Classification of Malware Attacks Using Machine Learning In Decision Tree

Abel Yeboah-Ofori
International Journal of Security (IJS), Volume (11) : Issue (2) : 2020 10
Classification of Malware Attacks Using Machine Learning
In Decision Tree
Abel Yeboah-Ofori u0118547@uel.ac.uk
School of Architecture, Computing & Engineering yeboah007@hotmail.com
University of East London.
London, E16 2GA, UK
Abstract
Predicting cyberattacks using machine learning has become imperative since cyberattacks have
increased exponentially due to the stealthy and sophisticated nature of adversaries. To have
situational awareness and achieve defence in depth, using machine learning for threat prediction
has become a prerequisite for cyber threat intelligence gathering. Some approaches to mitigating
malware attacks include the use of spam filters, firewalls, and IDS/IPS configurations to detect
attacks. However, threat actors are deploying adversarial machine learning techniques to exploit
vulnerabilities. This paper explores the viability of using machine learning methods to predict
malware attacks and build a classifier to automatically detect and label an event as “Has
Detection or No Detection”. The purpose is to predict the probability of malware penetration and
the extent of manipulation on the network nodes for cyber threat intelligence. To demonstrate the
applicability of our work, we use a decision tree (DT) algorithms to learn dataset for evaluation.
The dataset was from Microsoft Malware threat prediction website Kaggle. We identify probably
cyberattacks on smart grid, use attack scenarios to determine penetrations and manipulations.
The results show that ML methods can be applied in smart grid cyber supply chain environment
to detect cyberattacks and predict future trends.
Keywords: Cyberattack, Malware, Machine Learning, Smart Grid, Decision Tree.
1. INTRODUCTION
The unpredictable nature of cyberattacks and the cascading effects of cybercrimes on the
business system have made it difficult for organizations to predict endpoint attacks. ML assist in
recognizing attack patterns using datasets of previous attacks to predict future attacks trends and
responses [1]. Endpoints are the third-party vendor systems, workstations, servers, handheld
mobile devices and AMI devices. Malware attacks have intensified by the distributed nature of the smart
grid in supply chain systems. Adversaries are using cyberattacks such as cross site scripting, cross
site request forgeries, session hijacking and remote access trojan attacks to commit cybercrimes
such as modification of software, manipulating of online services, manipulations electronic
products, diverting e-products and other security misconfigurations. Ford and Siraj 2015,
highlighted different issues in the applications of machine learning in cybersecurity by detecting
phishing, network intrusion, testing security properties of protocols and smart energy
consumptions profiling [2].
Machine learning techniques are applied in a cybersecurity environment to predict network
intrusions detections, malicious codes detections, amount of suspicious transaction, electric
power fraud anomaly detection, substation location frauds, and spam filtering for spear phishing
attacks, as well as determine the probabilities of attacks. We could use ML to detect anomalies
in HTTPs requests such as XXE, XSS, SSRF attacks in communication networks, authentication
bypass in password setting and SQL Injection in a database system. Soska and Christin 2014,
applied ML techniques to automatically detect vulnerable websites before the turn malicious [3].
Canali et al. 2014 applied ML techniques to detect the effectiveness of risk prediction based on
browsing behaviours [4]. Hinks et al. 2015 use ML techniques on various classification algorithms

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to learn dataset to detect power system disturbance and cyberattack discrimination [1]. Mohasseb
et al. 2019 applied ML techniques to analyze a dataset from various organizations to improve
classification accuracies [5]. These works are important and contribute to detecting and predicting
cyberattacks using machine learning in the cybersecurity domain. However, there is a limited
focus on smart grid vulnerability from supply chain perspective, and specifically on threats relating
to inbound and outbound chain contexts that need adequate detection to improve smart grid
security control and decision makings.
In this paper, we use ML techniques to learn datasets and build a classifier to automatically
detect and label an event as Has Detection or No Detection. The rationale for choosing the DT
algorithm is that DT represents the major supervised schemes for ML in network security. We use
a dataset from Microsoft malware prediction [6] for our work. To demonstrate the effectiveness of
our approach, we adopt the decision tree algorithm to evaluate our data sets based on the attack
classifications.
The main contribution of this paper is threefold. Firstly, we identify probably cyberattacks on the
smart grid and the vulnerable sports that could be exploited through penetration and
manipulations base on the telemetry dataset. Secondly, we use attack scenarios to determine the
penetration and the manipulations for the threat predictions on the endpoint nodes. Finally, we
use ML techniques to learn the dataset and use the DT algorithm to predict whether the endpoint
nodes can classify if the nodes can detection cyberattack or not using Has Detection or No
Detection. The results show that ML algorithms in Decision Trees methods could be applied in
smart grid supply chain predictive analytics to detect cyberattacks and predict future trends.
