Utilisation of Artificial Intelligence and Cyber Security Capabilities The Symbiotic Relationship for Enhanced Security and Applicability

Abstract

The increasing interconnectivity between physical and cyber systems has led to more vulnerabilities and cyberattacks. The traditional preventive and detective measures are no longer adequate to defend against adversaries, Artificial Intelligence (AI) is used to solve complex problems, including cyber security. Adversaries are also utilising AI for sophisticated and stealth attacks. This study aims to address this problem by exploring the symbiotic relationship of AI and cybersecurity to develop a new, adaptive strategic approach to defending against cyberattacks and improve global security.
This paper explores different disciplines to solve security problems with real world context, such as the challenges of scalability and speed in threat detection and response. It develops an algorithm and a predictive model for a Malicious Alert Detection System (MADS) that detects security events and predicts threats in an environment. It evaluates the model's performance and efficiency.
The paper explores Machine Learning (ML) and Deep Learning (DL) techniques and their applicability in cyber security and limitations of using AI. Additionally, it discusses issues, risks, vulnerabilities and attacks against AI systems. It concludes by providing recommendations on security for AI and AI for security, paving the way for future research on enhancing AI-based systems and mitigating its risks.

Full text

Article Not peer-reviewed version
Utilisation of Artificial Intelligence and
Cyber Security Capabilities: The
Symbiotic Relationship for Enhanced
Security and Applicability
Ed Kamya Kiyemba Edris *
Posted Date: 24 March 2025
doi: 10.20944/preprints202503.1742.v1
Keywords: artificial intelligence; cyber security; machine learning; detection; alerts, adversarial attacks;
predictive model; evaluation
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Article
Utilisation of Artificial Intelligence and Cyber Security
Capabilities: The Symbiotic Relationship for
Enhanced Security and Applicability
Ed Kamya Kiyemba Edris †
University of Hertfordshire, Computer Science 1; e.edris@herts.ac.uk; e.edris@herts.ac.uk
†University of Hertfordshire, Hatfield, United Kingdom.
Abstract: The increasing interconnectivity between physical and cyber systems has led to more
vulnerabilities and cyberattacks. The traditional preventive and detective measures are no longer
adequate to defend against adversaries, Artificial Intelligence (AI) is used to solve complex problems,
including cyber security. Adversaries are also utilising AI for sophisticated and stealth attacks. This
study aims to address this problem by exploring the symbiotic relationship of AI and cybersecurity
to develop a new, adaptive strategic approach to defending against cyberattacks and improve global
security. This paper explores different disciplines to solve security problems with real world context,
such as the challenges of scalability and speed in threat detection and response. It develops an
algorithm and a predictive model for a Malicious Alert Detection System (MADS) that detects security
events and predicts threats in an environment. It evaluates the model’s performance and efficiency.
The paper explores Machine Learning (ML) and Deep Learning (DL) techniques and their applicability
in cyber security and limitations of using AI. Additionally, it discusses issues, risks, vulnerabilities and
attacks against AI systems. It concludes by providing recommendations on security for AI and AI for
security, paving the way for future research on enhancing AI-based systems and mitigating its risks.
Keywords: artificial intelligence; cyber security; machine learning; detection; alerts, adversarial attacks;
predictive model; evaluation
1. Introduction
Cyber security is a fast-evolving discipline, and threat actors are constantly endeavouring to stay
ahead of security teams with new and sophisticated techniques. The use of interconnected devices and
accessing data ubiquitously has increased exponentially, raising more security concerns. Traditional
security solutions are becoming inadequate in detecting and preventing sophisticated attacks. However,
advances in cryptographic and Artificial Intelligence (AI) techniques show promise in enabling cyber
security experts to counter such attacks [
1
]. AI is being leveraged to solve a number of problems using
chatbots to virtual assistants and automation, allowing humans to focus on higher-value work but also
used for predictions, analytics, and cyber security.
Recovery from a data breach costs $4.35 million on average and takes 196 days [
2
]. Organisations
are increasingly investing in cyber security, adding AI enablement to improve threat detection, incident
response (IR) and compliance. Patterns in data can be recognised using Machine Learning (ML),
monitoring, and threat intelligence to enable systems learn from the past events. It is estimated that
AI in the cyber security market will be worth $102.78 billion by 2032 globally [
3
]. Currently, different
initiatives are defining new standards and certifications to elicit users’ trust in AI. The adoption of
AI can improve best practices and security posture, but also create new forms of attacks. Therefore,
secure, trusted and reliable AI is necessary by integrating security in design, development, deployment,
operation and maintenance stages.
Failures of AI systems are becoming common, it is crucial to understand and prevent them as
they occur [
4
,
5
]. Failures in general AI have a higher impact than in narrow AI, but a single failure in a
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© 2025 by the author(s). Distributed under a Creative Commons CC BY license.

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superintelligent system might result in a catastrophic disaster with no prospect of recovery. AI safety
can be improved with cyber security practices, standards, and regulations. Its risks and limitations
should also be understood, and solutions should be developed. Systems commonly experience
recurrent problems with scalability, accountability, context, accuracy, and speed in the field of cyber
security [
6
]. The inventory of ML algorithms and techniques have to be explored through the lens of
security. Considering the fact that AI can fail, there should be models in place to make AI decisions
explainable [7].
This paper explores AI and cyber security with use cases and practical concepts in a real-world
context based on field experience of the authors. It gives an overview on how AI and cyber security
empower each other symbiotically. It discusses the main disciplines of AI and how they can be applied
to solve cyber security complex problems and challenges of data analytics. It highlights the need to
evaluate both AI for security and security for AI to deploy safe, trusted, secure AI-driven applications.
Furthermore, it develops an algorithm and a predictive model, Malicious Alert Detection System
(MADS) to demonstrate AI applicability. It evaluates the model’s performance. Proposes methods to
address AI-related risks, limitations and attacks. Explores evaluation techniques and recommends a
safe and successful adaption of AI.
