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AI-Driven Threat Detection and Response Systems for Secure

National Infrastructure Networks: A Comprehensive Review

Akinkunle Akinloye.,

Sunday Anwansedo and

Oladayo Tosin Akinwande

MTN Nigeria, Ltd.

Southern University and A & M College, United State.

Veritas University, Bwari Abuja, Nigeria.

Corresponding Author

DOI: https://doi.org/10.51583/IJLTEMAS.2024.130710

Received: 01 June 2024; Revised: 21 June 2024; Accepted: 01 July 2024; Published: 05 August 2024

Abstract: Due to the increased complexity and damage of cyberattacks in this digital age, the security of national infrastructure

networks has become a vital concern. However, a possible approach to improve the cybersecurity of these crucial networks is to

incorporate artificial intelligence (AI) into threat detection and response systems; to rapidly evaluate large data sets, identify

anomalies, and automate countermeasures to lessen the effects of cyberattacks. The impact, implementation and approaches for

anomaly detection and response automation of AI-powered solutions for safeguarding national infrastructure are examined in this

paper. Understanding how AI technologies are used to automate threat detection and response, reviewing the operational

usefulness of AI in enhancing cybersecurity measures and evaluating the deployment of these systems in critical infrastructure

settings were also examined. The study revealed that the speed and accuracy of threat detection and response are greatly increased

by AI-powered systems. The automation capacity of AI can potentially reduce the need for human analysts, while also providing

faster threat mitigation. Additionally, the usefulness of AI across sectors indicates its practicality in situations and how it may

adapt in response to new threats. In conclusion, AI-driven threat detection and response systems are an important development in

national infrastructure network cybersecurity. Therefore, by improving the capacity to recognize and address cyber-attacks these

technologies can ultimately increase the overall resilience of national infrastructures.

Keywords: Artificial Intelligence (AI), Machine Learning, Threat Detection, Response Systems, Network Security

I. Introduction

The reliance on digital infrastructure, and safeguarding national infrastructure networks in modern times is of utmost importance.

This growing digitization and interconnectedness of critical infrastructure systems, have increased accessibility and efficiency

while also creating new vulnerabilities (Daniel & Segun, 2024). The potential impact of cyber threats has also become a major

concern since critical sectors including energy, transportation, telecommunications, and healthcare are becoming more and more

dependent on networked networks (Reddy & Reddy 2014).

The security intelligence report by Reed (2023), revealed that the global high-impact attacks on critical infrastructure increased

by 140% in 2022, with over 150 occurrences impacting industrial operations. Therefore, effective security systems that can

rapidly react to changing threats are essential, as evidenced by cyber threats that are capable of launching persistent and focused

attacks. Artificial intelligence (AI) integration into threat detection and response systems has great potential in this regard (Kaur

et al., 2023). There are a lot of hazards associated with cyberattacks on national infrastructure networks, from possible economic

and societal repercussions to service interruptions and data breaches. Conventional security strategies, which depend on

signature-based detection techniques and rule-based systems, may no longer meet the requirements of the evolving digital

ecosystem marked by threats (Ghadge, 2024; Liu et al., 2021).

Cybersecurity protects information and communication systems that are accessible over the internet from threats and harmful

attacks (Li & Liu, 2021). In recent times, the Fourth Industrial Revolution and the Industrial Internet of Things (IIoT) have

expanded the scope of cybersecurity, becoming multidimensional, and encompassing infrastructure, cloud, and information

security in addition to network and application security (Yu & Guo, 2019). Therefore, cybersecurity comprises system security; a

variety of interconnected technologies and components in cyberspace. Cybersecurity in an organizational setting entails

concurrently safeguarding all pertinent cyberspace dimensions (Li & Liu, 2021).

The most common kind of AI in organizational cyber security, machine learning (ML), has emerged due to developments in data

science and computer science (Scott & Kyobe, 2021). ML is the ability of a machine to learn and adapt through experience It is

considered a subset of AI that concentrates on the application of specific system types that can learn from past data to find

patterns and make decisions autonomously (Wazid et al., 2022). Numerous ML applications in cyberspace, such as threat

intelligence, anomaly detection, and task automation for cybersecurity, can be facilitated by the enormous volumes of data that

organizations generate (Huang & Rust, 2018). Generally, adopting threat detection and response systems driven by AI is not

without its challenges (Rizvi, 2023). There are many obstacles to overcome, including issues with data availability and quality,

model interpretability, algorithm bias, and adversarial attacks. Furthermore, issues with dependability, privacy, and other

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unforeseen repercussions arise when AI is included in critical infrastructure (Paras, 2023). Understanding the technological,

organizational, and regulatory aspects of AI-driven cybersecurity solutions is necessary to holistically address these problems.