The rest of the paper is structured as follows: Section 2 presents an overview of related works in
the machine learning in smart grid supply security domain and the existing classification
algorithms. Section 3 considers our approach to evaluating the ML techniques to learn dataset
and the classification algorithms for smart grid supply chain, CPS smart grid infrastructure and
the vulnerable spots and probable attacks scenarios. Further, it discusses the data
representation, feature descriptions and extractions as well as the classification algorithm.
Section 4 presents the implementation of the machine learning simulation process, performance
evaluation on the classifier and determines the average accuracy of the model and predict the
probability of penetrations on the endpoint nodes. Section 5 presents the results and analysis of
the DT that predicts the cyberattack initiated and the cybercrimes committed or not. Further, we
provide discussions of the several observations identified in the study. Finally, section 6 presents
a conclusion of the study, comparisons of existing works, limitations and future works.
2. RELATED WORKS
This section reviews related works and the state of the art of cybersecurity in machine learning
predictions, decision tree classifications and how they are related to malware attacks on CPS
environment. That includes identification of previous classification approaches, leveraging the
classifications of malware with a specific data set and prediction task used. Sharmar et al. 2012
proposed an ML technique for detecting worm variants of known worms in real-time systems [7].
Tsai et al. 2009, proposed a review of the intrusion detection system by using ML techniques and
various classifiers on the intrusion detection domain [8]. Wang et al 2014. Performs an empirical
study of adversarial attacks against ML models in the context of detecting malicious
crowdsourcing systems [9]. Bilge et al. 2017, proposed a risk teller system that predicts cyber
incidents by analyzing malicious files and infection records according to the endpoint protection
software installed to determine machines that are at risk [10]. Canali et al. 2014 performed a
correlation analysis on the effectiveness of risk prediction based on user browsing behaviour by
leveraging ML techniques to provide a model that can be used to estimate the risk class of a
given user [4]. Barros 2015 posits that decision threats and induction methods in general, arose
in machine learning to avoid acquisition bottleneck for expert systems [11]. Villano, 2018,
proposed a method of classification of internet logs using ML techniques by correlation and
normalization process and evaluated the DT algorithm that could predict an attack or not [12].
Soska & Christin 2014, proposed a complementary approach to automatically detect vulnerable

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websites before they turn malicious by design, implement and evaluate a novel classification
system which predicts whether a given website could be compromised in future [3]. Hinks et al.
2014, proposed an ML technique for power system disturbance and cyberattack discrimination by
evaluating various ML methods for an optimal algorithm that is accurate in its classifiers to predict
disturbance discriminators and implications [1]. Yavanoglu et al. 2017, proposed a review of
cybersecurity datasets for ML algorithms by analyzing network traffic and detecting abnormalities
used for experiments and evaluation methods considered as baseline classifiers for comparisons
[13].
2.1 Decision Trees
Decision Tree is used as a method in ML for classification and regression in large and complex
data in order to discover patterns. DT is built as a tree structure to classify instances of an attack
by plotting each malware attack attributes from the top and down to its root. Each branch of the
dataset is broken down into subsets to represent a choice of possible values for the attributes of
output, and each leaf represents a decision. DT is used in supervised ML for classification and
regression [11]. DT inference process starts at its root and proceeds to the leave. DT processes
include splitting, pruning and tree selection [14]. Splitting includes partitioning the data into
subsets, pruning includes the process of reducing the tree by turning some branch nodes into leaf
nodes, and tree selection involves finding the smallest tree that fits the data. Each attribute is
assigned a node, and in the leaf are the probable outcome or state. DT uses inductive inference
as a method to arrive at a conclusion based on the independent input and using the dependent
values as attributes. There are several approaches to DT algorithms such as J34, C4.5, C5.0 cart
and others. We used C5 method to identify which attribute was the root of the tree [15]. Figure 1
shows an example of a DT.
Malware Detection
Unknown
Has Detection No Detection
Has Detection
Has Detection
No Detection
No Detection
Root
Branch
Internal
Node
Leaf
Nodes
FIGURE 1: Decision Tree.
2.2 Decision Tree Selection Criteria
Decision Tree uses various algorithms for inferences to arrive at a conclusion, therefore, it is
required to have a selection criterion with certain characteristics that we can use to determine the
challenges. The characteristics include: Challenges originating from the classification of the data
sets could be numerous. For instance, identifying attacks that are initiated through Intelligence
Electronic Devices on smart grid systems or classifying staff salaries based on qualifications,
skills and experience.