The rest of the paper is structured as follows. Section 2reviews related work on AI and cyber
security. In section 3, the security objectives and AI foundational concepts are presented. Section 4
discusses how AI can be leveraged to solve security problems. The utilisation of ML algorithms for
security use cases are presented in section 6. Section 5, presents a case study of a predictive model
MADS and AI evaluation techniques. The risk and limitations of AI are discussed in section 7. The
paper is concluded with remarks in section 8.
2. Background
Security breaches and loss of confidential data are still big challenges for organisations. The spike
in cyber threats has been triggered by the migration of data to the cloud, such as health, financial
records and digitalisation of social interactions [
8
]. Similarly, sensors and industrial devices that were
off the grid are now being connected to enterprise networks through Industrial Control Systems (ICS)
and the Internet of Things (IoT) [
9
]. Traditional approaches to protect this data are no longer adequate.
With the increased sophistication of modern attacks, there is a need to detect malicious activities, but
also to predict the steps that will be taken by an adversary. This can be achieved with the utilisation of
AI by applying it to use cases such as authentication, anomalous behaviour, traffic monitoring and
situation awareness [10].
Current AI research involves search algorithms, knowledge graphs, Natural Languages Process-
ing (NLP), expert systems, ML, and Deep Learning (DL), while the development process includes
perceptual, cognitive, and decision-making intelligence. The integration of cyber security with AI has
huge benefits, such as improving the efficiency of security systems, performance, and providing better
protection from cyber threats. It can improve an organisation’s security maturity by adopting a holistic
view of combining AI with human insight. Thus, socially responsible use of AI is essential to further
mitigate related concerns [11].
The speed of processes and the amount of data used in defending cyberspace cannot be handled
without automation. AI techniques are being introduced to construct smart models for malware
classification, intrusion detection and threat intelligence gathering [
12
]. Nowadays, it is difficult to
develop software with conventional fixed algorithms to defend against dynamically evolving cyber
attacks [
13
]. AI can provide flexibility and learning capability to software development. However,
threat actors have also figured out how to exploit AI and use it to carry out new attacks.
ML methods are vulnerable to adversarial learning attacks, which aim at decreasing the effec-
tiveness of threat detection [
14
]. These attacks are also effective when targeting Neural Networks
(NN) policies in Reinforcement Learning (RL) [
15
]. AI models are facing threats that disturb their
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data, learning, and decision-making. Deep Reinforcement Learning (DRL) can be utilised to optimize
security defence against adversaries using proactive and adaptive countermeasures [16].
The use of a recursive algorithm, DL, and inference in NN have enabled inherent advantages over
existing computing frameworks [
17
]. AI-enabled applications can be combined with human emotions,
cognitions, social norms, and behavioural responses [
18
] to improve societal issues. However, the
use of AI can also lead to ethical and legal issues, which are already big problems in cyber security.
There are significant concerns to data privacy and applications’ transparency because of the severe
ambiguity of the current ethical frameworks. It’s also important to address the criminal justice issues
related to AI usage, liability, and damage compensation.
2.1. Artificial Intelligence
A system to be considered to have AI capability, must have at least one of the six foundational
capabilities to pass the Turing Test [
19
] and the Total Turing Test [
20
]. These give AI the ability to
understand the natural language of a human being, store and process information, reason, learn from
new information, see and perceive objects in the environment, manipulate and move physical objects.
While some advanced AI agents may possess all six capabilities.
The advancements of AI will accelerate, making it more complex and ubiquitous, leading to the
creation of a new level of AI. There are three levels of AI [21]:
1.
Artificial Narrow Intelligence (ANI): The first level of AI that specialises in one area, cannot solve
problems in other areas autonomously.
2.
Artificial General Intelligence (AGI): AI that reaches the intelligence level of a human, having
the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn
quickly, and learn from experience.
3.
Artificial Super Intelligence (ASI): AI that is far superior to the best human brain in all cognitive
domains, such as creativity, knowledge, and social abilities.
Figure 1. ML Pipeline.
Even ANI has been able to disrupt technology unexpectedly, such as Generative AI, chatbots and
predictive models. AI enables security systems such as Endpoint Detection and Response (EDR) and
Intrusion Detection System (IDS) to store, process and learn from huge amounts of data [
22
,
23
]. This
data is ingested from network devices, workstations, and Application Programming Interface (API),
which is used to identify patterns such as sign-in logs, locations, time-zone, connection types and
abnormal behaviours. These applications can take actions using the associated sign-in risk score and
security policies to automatically block login attempts or enforce strong authentication requirements
[24].
2.1.1. Learning and Decision-Making
The discipline of learning is the foundation of AI, the ability to learn from input data moves
systems away from the rule-based programming approach. An AI-enabled malware detection system
operates differently from a traditional signature-based system. Rather than relying on a predefined list
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of virus signatures, the system is trained using data to identify abnormal program execution patterns
known as behavior-based or anomaly detection, which is a foundational technique used in malware
and intrusion detection systems [25].
In the traditional approach, a software engineer identifies all possible inputs and conditions that
software will be subjected to, but if the program receives an input that it is not designed to handle,
it fails [
26
]. For instance, searching for a Structured Query Language (SQL) injection in server logs,
in the programming approach the vulnerability scanner will continuously look for parameters that
are not within limits [
27
]. It is also complicated to manage multiple vulnerabilities with traditional
vulnerability management methods. However, an intelligent vulnerability scanner foresees the possible
combinations and ranges, using a learning-based approach. The training data like source code or
program execution context is fed to the model to learn and act on new data following the ML pipeline
in Figure 1[28].
Figure 2. AI Agent.
2.1.2. Artificial Intelligence and Cyber Security
An intelligent agent is used to maximize the probability of goal completion. It is fed huge amounts
of data, learns patterns, analyses new data and presents it with recommendations for analysts to make
decisions as shown in Figure 2[
29
]. AI can be used to complement traditional tools together with
policies, process, personnel, and methods to minimize security breaches.