The purpose of this paper is to provide an in-depth review of AI-powered methods for protecting national infrastructure networks

from online attacks. This review explores the impact of AI on cybersecurity within national infrastructure, AI approaches for

threat detection and response automation, AI systems, and relevant implementation in critical infrastructure settings including

operational assessments demonstrating the effectiveness of AI as well as potential applications of AI-driven security technologies

in protecting critical infrastructure.

II. Impact of AI on National Infrastructure Cybersecurity

Although the idea of AI in cybersecurity is not entirely novel, it has recently acquired prominence as a result of the complexity

and frequency of rising cyberattacks, as well as the volume and diversity of data and devices that are to be safeguarded. The

invention and adoption of other cutting-edge technologies, such as cloud, quantum, edge computing, blockchain, IoT, optical

networks and 5G, which provide new security concerns as well as opportunities, also have an impact on AI in cybersecurity

(Singh S. K. et al., 2021). Consequently, AI in cybersecurity is a dynamic and developing issue requiring continuous research and

collaboration. The widely recognized cybersecurity framework by NIST is to assist in comprehending the several categories

required to safeguard, identify, respond to, and repel cyberattacks (Barrett, 2018). The fundamentals of the NIST cybersecurity

framework outline how to strengthen an organization's cybersecurity (Figure 1).

Figure 1. Cybersecurity framework by NIST

The preliminary phases of AI in cybersecurity were centered on anomaly detection and intrusion prevention systems. This is

represented by the 1990s–2000s. The ML era began in the 2010s until the present times (Audibert et al., 2022). The emergence of

ML algorithms revolutionized AI-powered cybersecurity solutions by enabling more sophisticated threat identification, analysis,

and prediction (Mohamed, 2023). The 2020s and beyond saw the development of deep learning techniques, which further

enhanced AI capabilities and allowed for more complex and nuanced threat analysis, such as identifying hidden patterns in

cyberattacks and identifying zero-day attacks (Aslan et al., 2023).

Large volumes of data from many sources are analyzed by AI algorithms, allowing for the proactive detection of even the most

subtle dangers. Processes related to incident response, including containment, remediation, and recovery, can be automated by AI

systems, greatly speeding up reaction times (Chahal, 2023). AI models forecast potential cyberattacks by examining past data and

present trends. This allows for proactive mitigation techniques and resource allocation. AI technologies can improve the efficacy

and efficiency of security solutions by tailoring them to particular threats and situations. AI can help close the talent gap in

cybersecurity by automating repetitive processes and enhancing human knowledge (Tonhauser & Ristvej, 2023).

Since AI enables machines to carry out tasks including learning, thinking, and decision-making that traditionally need human

intelligence, AI has the potential to be an essential tool in safeguarding the national infrastructure of a country, which includes

things like electricity, water, transportation, and communication systems and assets that are necessary for a country to function

and be secure (Ghosh et al., 2018). By utilizing data analytics, ML, and natural language processing to find patterns, anomalies,

and vulnerabilities that can point to malicious activity or possible threats, AI can assist in the detection and prevention of

cyberattacks on critical infrastructure. AI may also improve and automate the handling of security incident response and

remediation through the use of preset workflows, policies, and rules. With the use of sensors, computer vision, and deep learning,

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AI can monitor and manage both the digital and physical components of infrastructure, including traffic lights, power grids, and

pipelines. This can assist optimize the resilience and performance of essential infrastructure (McMillan & Varga, 2022). AI can

also be used to anticipate and lessen the effects of man-made and natural disasters, like fires, floods, and earthquakes, on

infrastructure (Şimşek et al., 2023). AI can create and revolutionize infrastructure by employing image recognition, pattern

recognition, and data mining to identify and take advantage of new opportunities and problems for the infrastructure, such as

renewable energy sources, smart cities, and IoT devices, AI may help innovate and alter essential infrastructure.