2.3 Rational for Chosen Machine Learning and Decision Tree
Several algorithms have been used in ML. such as Naive Bayes, SVM, Random Forest and
Logistic Regression. However, our rationale for chosen DT algorithms in ML is that it provides

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discrete outputs as the factors are provided by attributed value pairs for strategic management
decision makings. E.g. Results: Pass or Fail. Cyberattack: Internal or external. Temperature: hot
or cold. Outcome: Positive or Negative.
 DT algorithm can identify attributes pairs that were not considered initially in the
classification such as the source of attack but could work without those attributes to
minimize inferred errors.
 DT algorithm can handle datasets that have errors in the attribute values and resolve
classification errors in the training and test phase. Such as false positives (FP) output
when network traffic is a normal or false negative (FN) when network traffic is under
cyberattack. The discrete probability outputs provide results that predict a ‘True or False’,
‘Yes or No’ and ‘A or B) outcomes.
3. APPROACH
This section considers our approach to evaluating the ML techniques to learn dataset and the
classification algorithms for the smart grid supply chain. We discuss the smart grid infrastructures
and the vulnerable spots, attack scenarios and the ML approach. The rationale for the ML
approach using DT to predict an attack is to determine the causal relationships amongst the
cyberattacks on a smart grid supply chain system and attempt to predict the malware using
probability distribution methods. Then based on the classification analysis, we evaluate the
predictive method with appropriate metrics to verify the organizational goal and security goal as
we seek to determine whether a specific cyber threat phenomenon is likely to appear in a similar
event. There are some algorithms for building decision trees such as ID3 and C4.5 formula and
others [15]. We discuss the ML methods used, as well as the approaches used for the malware
prediction.
3.1 CPS Smart Grid Infrastructure
The CPS smart grid infrastructure in figure 1, integrates application and network systems using
Intelligence Electronic devices (IEDs). Refer IEC 61850 [16] The application system uses the
IEDs, Sensors, Actuators and other communication devices for power generation, distribution,
and transmission. The Supervisory Control and Data Acquisition (SCADA) and Programmable
Logic Controls (PLC) establishes communication protocols with the Remote Telemetric Units
(RTUs) for monitoring and gathering real-time data across various substation. The network
system provides interconnectivity between substations, automation systems and field devices
such as AMI and Home Energy Management Systems (HEMS) software [17] [19].
SCADA Server
Command Center
Workstation
SCADA Network
Communication Network
Switchboard
CSC Vendors
Systems
Snort
FirewallIED
Threat Actor 1
Router
Sub Station
Server
Third Party
Vendors
Syslog Open PDC
Firewall
workstation
WAN
Threat Actor 2
Threat Actor 3
ISP
Cyber Physical
Generation
Transmission Distribution
FIGURE 2: Smart Grid System Infrastructure and Vulnerable Spots [19].

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The adversary could cause cyberattacks (penetration) and cybercrimes (manipulation) on the
CPS. Cyberattacks such as remote access trojan, spear phishing, cross site scripting or session
hijacking on the intelligent devices and communications networks to penetrate firewalls, IDS/IPS
or the IEDs. After penetrating the system, the adversary could commit cybercrimes by
manipulating the system to cause resonance attacks, DDoS attacks, IP theft, ID theft, intellectual
property theft as well as take command and control to monitor and control the core business
processes and operations. We include these attack scenarios in the analysis to determine the
validity of the penetration and manipulations in real-time.
3.2 Attack Scenarios
We identify various attack scenarios for the study that will assist in the feature selection process
as follows.
 Network Attack: An XSS or session hijacking attack on the CSC network may provide
access to alter the smart metering system, change configurations using distance
protection scheme to bypass controls in order to manipulate the software in the meter,
prevent the system from recording accurate purchases or billings.
 Spyware Attack: The attacker could insert spyware or deploy a ransomware attack
remotely to shut the systems down when the antivirus is outdated, and the software is
unpatched, and consequently affect the prepaid card settings change the configurations
using distance protection scheme so an attacker can manipulate and prevent accurate
readings from valid purchases.
 Ransomware Attack: The attacker could use reconnaissance and social engineering
tactics to gather intelligence and subsequently initiate a spear phishing attack on targeted
users to shut the system down until a ransom is paid.
 Software Manipulation Attack: Most organizations fail to change the hard-coded
password after buying software off the shelf. The attacker could deploy session hijacking
techniques to exploit this vulnerability using advanced persistent threats and command &
control techniques to manipulate the system and consequently cause cybercrimes such
as intellectually property theft, ID theft and industrial espionage.
 DDoS or Data Injection: Attacker deploys DDoS attack that could consequently cause
voltage surges by inserting a rootkit into the OS server to cause resonance attack on the
smart grid components for the power system to oscillate.
 Island Hopping attack: On the CSC systems, vendors are more susceptible to
cyberattacks, and the perpetrators are using RAT and Island-hopping attacks to gain
access to the major organizations on the supply chain.