Utilising AI can improve the efficiency of vulnerability assessment with better accuracy and make
sense of statistical errors such as False Positive (FP) and False Negative (FN) [
30
]. Threat modelling in
software development is still a manual process that requires security engineers’ input [
31
,
32
]. Applying
AI to threat modelling still needs more research [
33
], but AI has already made a great impact on threat
detection [
6
,
29
]. It is also being utilised in IR, providing information about attack behaviour, threat
actor (TA)’s Tactics, Techniques and Procedures (TTPs), and threat context [34].
3. Objectives and Approaches
This section explores foundational concepts, objectives and approaches to cyber security.
3.1. Systems and Data Security
The main security objective of an organisation is to protect its systems and data from threats
by providing Confidentiality, Integrity and Availability (CIA). Security is enforced by orchestrating
frameworks of defensive techniques [
35
,
36
], embedded in the organisation’s security functions to align
with business objectives. Applying controls that protect the organisation’s assets using traditional and
AI-enabled security tools.
This enables security teams to identify, contain and remediate any threats with lessons learned for
feedback to fine-tune the security controls. The feedback loop is also used for retraining ML models
with new threat intelligence, newly-discovered behaviour patterns, and attack vectors as shown in
Figure 3. In addition, orchestrating security frameworks needs a skilled team but there is a shortage of
such professionals [
37
]. Therefore, a strategic approach to employment, training and education of the
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workforce is required. Moreover, investment in secure design, automation, and AI can augment the
security teams and improve efficiency.
Figure 3. Orchestrated Security Operation Framework.
3.2. Security Controls
A security incident is prevented by applying overlapping administrative, technical, and physical
controls complemented with training, and awareness across the organisation as shared responsibil-
ity. The security policies should be clearly defined, enforced, and communicated throughout the
organisation [
38
], championed by the leadership. Threat modelling, secure designing and coding
best practices must be followed together with vulnerability scanning of applications and systems
[
39
]. Defence in-depth controls must be applied to detect suspicious activities and monitor TAs’ TTPs,
unwarranted requests, files integrity, system configurations, malware, unauthorised access, social
engineering, unusual pattern, user behaviour and inside threat [40].
When a potential threat is observed, the detection tool should alert in real-time so that the team can
investigate and correlate events to assist in decision-making and response to the threat [
41
], utilising
tools like EDR, Security Information and Event Management (SIEM) and Security Orchestration,
Automation and Response (SOAR). These tools provide a meaningful context about the security events
for accurate analysis. If it is a real threat, the impacted resource can be isolated and contained to stop
the attack from spreading to unaffected assets following the IR plan [42].
4. Cyber Security Problems
This section discusses security problems and how AI can improve cyber security by solving
pattern problems.
4.1. Improving Cyber Security
Traditional network security was based on creating security policies, understanding the network
topography, identifying legitimate activity, investigating malicious activities and enforcing a zero-trust
model. Large networks may find this tough, but enterprises can use AI to enhance network security
by observing network traffic patterns and advising functional groupings of workloads and policies.
The traditional techniques use signatures or Indicators of Compromise (IOC) to identify threats, this is
not effective to unknown threats. AI can increase detection rates, but also increase FPs [
43
]. The best
approach is combining both traditional and AI techniques, which can result in a better detection rate
and minimizing FPs. Integrating AI with threat hunting can improve behavioural analytics, visibility,
and develop applications and users’ profiles [44].
AI-based techniques like User and Event Behavioural Analytics (UEBA) can analyse the baseline
behaviour of user accounts, and endpoints, and identify anomalous behaviour such as a zero-day attack
[
45
]. AI can optimize and continuously monitor processes like cooling filters, power consumption,
internal temperatures, and bandwidth usage to alert of any failures and provide insights into valuable
improvements on the effectiveness and security of the infrastructure [46].
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4.2. Scale Problem and Capability Limitation
TAs are likely to leave a trail of their actions and security teams use the context from data logs to
investigate any intrusion, but this is very challenging. They also rely on tools like IDS, anti-malware,
and firewalls to expose suspicious activities, but these tools have limitations as some are rule-based
and do not scale well in handling massive amounts of data.
IDS constantly scans for signatures by matching known patterns in the malicious packet flow
or binary code. If it fails to find a signature in the database, it will not detect the intrusion and the
impending attack will stay undetected. Similarly, to identify attacks such as brute force or Denial
of Service (DoS), it has to go through large amounts of data over a period of time [
47
] and analyse
attributes such as source Internet Protocol (IP) addresses, ports, timestamps, protocols and resources.
This may lead to a slow response or incorrect correlation by the IDS algorithm.
The use of ML models improves detection and analysis in IDS. It can identify and model the real
capabilities and circumstances required by attackers to carry out successful attacks. This can harden
defensive systems actively and create new risk profiles [
23
]. A predictive model can be created by
training on data features that are necessary to detect an anomaly and determine if a new event is an
intrusion or benign activity [48].
4.3. Problem of Contextualisation
Organisations must ensure that employees do not share confidential information with undesired
recipients. Data Loss Prevention (DLP) solutions are deployed to detect, block and alert if any
confidential data is crossing the trusted parameter of the network [
49
]. Traditional DLP uses a text-
matching technique to look for patterns against a set of predetermined words or phrases [
50
]. However,
if the threshold is set too high, it can restrict genuine messages and if set too low confidential data such
as personal health records might end up in users’ personal cloud storage, violating user acceptable
and data privacy policies.
An AI-enabled DLP can be trained to identify sensitive data based on the context [
51
]. The
model is fed words and phrases to protect, such as intellectual property, personal information [
52
] and
unprotected data that must be ignored. Additionally, it is fed information about semantic relationships
among the words using embedding technique and then trained using algorithms such as Naive Bayes.