AI presents a variety of dangers and problems, including those related to data quality, privacy, security, ethics, and governance,

but it can also provide several advantages and chances for enhancing the sustainability, efficiency, and security of the national

infrastructure (Ade-Ibijola & Okonkwo, 2023). Therefore, it is critical to ensure AI is developed and utilized in a way that

supports infrastructure norms, human rights, and transparency while also being inclusive Modern society is anchored by its

national infrastructure, which includes critical systems like water treatment plants, transportation networks, energy grids, and

communication systems (Robbins & van Wynsberghe, 2022). To ensure public safety, economic growth, and national security,

this infrastructure must be protected from cyberattacks and other threats. Artificial intelligence (AI) is showing great promise in

several areas and is becoming a potent instrument for bolstering the security and resilience of the country's infrastructure.

Large volumes of data from sensors, cameras, and network traffic are analyzed by AI algorithms, which then detect anomalies

and possible risks that would go undetected by humans. By recognizing tiny patterns that point to malicious behaviour and

detecting zero-day attacks, ML models can learn from and adapt to evolving threats (Thwaini, 2022). Threat intelligence

solutions driven by AI gather and evaluate threat data from many sources, giving digital security professionals a thorough

situational awareness. AI makes it possible to perform preventative maintenance and save downtime by anticipating potential

infrastructure risks and equipment problems before they happen. AI-driven risk assessment models identify locations most

vulnerable to interruptions by analyzing a variety of variables, including traffic patterns, weather, and historical data (Gkioka et

al., 2024; Ghaffarian et al., 2023). Resource allocation and reaction planning are made possible by predictive analytics, which

also helps to reduce risks and potential harm from incidents (Aven, 2016).

Yaacoub et al., (2021) revealed that aspects of incident response that can be automated by AI systems include fast resuming

operations, containing threats, and isolating impacted systems. Security teams may reduce disruption during cyberattacks,

optimize resource allocation, and prioritize responses with the aid of AI-powered decision support technologies. Infrastructure

that is capable of self-healing can automatically identify and fix small problems, decreasing the need for human intervention and

enhancing resilience. Intrusion detection systems with AI capabilities can recognize and stop illegal access attempts, shielding

vital systems from online threats (Markevych & Dawson, 2023). Algorithms for behavioural analysis can recognize possible

insider threats and identify unusual user behaviour. Access control to critical infrastructure components can be made more

effective and safer with the use of AI-based authentication and authorization systems.

III. AI Approaches for Anomaly Detection and Response Automation

Maintaining an effective defense against cyber threats requires being able to recognize potential vulnerabilities in cyber security.

AI enables organizations access to advanced features that extend beyond conventional approaches, enabling them to strengthen

their security (Jada & Mayayise, 2023).

Anomaly Detection

AI systems are highly effective at identifying anomalies in behaviour, which serves as a vital barrier against cyber-attacks. The

foundation of this strategy is the creation of baselines, a dynamic process whereby AI continuously picks up knowledge and

keeps an eye on the intricate web of user and system activity in a cloud environment (Samariya & Thakkar, 2021). It is necessary

to properly construct baselines that include typical behaviour inside the cloud ecosystem so as to influence the anomalous

capabilities of AI. AI can identify common patterns and interactions between individuals, systems, and applications through

ongoing observation and learning. Therefore, comprehension enables prompt identification of deviations that can indicate

possible security risks.

Moreover, ongoing analysis of user behaviours, network activity, and system processes enables AI systems to improve and

modify their conception of normalcy, guaranteeing that the baseline continues to be applicable even when user patterns and

system configurations change (Alanazi & Aljuhani, 2023). AI gets proficient at swiftly spotting anomalies, or the unanticipated

departures from acquired norms, once the baselines are set. However, anomalies might manifest themselves in a variety of ways,

such as irregular data transfers or strange access patterns. The speed at which AI-driven anomaly detection operates is crucial

because it allows quick identification of possible dangers, such as zero-day assaults, that would have been impossible to detect

using more conventional methods. Consequently, through learning and adjusting to typical behaviour, AI systems can identify

deviations that correspond with the characteristics of zero-day attacks. These deviations may include anomalous network traffic,

unexpected data access, or aberrant system behaviour, all of which can serve as warning signs of impending danger.

Response Automation

According to Tatineni (2023), AI expedites recovery times and minimizes damage by streamlining incident response procedures.