 Malware: The attacker could insert malware or spyware in the software that is bought off
the shelf that gives the developers access to the system whenever users are prompted to
update their software. That may cause software errors and subsequently lead to
application system manipulations.
3.3 Threat Prediction Scenarios
The threat prediction attempts to investigate two kinds of scenarios that will determine the
classification result. The scenarios use’s ML techniques to determine the cyberattack initiated and
the cybercrime committed based on the scenarios and the cyberattacks.
 Scenario 1: what is the probability of the penetration on the endpoint nodes?
 Scenario 2: What is the extent of manipulation on the various endpoint node?
3.4 Analytical Approach
To determine the viability of using ML techniques to learn dataset for penetrations and
manipulations on the CPS, we used the DT algorithm and open source data from Microsoft
Malware Predictions endpoint protection solutions website [3]. DT provides an efficient and
nonparametric method that can be applied to a classification or regression task. Further, we used
supervised learning to train and test the dataset as it provides an accurate prediction of system

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performance. Using DT hierarchical data structures for supervised learning provides input space
that is split into local regions in order to predict the dependent variable for decision makings [11].
3.5 Data Representation
The data represented Microsoft Windows Machine’s probability of getting infected by various
families of malware, based on different properties of that machine. The telemetry data containing
threat report were collected by Microsoft Windows Defender from various MS windows operating
systems [6]. The properties and machine infections were generated by combining various user
activities on different organization and vendors. The dataset we used contains 4000 entries. DT
algorithm used determines attributes that return the highest information gain that satisfies the four
uncertainty axions in a confusion matrix and provides the degree of disorganization in the
dataset. Further, an Entropy formula was used to determine the information gained and the
degree of uncertainty by separating the positive and negative rates as follows:
Entropy (E) = - a log1 a – b log2 b (1)
3.6 Feature Extraction
The feature extraction process involves removing irrelevant columns names or duplicates in the
dataset to have unique values when training the data. Columns with a higher number of
duplicates are removed to the correct data. The command is to count the 62 variables and
remove the irrelevant variables. The output prints 62-8 = 54. (8 columns removed). However, the
4000 datasets were maintained. The classifier is set to model the features based on the
importance as well as the F Score. The F-Score was used as the harmonic mean to determine
the combinations of the precision and recall for plotting the model.
3.7 Classification Algorithm
The classification phase involves using the ML algorithm to test the dataset for prediction. In this
phase, the DT model was used to split the data for prediction to determine if each endpoint node
can detect infections or not. We considered C4.5 or C5 algorithms [15]. The training data is used
to build the DT model, and the test data is used to determine the dimensionalities of the dataset.
The rationale for choosing the DT algorithm is that DT represents the major supervised schemes
for ML in network security. We train the ML algorithm using the training sets. Then compare the
performance of the algorithm over the datasets.
4. IMPLEMENTATION
This section discusses the implementation of the machine learning simulation process. The
purpose of the study is to use the DT algorithm to predict cyberattack and indicate it as Has
Detection or No Detection. The dataset and the machine malware infections were gathered by
Microsoft Defender endpoint protection [6]. The dataset corresponds to a machine identifier that
provides results as to whether the Microsoft endpoints can predict if it can detect malware attacks
on the nodes. As discussed in section 2, the DT algorithm learns from data sets to approximate
an ‘if then else’ decision rules and generate branches for the tree nodes and decision nodes. We
follow the process below to build the DT classifier for our prediction.
4.1 Description of Data
The dataset is about a malware attack on Microsoft Endpoint system and such systems can be a
critical part of the smart grid CSC systems overall business continuity [6]. The dataset was
designed to meet certain business constraints in relation to privacy and time periods in which
machine was used. CSC integrates various organizational systems for the business process and
information dissemination in the CPS environment. The data set containing these properties and
the machine infections were generated by combining threat reports collected by Microsoft
Endpoint Protection Solution, Windows Defender. Each row in the dataset corresponds to a
machine unique identified by a Machine Identifier. Further, the dataset was created to meet
certain business constraints, both regarding privacy and when the machine was running. Hence,
the dataset is relevant for our work as it was gathered from global machines that used Microsoft

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Windows Defender. The rationale for using the dataset for our work is that the dataset does not
represent Microsoft customers machine only as it has been sampled to include a much larger
proportion of malware machines. Thus, we used the dataset for our work to determine whether
the has detection or no detection on various network nodes for threat predictions. Below are
some of the features from the metadata that are relevant for our work [6].
 MachineIdentifier - Individual machine ID
 GeoNameIdentifier - ID for the geographic region a machine is located in
 DefaultBrowsersIdentifier - ID for the machine's default browser
 OrganizationIdentifier - ID for the organization the machine belongs in.
 is protected - This is a calculated field derived from the Spynet Report's AV Products
field.