The model will be able to recognize the spatial distance between words and assign a sensitivity level
to a document and make a decision to block the transmission and generate a notification [53].
4.4. Processes Duplication
TAs change their TTPs often [
36
] but most security practices remain the same, with repetitive
tasks that lead to complacency and missing of tasks [
54
]. The AI-based approach can check duplicative
processes, threats and blind spots on the network that could be missed by the analyst. AI self-adaptive
access control can prevent duplication of medical data with smart, transparent, accountable secure
duplication method [55].
To identify the timestamp of attack payload delivery, the user’s device log data is analysed for
attack prediction by pre-processing the dataset and creating DL classification to remove duplicate and
missing values [56].
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Figure 4. Statistical Errors.
4.5. Observation Error Measurement
There is a need for accuracy and precision when analysing potential threats, as a TA has to be
right only once to cause significant damage, while a security team has to be right every time [
54
].
Similarly, if a security team discovers events in the log files that point to a potential breach, validation
is required to confirm if it is a True Positive (TP) or FP. But validating false alerts is inefficient, a waste
of resources and distracts the security team from real attacks [57].
An attacker can trick a user into clicking on a Uniform Resource Locator (URL) that leads to a
phishing site that asks for user name and password [
58
]. Traditional controls are blind to phishing
attacks, and phishing emails look more credible nowadays. These websites are found by comparing
their URL against block lists [
59
], which get outdated quickly, leading to statistical errors as shown in
Figure 4. Moreover, a genuine website might be blocked due to wrong classification or a new fraud
website might not be detected. This requires an intelligent solution to analyse a website on different
dimensions and characterize it correctly, based on its reputation, certificate provider, domain records,
network characteristics and site content. A train model can use these features to learn and accurately
categorise, detect, block and report new phishing patterns [60].
4.6. Time to Act
A security team should go through the logs quickly and accurately, otherwise a TA could get into
the system and exfiltrate data without being detected. Today’s adversaries take advantage of the noisy
network environment, and they are patient, persistent, and stealth [
61
]. The AI-based approach can
help to predict future incidents and act before they occur with reasonable accuracy, by analysing the
users’ behaviour and security events whether the pattern is an impending attack or not.
A predictive model uses events, data collected, processed and validated with new data to ensure
high prediction accuracy as shown in Figure 5. It learns from previous logins by users’ behaviour,
connection attributes, device location, time and attacker’s specific behaviour to build a pattern and
predict malicious events. For each authentication attempt, the model estimates the probability of it
being a suspicious and risky login [45].
Figure 5. Security Monitoring Predictive Model.
5. Machine Learning Applications
This section explores ML algorithms that power the AI sphere. The collected data can be labelled
or unlabelled depending on the method used such as supervised, semi-supervised, unsupervised, RL
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[
62
] and pattern representation can be solved using classification, regression, clustering and generative
techniques.
Figure 6. Learn by Training.
The discipline of learning is one of the capabilities that is exhibited by an AI system. ML uses
statistical techniques and modelling to perform a task without programming [
63
], whereas DL uses a
layering of many algorithms and tries to mimic the Neural Networks (NN) [
64
] as shown in Figure 6.
When building an AI solution, the algorithm used depends on training data available, and the type of
problem to be solved. Different data samples are collected, and data whose characteristics are fully
understood and known to be legitimate or suspicious behaviour is known as labelled data. Whereas
data that is not known to be good or bad, and not be labelled is known as unlabelled data.
To train ML model using labelled data, knowing the relationship between the data and desired
outcome is supervised learning, whereas when a model discovers new patterns within unlabelled
data is unsupervised learning [
65
]. In RL, an intelligent agent is rewarded for desired behaviours or
punished for undesired ones. The agent has the capacity to perceive and understand its surroundings,
act, and learn through mistakes [66].
Since an algorithm is chosen based on the type of problem being solved and for cyber security,
ML is commonly applied to predict future security events based on the information available from
past events. Categorizing the data into known categories, such as normal versus malicious. Finding
interesting and useful patterns in the data that could not be found like zero-day threats. While
generating adversarial synthetic data that is indistinguishable from the real data is achieved by
defining the problem to solve, data availability and choosing a subset of algorithms for experiment
[67].
5.1. Classification of Events
Classification segregates new data into known categories and its modelling approximates a
mapping function
(f)
from input variables
(X)
to discrete output variables
(Y)
, its output variables
are called labels or categories [
68
]. The mapping function predicts the category for a given observation.
Events should be segregated into known categories, such as whether the failed login attempt is from
an expected user or an attacker, and this falls under the classification problem [
63
]. This can be solved
with supervised learning and logistic regression or K-Near Neighbors (k-NN) and it requires labelled
data. Equation 1is a logistic function that can be utilised to find probability prediction. It takes in a set
of features xand outputs a probability P(X).
P(X) = e(β0+β1x)
e(β0+β1x)+1(1)
where
β0
and
β1
are the parameters of the logistic regression model. The parameters
β0
and
β1
are learned from the training data.
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5.2. Prediction by Regression
Regression predictive modelling approximates a mapping function
(f)
from input variables
(X)
to a continuous output variable
(Y)
, which is a real-value [
69
] and the output of the model is a numeric
variable. Regression algorithms can be used to predict the number of user accounts that are likely to be
compromised [
70
], the number of devices that may be tampered with or the short-term intensity and
the impact of a Distributed DoS (DDoS) attack in the network [71]. A simple model can be generated
using linear regression with a linear equation between output variable (Y), and input variable (X), to
predict a score for newly identified vulnerability in an application. To predict value of (
Y
), put in a
new value of (X) using equation 2for simple linear regression.
y=β0+β1x+ϵ(2)
where
y
is the predicted value,
β0
is the intercept and the value of
y
when
x
is 0.
β1
is the slope,
x
is
the independent variable, where
β1
is the change in
y
for a unit change in
x
. The
ϵ
is the error term,
the difference between the predicted value and the actual value. Whereas, algorithm like support
vector regression is used to build more complex models around a curve rather than a straight line.