AI can accomplish this considering that it can recognize and react to threats rapidly, therefore making this component of security

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automation and incident response more effective and enabling smooth, advanced threat detection and response in cloud security.

In addition, the rapid threat detection and reaction of AI reduces the effect of security issues without requiring human

participation. AI, for instance, may swiftly and effectively respond to new threats by automatically quarantining infected devices

or undoing modifications initiated by cyber criminals.

Furthermore, security automation also includes a wide range of routine and repetitive security duties that are easy for humans to

overlook. These include setting up firewalls, running malware scans, reacting to notifications, fixing vulnerabilities, and changing

passwords (Settanni, 2022). The cyber security teams may concentrate on higher-value duties like threat hunting, constant

monitoring, and improving the overall security status when these security operations are automated using AI and relieved of

routine tasks. AI-driven automation relieves the teams of these tedious activities, which speeds up response times and lowers the

chance of mistakes, resulting in a more flexible and effective security framework and promoting ongoing security improvement.

IV. AI Implementation and Effectiveness in Critical Infrastructure Systems

Each of the four infrastructure sectors: transportation, water, energy, communications, military, and finance utilize AI

distinctively. The implementation, application, and effectiveness of AI techniques in each sector are covered in this section.

Transportation

Abduljabbar et al., (2019) revealed that the most varied range of tasks to which AI has been applied has been found in the

transport sector. When considering transport networks as a whole, several ontology-based knowledge representation systems have

been proposed (Bouhana et al., 2015). Additionally, a variety of ML techniques have been useful in recent research on how the

public interacts with transport systems from a behavioural perspective, including the selection of transport modes (Koushik et al.,

2020). While it is acknowledged that traffic flow and accident prediction can be used in several urban transportation systems

(Doğan and Akgüngör, 2011, Zhang et al., 2020), the majority of the remaining research in this field has concentrated on

individual transportation modes.

Concerning the use of road vehicles, various ML techniques have been used for navigational tools (Veres & Moussa, 2020),

traffic (Jiang & Zhang, 2019), and accident forecasting (Ren et al., 2018). Comparable instruments have also been employed in

destination prediction for taxi services (Veres and Moussa, 2019) and demand prediction (Yao et al., 2018). While Šegvić et al.,

(2010) proposed computer vision-based techniques for traffic infrastructure monitoring, other researchers have attempted to use

AI in the identification and mapping of road networks (Ekpenyong et al., 2009). Deep learning techniques are expected to be

important in the creation of an intelligent transport network, with Convolutional Neural Networks (CNNs) being used in object

detection, localization, and classification for a variety of applications. In-vehicle and roadside sensors have the potential to

provide more data on road networks than ever before (Sirohi et al., 2020). In recent years, there has been a great deal of interest in

the development of self-driving cars. Computer vision, machine and deep learning, automated reasoning, and other methods have

all been applied to this mostly robotics-based challenge (Ma et al., 2020). AI has been used in transportation in areas other than

roadways. Even while robotics, particularly unmanned aerial vehicles (UAVs), show great promise for railway asset monitoring,

many still rely heavily on human contact (Flammini et al., 2016). However, deep learning technologies have shown themselves to

be useful for diagnosing faults in high-speed rail, which is predicted to become a more common means of transportation (Yin and

Zhao, 2016). Individual research by Mendes-Moreira et al., (2015) on bus networks has mostly focused on scheduling challenges,

whereas the majority of other work in public transport has primarily focused on traffic flows or choice of transportation method

(Koushik et al., 2020).

Military

The study by Bhardwaj (2023) revealed that projections regarding the use of AI in military applications, and several important

advanced military technologies will either be redefined or defined over the coming years. Intelligent AI solutions will mostly

arise from the combination of knowledge-focused and analytical skills. The AI solutions will then be connected to fully utilize the

advantages of blockchain technology concerning data integrity and to influence the network of physical and virtual domains,

which will comprise sensors, organizations, people, and autonomous agents. In line with predictions made by the NATO Science

and Technology Organization (2020), this comprises digitally merging the physical, informational, and human fields to support

new disruptive effects; disperse over a sizable region and utilize large-scale, decentralized sensor networks, storage, and

processing.