 Processor - This is the process architecture of the installed operating system
 HasTpm - True if the machine has tpm
 over - Version of the current operating system
 OsBuild - Build of the current operating system
 Census_DeviceFamily - AKA DeviceClass. Indicates the type of device that an edition of
the OS is intended for desktop and mobile
 Firewall - This attribute is true (1) for Windows 8.1 and above if windows firewall is
enabled, as reported by the service.
4.2 Data Preparation
The dataset represents Microsoft malware prediction events collected from various families of
malware infections based on different properties of attacks. Windows Defender tool was used to
generate the threat reports of the malware infections from various Microsoft endpoint protection
solutions. [6]. The dataset derived 4000 entries with 64 columns, and each row represents
different metadata entry. Each row in the dataset corresponds to a machine uniquely identified by
a Machine Identifier. We used supervised learning to derive the dataset that represented the
instance of each table and attribute. The rationale is to predict an outcome for future events. The
variables in the datasets are for each instance to determine whether a malware attack is Has
Detection or No Detection.
4.3 Feature Selection
The features for the dataset are split into the partition of the subsets of attacks as indicated in
table 1. The attack features indicate the categories of attacks grouped. The splitting of the attack
categories builds the classifications model for the three structure and breaks it down to represent
the attack features. Further, we pruned the dataset to reduce the size of the tree by turning some
branches nodes into leaf nodes. For instance, we categorized the attacks based on the threat
descriptions in the table for us to fit the training data for the classifier and finds the tree that
produces the lowest cross validation.
Attack
Category
Attack Features Threat Descriptions for Probable Cause of Attack
1 XSS/Session
Hijacking
Default Browser vulnerabilities and injecting code in the URL
or website
2-5 Spyware/Ransomware Outdated Antivirus/Patches that are not updated regularly
6-7 Spear Phishing Use Reconnaissance to identify vulnerable spots and attach
email with a virus
8-9 Session Hijacking Exploit Unchanged Hard-Coded password in software
bought off the shelf
10-14 Rootkit/DDoS Attack on BIOS or attach a virus to a USB key to cascade
when booting.
15-20 RAT/Island Hopping Attacks from Vendor systems to gain access to the
organizational system
21-28 Ransomware/Malware Exploiting outdated OS versions and encryptions especially
TLS/SSL

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29-35 Malware/Spyware Packet injection and Resonance attacks
36-38 DDoS Exploit IP Address Systems and Packet injections
TABLE 1: Attack Category and Feature Descriptions.
4.4 Performance Evaluation on the Classifier
The performance evaluation on the classifier determines the average accuracy of the models
when we run the integer values in the cell. The performance of the model will be determined on
the following values: True Positive (TP), True Negative (TN), False Positive (FP) and False
Negative (FN) rates. Further, the FPR and FN will be determined based on the elements.
(2)
5. RESULTS
In this section, we present the analysis of the investigation of threat prediction to the two
scenarios for the classification results. We discuss adversarial ML briefly and how adversaries
use ML techniques to exploit vulnerabilities. As discussed in section 3.3, the scenarios use the
DT algorithm to predict the cyberattack initiated and the cybercrime committed.
5.1 Determine the Accuracy of the Threats
For us to predict the probability of an attack, we need to determine the known and the unknown
attacks. As listed in Table 1, Known attacks include Malware, Spyware, Ransomware and, RAT,
Cross-Site Scripting, Session Hijacking, Cross Site Request Forgery. These are the known
attacks that could be identified. However, unknown attacks are cyber crimes committed after the
attacks. Here, after gain access using the known attacks, the attacker, using APT and C&C to
commit cybercrimes such as manipulation during development, manipulation during development,
altering and changing delivery channels. The extent of these cybercrimes manipulations and the
cascading impact are unknown and unquantifiable.
Scenario 1: Predict the probability of penetration on the endpoint nodes?
Determining the accuracy process involves evaluating the threats, and its impacts on the various
network nodes for understanding and to provide cyber threat intelligence of the causes and
effects cyberattacks on the organizational goal, the business process, financial impact. Table 2
presents the performances accuracies of the DT classifier of each cyberattack on various
endpoints of the network. Using the confusion matrix, we determine the harmonic mean between
the Precision (P), Recall (R) and F-Score (F). From the table, XXS/Section Hijacking, spear
phishing, RAT/Island Hopping attacks predicted a higher probability of the penetration on the
endpoint nodes with a percentage score of 82%, 75% and 75% respectively. However, the results
revealed the XSS and Session Hijacking are the most like penetration method to deploy base on
the predictions.