Regression Artificial Neural Networks (ANN) are applicable to intrusion detection and prevention,
zombie detection, malware classification and forensic investigations [13].
5.3. Clustering Problem
Clustering is considered where there is no labelled data and useful insights need to be drawn
from untrained data using clustering algorithms such as Gaussian distributions. It groups data with
similar characteristics but were not known before. For instance, finding interesting patterns in logs to
benefit a security task with a clustering problem [
23
]. Clusters are generated using cluster analysis
[
72
], instances in the same cluster must be similar as much as possible and instances in the different
clusters must be different as much as possible. Measurement for similarity and dissimilarity must be
clear with a practical meaning [73], this is achieved with distance 3and similarity 4functions.
d
∑
l=1xil −xjl 
n!1/n
(3)
where
xil
is the
ith
element of
lth
vector,
xjl
is the
jth
element of
lth
vector,
d
is the dimension of the
vectors, and nis the power to which the absolute values are raised.
J(A,B)=|A∩B|
|A∪B|(4)
where
A∩B
is the intersection of sets
A
and
B
,
A∪B
is the union of the sets
A
and
B
, and
|x|
is the
size of set x.
For clustering pattern recognition problem, the goal is to discover groups with similar characteris-
tics with algorithms such as K-means [
74
]. Whereas anomaly detection problem, the goal is to identify
the natural pattern inherent in data and then discover the deviation from the natural [
75
]. For instance,
to detect suspicious program execution, an unsupervised anomaly detection model is built using file
access and process map as input data based on algorithms like Density-based Spatial Clustering of
Applications with Noise (DBSCAN).
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Figure 7. GAN model.
5.4. Synthetic Data Generation
The generation of synthetic data has become accessible due to the advance of rendering pipelines,
generative adversarial, fusion models and domain adaptation methods [
76
]. Generating new data to
follow the same probability distribution function and same pattern as the existing data, can increase
data quality, scalability and simplicity. It can be applied to steganography, data privacy, fuzz and vul-
nerability testing of applications [
77
]. Some of the algorithms used are Markov chains and Generative
Adversarial Networks (GAN) [78].
The GAN model is trained iteratively by generator and a discriminator networks. The generator
takes random sample data and generates a synthetic dataset, whereas the discriminator compares
synthetically generated data with a real dataset based on set conditions [
79
] as shown in Figure 7. The
generative model estimates the conditional probability
P(X|Y=y)
for a given target
y
. Naive Bayes
classifier models
P(x
,
y)
and then transforms the probabilities into conditional probabilities
P(Y|X)
by applying the Bayes rule. GAN has been used for synthesizing deep fakes [
80
]. To get an accurate
value, Bayes’ theorem’s equation 5is used.
posterior =prior ×likelihood
evidence ⇒P(Y|X) = P(Y).P(X|Y)
P(X)(5)
where the posterior is the probability that the hypothesis
Y
is true given the evidence
X
. The prior
probability is the probability that the hypothesis
Y
is true before we see the evidence
X
. The likelihood
is the probability of the evidence
X
given that the hypothesis
Y
is true. The evidence is the data that
we have observed.
6. Malicious Alerts Detection System (MADS)
This section presents the proposed AI predictive model for Malicious Alert Detection System
(MADS). A predictive model goes through training, testing and feedback loops using ML techniques.
The workflow for security problems consists of planning, data collection and preprocessing, model
training and validation, event prediction, performance monitoring and feedback.
Detecting threats on a device depends on rules developed to detect anomalies in the data being
collected and analysed. It requires understanding the data, keeping track of events, correlating and
creating incidents on affected machines as shown in Table 1and Figure 5, where machine (
m
) represents
the endpoint {m0, ..., m5} and where (e) is an event and (S) is a stream of events {e0, ..., e10} of an attack
or possible multi-stage attack shown in Table 2. All machines generate alerts, but not all turn into
incidents or are TP, as shown in Figure 8.
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Figure 8. Alerts and Incidents.
6.1. Multi Stage Attack
The multistage attack illustrated in Figure 9utilises a popular malware, legitimate infrastructure,
URLs and emails to bypass detection and deliver IcedID malware to the victim’s machine [81], in the
following stages.
•
Stage 1:Reconnaissance, TA identifies a website with contact forms to use for the campaign.
Delivery, TA uses automated techniques to fill in a web-based form with a query which sends a
malicious email to the user, containing the attacker-generated message, instructing the user to
download a form with a link to a website. The recipient receives an email sent from a trusted
email marketing system, by clicking on the URL link.
•
Stage 2:Execution, downloads a malicious zip file, unzips to malicious JS file and executes via WS
script. Downloads IceID payload and executes the payload.
•
Stage 3: Redirects to malicious, top-level domain. Google user content page launches. Downloads
malicious ZIP file. Unzips a malicious JS file, and executes it via WS script. Downloads IceID
payload and executes the payload.