The uses of AI such as chatbots, automated drones, virtual assistants, facial recognition, cognitive automation, fraud detection,

autonomous vehicles, and predictive analytics applications have a common feature. Figure 2 illustrates how, despite the wide

range of applications, experts who have developed numerous AI projects are aware that each AI use case fits into one or more of

these seven categories. Goal-driven systems, autonomous systems, conversational/human interfaces, hyper personalization,

predictive analytics, and decision support are the seven patterns of artificial intelligence. These seven AI patterns have

transformed military operations in the last few years by bringing new capabilities and uses for activities including conversational

interactions, decision support, and object recognition. Let's examine a small portion of the seven AI patterns in the military,

emphasizing current developments and academic research in each field.

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Figure 2. The seven AI patterns (Rashid A. B. et al., 2023)

On battlefields, AI is becoming more common. Similar to corporations and sectors, the military is gradually beginning to focus

more on AI in its advancement and development. Military systems with AI capabilities can handle massive amounts of data faster

than traditional systems. Additionally, through natural computation and decision-making skills, AI improves the self-actuation,

self-regulation, and self-control of flying systems. Figure 3, shows the AI capabilities important to military operations for

simplicity and their applications in defence sectors. Most military applications involve AI, and growing military support for

innovative and advanced AI technologies is anticipated to increase the demand for AI-driven systems in the military (Taddeo et

al., 2021).

Figure 3. AI applications in the defence sector (Rashid A. B. et al., 2023)

Finance

Integration of AI in the financial market has gained popularity, due to the potential to revolutionize the financial industry (Maple

et al., 2023). The advanced computational methods, including ML, natural language processing, and predictive analytics of AI

allow systems to examine large volumes of data, identify trends, and make well-informed decisions without the need for explicit

human programming. AI applications have demonstrated the potential to improve risk management, increase efficiency, and

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increase accessibility to financial services in the context of financial markets, especially in emerging economies where

conventional infrastructures may be lacking (Ochuba et al., 2024).

Furthermore, AI plays a role in expanding financial services accessibility, especially in developing nations. AI-driven solutions

are empowering people and communities, promoting economic growth, and propelling social development through democratizing

access, personalizing guidance, and increasing inclusiveness. The ability of AI to solve accessibility issues and spur innovation in

the financial services sector will only increase as these technologies develop and become more advanced (Kang et al., 2020). This

will completely transform the sector and open up new avenues for financial inclusion and empowerment.

Water

Water networks have used AI techniques for anything from early water treatment to distribution and customer-related issues. On

the supply side, a large portion of research has focused on pollutant removal (Fan et al., 2018) and water quality, in both potable

and wastewater treatment (Granata et al., 2017, Li et al., 2021). Al Aani et al. (2019) have reported that ML techniques have been

applied in the desalination process, with potential consequences for the design of water plants. From the viewpoint of end-users, a

variety of machine learning techniques, such as Artificial Neural Networks (ANNs), Random Forests (RFs), Support Vector

Machines (SVMs), regression trees, and Deep Belief Networks (DBNs), have been applied to price forecasting (Xu et al., 2019)

and water demand forecasting (Antunes et al., 2018) across a range of geographic scales.

Energy

AI tools have been widely used in the energy sector for demand forecasting, particularly at the residential and building levels

(Mocanu et al., 2016, Ahmad et al., 2014, Mat Daut et al., 2017). Demand-side management and price forecasting are two other

uses (Macedo et al., 2015; Ghoddusi et al., 2019). In this industry, facilitating the reduction of energy use is becoming more and

more important. In general, a variety of techniques have been used, from efficiency-centered ontologies to natural language

creation of consumer advice reports (Tomic et al., 2010; Conde-Clemente et al., 2018). The majority of the remaining energy

sector activity revolves around generating systems, with many of the most advanced applications relating to infrastructure for

renewable energy sources. Although the oil, gas, and nuclear industries can benefit greatly from robotics, the current generation

of robots has limited autonomy (Shukla and Karki, 2016). In recent times, AI in renewable energy systems for supply forecasting,

with ANN techniques widely used in meteorological forecasting (Suganthi et al., 2015), and in solar tracking (Carballo et al.,

2019).

Communication Networks

According to Wang et al., (2020), the efficiency of wireless networks in the future is thought to be dependent on machine learning

techniques A variety of networks such as cellular or wireless, 5G, optical, software-defined networks (SDNs) and cloud have

been discussed by Li et al., (2017), optical networks, software-defined networks and the cloud (Mata et al., 2018; Gulenko et al.,

2016).