SCENARIO DT PREDICTIONS
ACCURACY 83% 100%
CYBERATTACKS P R F RESULTS
XSS/Session Jacking 0.89 0.41 0.75 82%
Spyware/Ransomware 0.89 0.58 0.85 87%
Spear Phishing 0.81 0.37 0.71 75%
Session Hijacking 0.71 0.39 0.64 65%
Rootkit/DDoS 0.66 0.37 0.68 55%
RAT/Island Hopping 0.67 0.30 0.74 68%
Ransomware/Malware 0.89 0.55 0.71 85%
Malware/Spyware 0.87 0.58 0.78 84%
DDoS 0.78 0.36 0.65 66%
TABLE 2: Predicting the Probability of Penetration.

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Scenario 2: Predict the extent of manipulation on the various endpoint node?
Predicting the extent of manipulations on the various endpoint nodes after penetrations are very
challenging due to the invincibility, uncertainty and fuzzy nature of cybercrimes. Further,
determining the extent of cyberattack propagation and manipulations in an integrated network
environment posse a major challenger in the cybersecurity discipline. From Table 2: the results
indicate that:
 Ransomware, Malware and Spyware predicted a higher probability of manipulations on
the endpoint nodes with a percentage score of 87%, 85% and 84% respectively after
determining the Precision and F-Score with a low Recall rate.
 That indicates that the extent of manipulation in a given event could be high with an
average accuracy of 85%.
 The manipulations could result in cyberattacks such as Industrial Espionage, Intellectual
property theft, Advanced Persistent Threat and Command & Controls.
6. DISCUSSION
Predicting cyberattacks in real-time is challenging due to factors such as type of OS being used,
system refresh rates, time zones, running updates and data in transition. Attacks such as
Ransomware or malware may impact on the system based on the OS being used, the origin of
the attack and due to the time zone. Threat actors could use adversarial machine learning
techniques to exploit vulnerabilities in ML threat predictions
6.1 Adversarial Machine Learning
Adversarial machine learning is a technique used by the adversary to inject malicious input data
in the dataset during the training and testing phase to manipulate the classification algorithms for
the model. The technique can be used in supervised learning algorithms for cybersecurity
datasets to exploit vulnerabilities and compromise performance results of malware detections,
spam filters and IDS/IPS intrusions when predicting cyberattack trends and predicting the
probability of fraudulent activities. The adversary could cause an increase in the false-positive
rates by inserting malicious samples in the test phase to generate wrong classifications rates of
the sample data. The adversarial machine learning technique could be used to manipulate
training data to violate security policy, gain knowledge of threat intelligence, adversary
capabilities and level of manipulations.
6.2 Determining Processor Count for Vulnerable Operating System
The classification of the malware attack is built based on the type of operating system that is
being used by the organization. The OS determines the nature of antivirus that can be installed
and could be exploited on each system and if it can detect malware attacks or not. An outdated
antivirus within a third-party system could easily be a point of failure if a malware attack is
initiated from there leading to power loss, power surge, system error or power fluctuations issues.
Addressing downtime and uptime in the event of failure is critical for all the organizations that are
integrated on the supply chain. For instance, a redundant array of independent disk (RAID) uses
multiple hard drives in unique groupings and storage capacity mechanisms to produce a storage
solution that provides improved throughput, resistance, and resilience. These drivers rely on
antivirus updates and patches as trusted sources to prevent any compromises. Figure 3 explains
the ML processor count of the systems and how it determines the speed at which the malware
attack could occur as well as the extent of propagation in the event of an attack. The X-axis
determines the process count for the vulnerable OS. The Y-axis determines the number of OS
that are affected. From Figure 3, we realized that the processor count of 3000 was able to affect
4.0 systems. Indicating that the region identifier may have fewer systems with a higher probability
of penetration and manipulation. Thus, exploiting outdated OS versions and encryptions
especially TLS/SSL raises antivirus protection issues and application interoperability on the
various network nodes. Users can be lured into a false sense of security by the threat actor
across deferent platforms to update the antivirus that could lead to heuristic detections or false
positives.

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FIGURE 3: Processor Count for Vulnerable Operating System.
6.3 Resolution Rate of Probable Ransomware Attack
The figure displays the detection rate of the training dataset using the feature description of smart
screen usage in the CSC environment. The implication of the of our results is that attackers have
used malware attacks to penetrate the smart screen and inset spyware in the system that turns
the camera on in the smart screen monitor. With that, the attacker can see everything the victim
will do, take command and control, leading to cybercrimes attacks such as Intellectual property
and industrial espionage. Refer to the FLocker mobile ransomware attack on smart screens
(Duan. 2016). Further, the attacker could use social engineering tactics such as spear phishing
to cause ransomware attacks to deface the monitors and cascade to other smart screen systems
on the CSC.