•
Stage 4:Persistence, IcedID connects to a command-and-control server. Downloads modules and
runs scheduled tasks, to capture and exfiltrate data. Downloads implants like Cobalt Strike for
Table 1. Security Events on in the System
Machine Events
m1 e5 e21 e63 e44 e3 e46 e7 e88 e9 e10
m2 e4 e20 e2 e32 e6 e9 e46 e10 e71 e88
m3 e99 e1 e2 e14 e18 e6 e3 e9 e50 e10
m4 e41 e33 e29 e4 e46 e6 e43 e8 e19 e2
m5 e1 e20 e99 e3 e66 e77 e7 e18 e4 e10
Table 2. Attack Types
Event Attack
e1 Phishing credential stealer
e2 Privilege escalation
e3 Exploit CVE
e4 Failed login attempts
e5 Suspicious file download
e6 Lateral movement
e7 Execution suspicious process
e8 Download from suspicious domain
e9 Command and control connection
e10 Data Exfiltration
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Algorithm 1 MADS Algorithm
Require: Machine M, Event Stream S
1:
Output: Initialize AlertList = [], IncidentList = [], k-NN Parameter: k, IncidentThreshold, IncidentID
= 1, MaliciousSamples = 0
2: for each incoming event ein Event Stream Sdo
3: AlertList.append(e)
4: if length(AlertList) >=kthen
5: maliciousSamples = detectMaliciousSamples(AlertList, k)
6: MaliciousSamples += maliciousSamples
7: if MaliciousSamples >=IncidentThreshold then
8: incident = createIncident(AlertList, IncidentID)
9: IncidentList.append(incident)
10: IncidentID++
11: MaliciousSamples = 0
12: AlertList.clear()
13: else
14: AlertList.removeFirstEvent()
15: end if
16: end if
17: end for
18: Output: IncidentList
19: Function: detectMaliciousSamples(alertList, k)
20: maliciousSamples = 0
21: for i = 1 to length(alertList) do
22: Di = k-NN(alertList[i], alertList)
23: vote = label_voting(Di)
24: confidence = vote_confidence(Di, vote)
25: if confidence >% and vote != alertList[i] then
26: maliciousSamples++
27: end if
28: end for
29: return maliciousSamples
30: Function: createIncident(alertList, incidentID)
remote access. Collecting additional credentials, performing lateral movement and delivering
secondary payloads.
Different machines might have similar events with the same TTPs but with different IOCs and could
be facing multi-stage attacks with different patterns [
36
]. There is no obvious pattern observed in
which a certain event
ex
would follow another event
ey
given stream
Si
. A predictive model is utilised
to identify FPs, recognize multiple events with different contexts, correlate, and accurately predicts
potential threat with attack story, detailed evidence, and remediation recommendation.
Figure 9. Multistage Attack Flow.
6.2. Detection Algorithm
The security event prediction problem is formalized as security event
ey∈S
at timestamp
y
,
where
S
is the set of all events. A security event sequence observed on an endpoint
mi
is a sequence
of events observed at a certain time. The detection of security alerts and the creation of incidents are
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based on the provided event streams and algorithm parameters. Algorithm 1takes machine (
M
) and
stream (
S
) consisting of multiple events as input. It uses AlertList to store detected security alerts and
IncidentList for created security incidents. The algorithm uses k-NN parameter (k) to determine the
number of nearest neighbours to consider and a threshold value
(IncidentThreshold)
to determine
when to create a security incident. The
(IncidentID)
is used to assign unique IDs to created incidents
and (MaliciousSam ples) to keep track of the number of detected malicious samples.
Figure 10. Data Description.
It iterates over each incoming event (
e
) in stream (
S
) and appends the event (
e
) to the AlertList.
Checks if the length of the AlertList is equal to or greater than
k
(the number of events needed for k-NN).
Uses detectMaliciousSamples function to identify potential malicious samples in the AlertList using
k-NN, and gets the count of malicious samples. Increments the MaliciousSamples counter by the count
of detected malicious samples and checks if they exceeded the IncidentThreshold. If the threshold is
reached, it calls the createIncident function to create an incident object using the alerts in the AlertList
and the IncidentID. Appends the incident object to the IncidentList. Increments the IncidentID for the
next incident, resets the MaliciousSamples counter to 0 and clears the AlertList. But if the threshold is
not reached, it removes the first event from the AlertList to maintain a sliding window and continues
to the next event. It also gives output of the IncidentList containing the created security incidents.
For each alert in the AlertList, the k-NN
(Di)
is calculated using the k-NN algorithm. It performs
label voting to determine the most frequent label (vote) among the neighbours. It calculates the
confidence of the vote (confidence). If the confidence is greater than 0.60 and the vote is not the same as
the original alert, it counts it as a potentially malicious and returns the count of malicious samples.
The incident object includes incident ID, alerts timestamps, severity,and affected machines.
The event to be predicted is defined as the next event
En
and each
En
is associated with already
observed events
Eo
. The problem to solve is to learn a sequence prediction based on (
S
) and predicting
En
for a given machine
Mi
. A predictive system should be capable of understanding the context and
making predictions given the (S) sequence in the algorithm and model output.
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(a) (b)
(c)
Figure 11. Confusion Matrix. (a, Machine 1) (b, Machine 2) (c,Machine 5)
6.3. Evaluation of the MADS Model
These are some of the evaluation techniques used when solving ML problems for cyber security
[
6
,
15
,
78
,
82
]. Some machines are excluded because their actual malicious labels had one class or no
incidents were created based on the data frame in Figure 10. For the classification problem, the model
is evaluated on TP, True Negative (TN), FP (Type I error) and FN (Type II error) and the elements of
the confusion matrix, with
N×N
matrix, where
N
is the total number of target classes. They are used
for accuracy, precision, recall and
f
1. The accuracy is the proportion of the total number of predictions
that are considered accurate and determined with equation 6shown in Figure 11.
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(a) (b)
Figure 12. Precision Recall. (a, Machine 1) (b, Machine 2)
Accuracy =TP +TN
TP +FP +TN +FN (6)
The recall is the proportion of the total number of TP, where FN are higher than FP and calculated
with equation 7. Precision is the proportion of the predicted TP that was determined as correct if the
concern is FP using equation 8both shown in Figure 12.
Recall =TP
FN +T P (7)
Precision =TP
FP +TP (8)
Figure 13. F1 score.
In cases where precision or recall needs to be adjusted, F-measure of F1 – Score (F) is used as a
harmonic mean of precision and recall with equation 9, iteration of dataset epochs are shown in Figure
13.
F1=2×Precision ×Recall)
Precision +Recall (9)
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(a) (b)
Figure 14. ROC. (a, Machine 1) (b, Machine 2)
A receiver operating characteristic curve (ROC) curve shows the diagnostic ability of the model
as its discrimination threshold is varied using TP Rate (TPR) and FP Rate (FPR) shown in Figure 14
using Formulas 10 and 11.