Generally, transmission quality and user experience are critical therefore, Casas et al., (2017) and Mata et al., (2018) investigated

the assessment of customer experience and network quality, which can be influenced by characteristics such as latency, loss rate,

and picture or video definition using relevant ML techniques. However, security remains crucial, particularly with wireless and

SDN telecommunications (Lv et al., 2021). Therefore, ML techniques have been applied to the detection of anomalies,

identification of intrusion attacks, and choosing suitable responses.

V. Limitations and Ethical Consideration

There are various ethical concerns with the use of AI in critical infrastructure. Inadvertently maintaining biases found in training

data can result in unjust or discriminatory outcomes for AI systems (Chen et al., 2023). This is especially problematic for national

infrastructure since skewed judgements can have far-reaching effects. For example, an AI system may result in unfair scrutiny or

unbalanced resource allocation if it disproportionately labels particular locations or demographics as high risk based on biased

data.

Implementing AI-driven systems requires a large infrastructural investment. To handle and analyze data in real time, these

systems need sophisticated networking infrastructure, large amounts of storage, and high-performance computing resources.

Several organizations may find these standards prohibitively expensive, particularly those in the public sector (Umoga et al.,

2024). Additionally, there are continual logistical and budgetary difficulties in maintaining and updating this infrastructure to stay

up with technology improvements. Partnerships between public and private sector organizations may help remove in removing

these obstacles (Alhosani & Alhashmi, 2024). Public-private partnerships can assist in distributing the cost and utilizing the

private sector's technological know-how. However, implementing cloud-based solutions can offer flexible and scalable

infrastructure, negating the need for significant upfront costs.

Furthermore, due to potential resistance from stakeholders, several parties may object to the installation of AI-driven technologies

in critical infrastructure. Automation may cause workers to dread losing their jobs, but managers and other decision-makers may

have doubts about the security and efficacy of AI systems (Gavaghan et al., 2021). The success and effectiveness of AI systems

as a whole may be impacted by this opposition, which may impede their acceptance and integration. To solve these issues,

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effective change management techniques are crucial (Ross et al., 2023). This entails open communication regarding the

advantages and constraints of AI, offering staff members chances for training and upskilling, and incorporating stakeholders in

the process of development and execution. Building confidence and lowering opposition can also be accomplished by showcasing

the early accomplishments and observable advantages of AI systems.

Conclusion

The use of AI techniques is growing in both capacity and popularity and the incorporation of AI into cybersecurity systems for

national infrastructure networks is a revolutionary advancement in protecting vital assets from a constantly evolving array of

threats. The increased instrumentation and digitalization of infrastructure systems, which provide data for AI tools, is also

expected to drive the growth of AI applications in this domain. By rapidly analyzing large volumes of data and identifying trends

and anomalies that can point to malicious activity, AI improves the capacity to recognize and address cyber threats, especially for

national infrastructure, where prompt threat detection might avert disastrous outages. AI is an essential component of the safety of

critical infrastructure because of its capacity to adapt and learn from novel data, which guarantees cybersecurity measures stay

effective against developing threats.

Security technologies driven by AI can therefore potentially protect national infrastructure. Hence, the resilience and

dependability of cybersecurity measures are to be improved by developments in AI research, including the creation of more

complex algorithms and the integration of AI with other innovative technologies. Furthermore, the increased focus on ethical AI

and transparency in AI decision-making can aid in resolving issues with responsibility and bias, ensuring AI technologies are

applied in a way that preserves the standards of security and trust.

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AUTHORS BIOGRAPHY

1. Akinkunle Akinloye is a Highly accomplished and process-driven professional with over 13 years of experience in

network security, network operations, network design and cloud computing. He is also proficient in performing threat

intelligence and detection, and vulnerability assessments. He has MSc in Network Systems from University of Teessde

2. Sunday Anwansedo has a Masters in Electrical Enginnering with expertise in telecommunications devices and

technology, including the designs and optimization of Systems and components for communication infrastructure.

3. Oladayo Tosin Akinwande obtained his B. Tech and M. Tech in Computer Science from Federal University of

Technology, Minna, Niger State, Nigeria. He is currently a PhD Student of Computer Science, Federal University of

Technology, Minna, Niger State, Nigeria. His current research interests include artificial intelligence, explainable

artificial intelligence, security and privacy issues in artificial intelligence and information and communication security.

He is a member of Nigeria Computer Society (NCS).