Ransomware attacks could affect CSC system platforms that use multiple smart screens monitors
by infecting a single screen and may propagate to others on the monitors with the same network
nodes and lock the screens during run time. Section 4.1 describes the dataset and how each row
in the dataset corresponds to a machine unique identified by a Machine Identifier gathered from
global machines that use Microsoft Windows Defender. The FLocker ransomware infects smart
screens and avoids detections as the code is always being rewriting to improve its routine
variants and meet changing trends. When launched, the malware identifies the country ID, the
machine ID and activates depending on the motives and intents of the adversary. Figure 4
identifies the vertical resolution rates of the various systems and how the infections propagate
through the systems during run time. The Y-axis indicates the extent of vertical infections and the
X-axis indicates the resolution rates of the infected systems. Malware or ransomware that is
embedded directly into the requested web page in the attack could propagate to other systems.
FIGURE 4: Resolution Rate of Probable Ransomware Attack.

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6.4 Decision Tree Predictions
The DT in Figure 5 depicts the results of the classifier that predict a Windows machine probability
of getting infected by attacks listed in table1, based on various properties of that machine. The
properties used to generate the DT are, SmartScreen, CountryIdentifier, AVProducts,
OSInstallTypeNname, TotalPhysivalRam, OsBuildLab, OSWUAutoOptionsName. The Smart
screen represents workstations. The Country identifier represents the country the Windows
Operating System is located. According to a report by Controller and Audit General on the
investigations of ‘WannaCry’ Ransomware Attack in 2017, the attack initially infected the NHS
system the UK and then propagated to other countries across the world and infected various
system [18]. The report indicated that the OS antivirus product was outdated hence the attack.
The objective of the paper is to use ML techniques on a dataset to predict whether the system
can detect an attack and label that as Has Detection or No Detection. From, the sample dataset
of 4000, we predict the probability of ransomware attack infection based on the type of OS
Installed that could lead to the vulnerability of the ransomware infecting the smart screen as well
as the country the OS is installed and the version.
6.5 Gini Index Based Decision Tree
The Gini index based decision tree was the calculation for Smart Screen Malware (M) Infection
Trend.
 If the dataset (D) contains examples from n class, then the Gini index, gini(D) is defined
as:
(3)
Where pi is the probability of an object being classified to a particular class that infected.
 If a dataset (D) is split on the root (R) into two sets subsets D1 and D2 the gini index (D) is
expressed as:
(4)
The Reduction in Impurity for the split in the dataset was calculated as:
Gini(R) = gini(D) – giniR (D) (5)
From the DT algorithm, we calculate the information gained after the malware (M) infection trend
test is applied on the smart screen for the classification. A weighted sum of Gini Indices was
calculated using the DT and generated the Has Detection and No Detection tree.
Figure 6 depicts the DT indicating the results of the gini index used to measure the probability of
infections of a ransomware attack that may be wrongly classified. The DT root indicates a smart
screen rate of <= 6.5 with a split Gini of 0.5 indicating an equal distribution of the dataset. The
root of the three has an initial dataset of 4000 as the sample size. The DT algorithm split the
value into two sets: [1973, 2027]. From the analysis, 1973 were identified as has detection, hence
are not vulnerable to the attacks. However, 2027 were found to have no detection hence
vulnerable to malware or ransomware attacks. The branch with has detection is indicated as
(True) and the other with no detection is indicated as (False). identified as has identified as a
country identifier with the class Has detection, identified the values of 2531 and 1648 from a
sample size of 3531. A sample size of 2955 has antivirus product installed. However, the total
physical RAM has no detection rate of 1458. The DT split the sample size further till the values
were at the threshold. Figure 5 depicts the gini index calculated and information gained after the
DT test is applied.

Abel Yeboah-Ofori
FIGURE 5: Decision Tree Predictions.
The results from Scenarios 1 and 2 provides cyber threat intelligence as to what could happen in
the event of a cyberattack without the classifications of the detections rates in Figure 5.
Scenario 1 predicted a higher probability of the penetration’s attacks on the endpoint nodes
after determining the harmonic mean between the Precision, Recall and F-Score with a
percentage score of:
 82% for XXS/Section Hijacking
 75% for spear phishing
 75% for RAT/Island Hopping attacks
Scenario 2, determining the extent of cyberattack propagation and manipulations in an integrated
smart grid network environment. The results show that cyberattacks such as Ransomware,
Malware and Spyware predicted a higher probability of attack propagation and manipulations to
other systems.
 The results indicate the extent of manipulation to other integrated network systems could
be high with an average accuracy of 85% in a given event.
 The extend of manipulations indicates the relevance of the classification of the
cyberattack. The threat intelligence indicates that it could result in cyberattacks such as
Industrial Espionage, Intellectual property theft, Advanced Persistent Threat and
Command & Controls.