TPR =TP
TP +F N (10)
FPR =FP
FP +T N (11)
In clustering, Mutual Information is a measure of similarity between two labels of the same data.
Where
|Ui|
is the number of samples in cluster
Ui
and
|Vi|
is the number of samples in cluster
Vi
. To
measure loss in regression, Mean Absolute Error (MAE) can be used to determine the sum of the
absolute mean and Mean Squared Error (MSE) to determine the mean or normal difference to provide
a gross idea of the magnitude of the error with the equation. While Entropy determines the measure of
uncertainty about the source of data. Where
a
= Proportion of positive examples and
b
= Proportion of
negative examples. For GAN, to capture the difference between two distributions in loss functions, the
Minimax loss function [79] and Wasserstein loss function [82] are used.
7. Risk and Limitation of Artificial Intelligence
The utilisation of AI is picking up momentum, however, there is still a lack of responsible AI
frameworks and regulations to enforce security, privacy and ethical practices. Therefore, the existing
methods need to be evaluated, and new strategies and standards developed [
83
]. For ML, high-quality
data is required for training, but due to high costs, existing data and pre-trained models are often
obtained from external sources, exposing AI systems to new security risks [
84
]. An AI system may
produce false results if malicious training data is inserted through a backdoor attack. Mislabelling data
might lead to miss-classification like wrongly tagging stop signs in autonomous driving systems [
5
,
19
]
and quarantining files in attack detection. Recently, Zoom URLs were mislabelled as malicious by
Microsoft’s EDR, leading to a huge volume of FP alerts, resource wastage and meetings cancellations.
The impact of AI on cyber security is both positive and negative. Mishandling of AI can lead to
negative social impact and system compromise. There are also issues of inherent weaknesses in the ML
algorithms and their dependencies, poor deployment and maintenance, abuse, espionage and bias.
7.1. Limitations and Poor Implementation
AI comes with limitations and dependencies, if implemented poorly, security teams may end
up making poor decisions. ML is probabilistic, DL algorithms do not have domain knowledge or
understand network topologies or business logic [
85
]. Models may produce results that violate the
fundamental constraints of the organisation environment, hence, constraints should be added to
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algorithms to align with the rules and logic. The AI model is unable to intuitively explain the rationale
behind its decision on pattern finding or anomaly detection [
7
]. Explainable AI (XAI) techniques can be
applied to describe an AI model, its decisions, expected impact and potential biases [
86
]. This defines
model correctness, fairness, transparency, and results in decision-making. XAI is crucial in building
trust and confidence during AI deployment.
Figure 15. Ensemble learning.
There is always a degree of error associated with the probability such as FP and FN errors, bias-
variance and auto correlation [
87
]. ML has a dependency on large datasets and labelled data, in cases
where the train data is not enough, the following methods can be adopted:
•
Model Complexity: Build a simple model with fewer parameters, less susceptible to over-fitting
algorithms. Using Ensemble learning to combine several learners to improve prediction [
88
], as
shown in Figure 15.
•
Transfer Learning: Use a pre-built model fine-tuned on small datasets, as shown in Figure 16. It
reuses a trained NN and model weights to solve similar problems [89].
•
Data Augmentation: Make slight improvements to get new images with pre-existing samples and
increase the number of training samples with scaling, rotation, and affine transforms [90].
•
Synthetic Data: Artificially generate samples which mimic the real-world data, when you have a
good understanding of the features, but it may induce bias in existing data [76].
Figure 16. Transfer Learning.
7.2. Attacks against Artificial Intelligence
The creation of an AI has led to new attack surface, exploitation and abuse [
5
,
6
,
23
,
29
,
33
,
91
]. There
are also new top 10 most critical vulnerabilities for Large Language Mode (LLM) [
92
]. AI is still
susceptible to traditional attacks such as buffer overflow, and DoS. Some of AI attacks include:
•
Adversarial attack: The attacker manipulates input data to fool an AI system into producing
incorrect results, by adding noise to an image or object to make it unrecognizable.
•
Evasion attack: The attacker exploits AI vulnerabilities to evade detection, by modifying a
malware file to evade antivirus software.
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•
Poisoning attack: The attacker introduces malicious data into an AI system to corrupt its training
or compromise its accuracy. By injecting fake data into a dataset used for training a model to bias
it towards making incorrect predictions. An attacker can also insert false data into a training set
for a spam filter to cause it to misclassify emails as spam.
•
Model stealing attack: It involves stealing a trained model to replicate it, extracting a model’s
parameters or training data through reverse engineering.
•
Model inversion attack: It involves extracting sensitive information from an AI system by invert-
ing its trained model to reconstruct a patient’s medical history from a hospital’s system.
•
Backdoor attack: An attacker adds a hidden trigger to an AI system that can be activated to cause
it to behave maliciously. The model might be trained to recognize a pattern input, such as a
specific phrase or image, and then program it to perform a specific action when that input is
detected.
•
DoS attack: The attacker sends numerous inputs to cause an AI system to crash or malfunction,
such as flooding a chatbot with requests, exceeding its computational and memory resource, and
rendering it unavailable for legitimate users.
These attacks are performed against CIA of the AI system during training, testing and in produc-
tion. The attack against confidentiality aims to uncover the details of the algorithms [
93
]. An attack can
be initiate by inferring the model’s training attributes, the data and algorithms [
94
]. Attack on integrity
is aimed at altering the trustworthiness of AI’s capability [
95
]. An attack can change the behaviour of
the model in classifying the malicious and genuine users such that it fails to classify the users correctly.
To amend this change, the model has to be retrained with clean and trustworthy data. An attack on
the availability makes the targeted model useless and unavailable. The TA can take control of the
model and make it perform a completely different task than it was designed to for, using adversarial
reprogramming or induce response action such as temporary shut-down and model recalibration [
96
].
