2, , Modeln}
To evaluate the model class for each submodel, each submodel has to be assigned
a name. The D.B. submodel database was trained in algorithm 2 using the classifier.
The model classification algorithm (1) was used after the model was split into a
series of submodels to classify the mark by the qualified classifier for each submodel.
Depending on the design, nearly all very much alike objects in the submodel database,
the method identifies the model class dependent. It determines its final label by
combining the labels of all submodels.
Algorithm II
Input: Submodel database DB & Model
Output: Label {R, G}
Labels ← { }
Classifier = Train-Classifier (DB)
Submodel = Grind Model Employing Algorithm 1
i ← 1
while i < dimensions (Submodels) do
L ← Classifier (Submodel i)
Append L to Labels
i ← i + 1
end
return most recurrent element in labels

Application of Machine Learning for Ransomware Detection … 409
The next section discusses the results.
6 Results and Discussion
LSTM network implementation was performed using R programming. The training
network is composed of 3 layers, with 64 LSTM nodes in each layer. The proposed
method used sigmoid as the activation function and Adam optimizer, as this is a
problem with binary sequences classification. The experiment was conducted using
various window sizes, ranging from 5 to 50 in each case with an increment of 5.
Therefore, each dataset includes submodels of the length w of the window size. The
classifier was used for the classification of all submodels of each sample. Using
LSTM, the ground data was measured. To summarize the results, another setting (K
=5, 10) was omitted, and K =1 is the setting with higher efficiency.
The assessment of success in machine learning has been used to assess the
proposed method. The following metrics widely used in the literature, namely: accu-
racy, recall, precision, and F-measure, were used to test the efficacy of the proposed
technique, along with other algorithms of machine learning. For the proposed
approach, the following machine learning was used as a comparison: Random Forest
(RF), Support Vector Machine (SVM), Neural Network (NN), and Dynamic Time
Warping (DTW) for k-Nearest Neighbor (KNN).
Table 1summarizes a performance comparison between the proposed model and
other related machine learning in IoT devices for ransomware detection. It can be seen
from the table that the built model performed better with 96.42% accuracy, followed
by KNN with DTW with 94.27% accuracy. SVM has an accuracy of 91.19%, NN
has 90.67% accuracy, and RF has 87.56% accuracy. The LSTM model with power
consumption ransomware detection for IoT obtained the best results. The LSTM
outperformed the other machine learning based on the devices used. The developed
LSTM machine learning algorithm for ransomware detection of power consumption
for IoT devices showed better performances across all evaluation metrics than similar
approaches.
Tabl e 1 Performance and evaluation metrics of machine learning procedures comparison overview
Model Accuracy Recall Precision F-measure
RF 87.56 78.57 88.89 81.20
SVM 91.19 94.20 88.37 88.44
NN 90.67 88.41 86.96 86.96
KNN and DTW 94.27 95.65 89.19 92.31
Proposed method 96.42 95.75 91.07 93.17

410 R. O. Ogundokun et al.
7 Conclusion and Future Work
The large-scale implementation of IoT and the technologies that include networking,
data storage, management, and analytics endanger and expose it to attacks by
Ransomware. The need for a secure and productive connection grows as the Internet
is more deeply incorporated through the IoT platform into our business operations.
When attacks have occurred, they will have enduring and crippling effects on IoT
growth. With the increased prevalence of things in our data-centric society and
Internet-connected devices, the security of IoT devices cannot be overemphasized.
The successful compromise of any IoT devices could hold the device to ransom.
For instance, the case of Ransomware holding any device and denying access or
not available for use to information in the IoT environment may perhaps harmfully
influence the process, thus causes reputation destruction to industries and result in
substantial financial damage.
The research presented an approach that uses its power consumption to detect
Ransomware in IoT devices. Precisely, to distinguish Ransomware from non-
malicious applications, use the single resident fingerprint of the energy consumption
of Ransomware. The power-use subsample sequences were created from splitting into
several sequences of energy consumption applications; thus, classified to create the
class labels of collected subsamples. The proposed model adopts a recurrent LSTM
classifier for the neural network, equipped with an improved version of the back-
propagation algorithm. Quality evaluation findings show the efficacy of the proposed
model for improving ransomware detection in IoT systems. The results of the exper-
iments showed that the method achieved a 96.42% detection rate and a 91.07%
accuracy rate. The proposed system only used LSTM for ransomware detection in
IoT devices. Future work, therefore, will seek to use feature selection algorithms to
select more informative features for better classification. It will also help create a
reliable framework by combining more than one machine learning technique, thereby
improving system output accuracy. In the future, to classify power consumption at
the line rate, the classifier will be written in C++ as a stream processing kernel to
speed up the classification process. Therefore, the proposed model will be proficient,
appropriately and proficiently active in real-time situations.
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