6.6 Comparing Our Results with Existing Work
A significant amount of literature exists in machine learning techniques and classification
algorithms to learn dataset for performance accuracies in the cybersecurity domain that have
considered threat predictions. Comparing our results to existing works, we considered works that
used ML methods from cyberattack penetration and cybercrimes manipulations perspective to
detect attacks. Hinks et al. 2014, considered an ML technique for power system disturbance and

Abel Yeboah-Ofori
cyberattack discrimination by evaluating various ML methods for accurate classification to predict
disturbance discriminators and implications [1]. Sharmar et al. 2012 use ML to detect worm
variants of known worms in real-time [7]. Bilge et al. 2017 applied ML techniques to predict a risk
teller system for cyber incidents by analyzing malicious files [10]. Canali et al. 2014 used the ML
method to perform a correlation analysis of the effectiveness of risk prediction based on user
browsing behaviour [4]. Villano, 2018, applied ML method for correlation and normalization
process and evaluated the DT algorithm that could predict an attack or not [12]. Soska & Christin
2014 used the ML approach to automatically detect vulnerable websites [10]. Mohasseb et al.
2019 applied ML approach for predictive analytics using SVM and Naïve Bayes algorithms for
evaluation accuracies [5].
Further, various DT algorithms, models and techniques have been implemented using a various
dataset for building intrusion detections, anomaly detection and threat predictions. Pournouri et al
(2017) proposed a cyber attack analysis using decision tree techniques to learn an open source
intelligence dataset for prediction and for improving cyber situational awareness [21]. Patel and
Prajapati (2018) proposed a study and analysis of decision tree based classification using ID3,
C4.5 and CART algorithms to learn a dataset to determine the best performance accuracy [22].
Moon et al (2017) proposed an intrusion detection system based on a decision tree using
analysis of attack behaviour information to detect the possibility of intrusion for preventing APT
attacks [23]. Sarker et al (2020), presented a machine learning intrusion detection system based
security model called “IntruDTree” that evaluated various algorithms on a dataset by ranking the
security features according to their importance then build a generalized tree for detecting
intrusions [24]. Das & Morris (2018) presented a survey of machine learning and data mining
methods for cybersecurity applications and analytics for intrusion detection and traffic
classifications in emails by evaluating the various classifications algorithms on a dataset for
performance accuracies [25]. Balogun & Jimoh (2015) proposed a hybrid of DT and KNN
algorithms to detect anomaly intrusions [26]. Malik et al (2018) used a hybrid of DT pruning and
BPSO algorithms for network intrusion detection [27]. Rai et al (2016) proposed C4.5 DT
algorithm to construct a model for intrusion detection [28]. Yeboah-Ofori & Boachie (2019)
present a malware attack predictive analytics using various ML Classification algorithms in a
majority voting for performance accuracies [29]. Ingre et al (2017) proposed a DT algorithm that
classifies an IDS dataset as normal or attack after the learning and testing the dataset [30]. Relan
and Patil (2015) used a variant of C4.5 DT algorithm to implement an IDS by considering discrete
values for classifications [31].
However, none of the works explored the viability of using machine learning methods to predict
malware attacks and build a classifier to automatically detect and label an event as Has Detection
or No Detection on smart grid supply chain domain to predict the probability of penetration and
the extent of manipulation on the network system nodes for cyber threat intelligence and
situational awareness.
7. CONCLUSION
Our work focused on using ML to learn dataset and used the DT algorithm to determine whether
the classifier can predict an attack and label the attack as Has Detection or No Detection. In this
paper, we have used a malware prediction dataset from a well-known source learn the dataset.
We have used the DT algorithm to model the infections. Although, other algorithms can perform
the same task that the DT could handle datasets that have errors in the attribute values and
resolve classification errors in the training and test phase. Based on our result, the precision was
83% accurate and concluded that supervised learning model performed better in our predictions.
Description of objects may include attributes based on measurement or subjective judgement,
both of which might give rise to errors in the values of the attributes. Some of the objects in the
training set may even have been misclassified. Take, for instance, a malware attack classification
rule from a collection of cyberattacks events. An attribute might test for the presence of
propagation of attack that might give a positive or negative reading at some point. However,
questions remaining to be addressed as to what performance evaluation methods could provide

Abel Yeboah-Ofori
the best performance indicators for threat predictions and cyber threat intelligence gatherings that
could provide security control mechanisms. There are limitations in our work, such as comparing
other classification algorithms for predictive analytics due to the invincibility nature of cyberattacks
and the cascading impacts on other system nodes.
Future Works
Future research will focus on using ML techniques on various classification algorithms to learn
the dataset for anomaly detection and to predict cyberattacks trends. The approach will assist to
determine the best performance metrics, for cyber threat intelligence and predict future trends.
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