7.2.1. Misuse
AI is empowering different industries by enhancing their capability, however, cybercriminals
are also utilising AI to carry out attacks with greater speed, scope and stealth than ever before. TAs
are bypassing control and utilising the data and API offered by genuine tools to test undetectable
malware using ML [
97
]. ML enables attackers to be extremely precise and launch an attack where
there is the highest likelihood of success, such as identifying and targeting users through online and
offline footprints. The TAs are operationalising AI models to automatically build and send targeted
phishing emails with specific context unique to each recipient, using genuine cloud vendor platforms.
They are utilising generative AI to write text messages, phishing emails and social media posts that
can pass filtering tools or convince users.
Attackers’ tasks are now performed by AI models, running autonomously and making highly
intelligent decisions [
98
]. They can also generate synthetic data to impersonate a person or create
false information. A criminal recently impersonated an executive of an energy company, by using
a synthetic voice, then persuaded an employee to transfer about a quarter of a million dollars to a
fraudulent account [99].
7.2.2. Limitations
There are some limitations, it requires a lot of time and money on resources like computing
power, memory, and data to build and maintain AI systems. Different datasets of malicious codes and
anomalies are required. Some organisations just don’t have the resources and time to obtain all the
accurate datasets [
12
]. Attackers are learning from AI tools to develop more advanced attacks. They
are utilising neural fuzzing to leverage AI to quickly test large amounts of random inputs [
100
], and
learning about the weaknesses of systems by gathering information with the power of NNs.
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7.3. Deployment
The limited knowledge of AI has led to deployment problems, leaving organisations more
vulnerable to threats. Certain guiding principles should be applied while deploying AI to ensure
support, security and capabilities availability [
101
]. This improves the effectiveness, efficiency, and
competitiveness, it should go through a responsible AI framework and planning process together with
the current organisational framework to set realistic expectations for AI projects. Some of the guiding
principles are as follows [102]:
•
Competency: An organisation must have the willingness, resources, and skill set to build home-
grown custom AI applications or a vendor with proven experience in implementing AI-based
solutions must be used.
•
Data readiness: AI models rely on the quantity and quality of data. But it might be in multiple
formats, different places or managed by different custodians. Therefore, the use of the data
inventory, to assess the difficulty of availability, ingesting, cleaning, and harmonizing the data is
required.
•
Experimentation: Implementation is complex and challenging, it requires adoption, fine-tuning
and maintenance even with already-built solutions. Experimenting is expected using use cases,
learning, and iterating until a successful model is developed and deployed.
•
Measurement: An AI system has to be evaluated for its performance and security using a
measurement framework. Collect Data must be collected to measure performance, confidence
and for metrics.
•
Feedback loops: Systems are retrained and evaluated with new data. It is best practice to plan
and build a feedback loop cycle for the model to relearn, and fine-tune it to improve accuracy
and efficiency. Workflow should be developed and data pipelines automated to constantly get
feedback on how the AI system is performing using RL [66].
•
Education: Educating the team on technology’s operability is instrumental in having a successful
AI deployment and it will improve efficiency and confidence. The use of AI can help the team
grow, develop new skills and accelerate productivity.
7.4. Product Evaluation
Evaluation is fundamental when acquiring or developing an AI system. Different products are
being advertised as AI-enabled with capabilities to detect and prevent attacks, automate tasks, and
predict patterns. However, these claims have to be evaluated and identified for scoping and tailoring
purposes to fit the organisation’s objectives [
103
]. It is vital to validate processes for model training,
applicability, integration, proof of concept, acquisition, support model, reputation, affordability, and
security to support practical and reasoned decisions [104].
The documentation of the product’s trained models process, data, duration, accountability, and
measures for labelled data unavailability should be provided. It should state if the model has other
capabilities, the source of training data, who will be training the model and managing feedback loops,
and a time frame from installation to actionable insights should be provided. A good demonstration
of an AI product does not guarantee a successful integration in the environment, a proof of concept
should be developed with enterprise data and in its environment. Any anticipated challenges should
be acknowledged, and supported. A measurement framework should be developed to get meaningful
metrics in the ML pipeline. An automated workflow should be developed to orchestrate the testing
and deploying of models using a standardised process.
It is important to understand whether AI capabilities were in built or acquired, add-on module or
part of the underlying product and the level of integration. The security capabilities and features of the
product must be evaluated, including data privacy preservation. There should be a clear agreement
on the ownership of the data to align with privacy compliance and evaluate vendors’ approach and
measures to protect AI system.
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8. Conclusion
The fields of AI and cyber security are evolving rapidly and can be utilised symbiotically to
improve global security. Leveraging AI can yield benefits in defensive security, but also empower
TAs. This paper discussed the discipline of AI, security objectives and applicability of AI in the field
of cyber security. It presented use cases of ML to solve specific problems and developed a predictive
MADS model to demonstrate an AI-enabled detection approach. With proper consideration and
preparation, AI can be beneficial to organisations in enhancing security, increasing efficiency and
productivity. Overall, AI can improve security operations, vulnerability management, and security
posture, accelerate detection and response, and reduce duplication of processes and human fatigue.
However, it can also increase vulnerabilities, attacks, violation of privacy and bias. AI can also be
utilised by TAs to initiate sophisticated and stealth attacks. The paper recommended best practices,
deployment operation principles and evaluation processes to enable visibility, explainability, attack
surface reduction and responsible AI. Future work will focus on improving the MADS, developing
models other use cases and developing a responsible AI evaluation framework for better accountability,
transparency, fairness and interpretability.
Author Contributions: Conceptualization, E.K.K.E.; methodology, E.K.K.E.; validation, E.K.K.E.; formal analysis,
E.K.K.E; investigation, E.K.K.E.; writing—original draft preparation, E.K.K.E.; writing—review and editing,
E.K.K.E.
Funding: This research received no external funding.
Data Availability Statement: Not Applicable.
Conflicts of Interest: The authors declare no conflicts of interest.
.
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