doi: 10.62486/latia2025331

REVIEW

Machine learning and AI for security mechanisms: A Systematic Literature Review Using a PRISMA Framework

Aprendizaje Automático e Inteligencia Artificial para Mecanismos de Seguridad: Una Revisión Sistemática de la Literatura Utilizando el Marco PRISMA

Hockings Mambwe¹ *, Petros Chavula^1,2 *, Fredrick Kayusi^3,4 *, Gilbert Lungu⁴ *, Agnes Uwimbabazi^2,5 *

¹World Agroforestry Centre, St Eugene Office Park 39P Lake Road, P.O. Box 50977, Kabulonga, Lusaka, Zambia.

²African Centre of Excellence for Climate-Smart Agriculture and Biodiversity Conservation, Haramaya University, Dire-Dawa, Ethiopia.

³Department of Environmental Sciences, School of Environmental and Earth Sciences, Pwani University, Kilifi, Kenya.

⁴School of Natural Resources Management, Copperbelt University, P.O. Box 21692, Kitwe, Zambia.

⁵Department of Nature Conservation Rwanda Polytechnic-Integrated Polytechnic Regional College of Kitabi, Rwanda, P.O. Box 330 Huye Rwanda.

Cite as: Mambwe H, Chavula P, Kayusi F, Lungu G, Uwimbabazi A. Machine learning and AI for security mechanisms: A Systematic Literature Review Using a PRISMA Framework. LatIA. 2024; 2:331. https://doi.org/10.62486/latia2025331

Submitted: 27-05-2024                   Revised: 17-08-2024                   Accepted: 27-11-2024                 Published: 28-11-2024

Editor: PhD. Rubén González Vallejo

Corresponding author: Petros Chavula *

ABSTRACT

Cyber threats are evolving rapidly, posing significant risks to individuals, organizations, and digital infrastructure. Traditional cybersecurity measures, which rely on predefined rules and static defence mechanisms, struggle to counter emerging threats such as zero-day attacks and advanced persistent threats (APTs). The integration of artificial intelligence (AI) and machine learning (ML) into cybersecurity presents a transformative approach, enhancing threat detection, anomaly identification, and automated response mechanisms. This study systematically reviews the role of ML and AI in cybersecurity defence using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. A comprehensive literature search was conducted across multiple academic databases, identifying and analyzing studies published within the last decade. The review focuses on AI-driven cybersecurity applications, including intrusion detection systems (IDS), malware analysis, and anomaly detection in cloud and IoT environments. Findings indicate that ML models, such as neural networks, support vector machines, and ensemble learning techniques, improve detection accuracy and adaptability to evolving threats. AI-driven automated response systems enhance incident mitigation, reducing reliance on human intervention. However, challenges such as adversarial attacks, data privacy concerns, and computational resource demands persist. The study concludes that AI and ML significantly enhance cybersecurity resilience but require continuous advancements in model robustness, interpretability, and ethical considerations. Future research should focus on refining AI-driven security mechanisms, addressing adversarial vulnerabilities, and improving regulatory frameworks to maximize AI’s potential in cybersecurity.

Keywords: Artificial Intelligence; Machine Learning; PRISMA Framework; Cybersecurity; Security Defense Mechanism; Systematic Literature Review; Intrusion Detection; Malware Analysis; Anomaly Detection.

RESUMEN

Las amenazas cibernéticas evolucionan rápidamente, representando riesgos significativos para individuos, organizaciones e infraestructuras digitales. Las medidas tradicionales de ciberseguridad, que dependen de reglas predefinidas y mecanismos de defensa estáticos, tienen dificultades para contrarrestar amenazas emergentes como los ataques de día cero y las amenazas persistentes avanzadas (APTs). La integración de la inteligencia artificial (IA) y el aprendizaje automático (ML) en la ciberseguridad presenta un enfoque transformador, mejorando la detección de amenazas, la identificación de anomalías y los mecanismos de respuesta automatizada.Este estudio revisa sistemáticamente el papel del ML y la IA en la defensa de la ciberseguridad utilizando el marco de referencia PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). Se realizó una búsqueda exhaustiva de literatura en múltiples bases de datos académicas, identificando y analizando estudios publicados en la última década. La revisión se centra en aplicaciones de ciberseguridad impulsadas por IA, incluyendo sistemas de detección de intrusos (IDS), análisis de malware y detección de anomalías en entornos de nube e IoT. Los hallazgos indican que los modelos de ML, como redes neuronales, máquinas de soporte vectorial y técnicas de aprendizaje en conjunto, mejoran la precisión en la detección y la capacidad de adaptación a amenazas en evolución. Los sistemas de respuesta automatizada basados en IA optimizan la mitigación de incidentes, reduciendo la dependencia de la intervención humana. Sin embargo, persisten desafíos como ataques adversariales, preocupaciones sobre la privacidad de los datos y demandas de recursos computacionales. El estudio concluye que la IA y el ML fortalecen significativamente la resiliencia en ciberseguridad, pero requieren avances continuos en robustez de modelos, interpretabilidad y consideraciones éticas. Las investigaciones futuras deben centrarse en perfeccionar los mecanismos de seguridad basados en IA, abordar vulnerabilidades adversariales y mejorar los marcos regulatorios para maximizar el potencial de la IA en la ciberseguridad.

Palabras clave: Inteligencia Artificial; Aprendizaje Automático; Marco PRISMA; Ciberseguridad; Mecanismo de Defensa en Seguridad; Revisión Sistemática de la Literatura; Detección de Intrusos; Análisis de Malware; Detección de Anomalías.

INTRODUCTION

In today’s digital landscape, cybersecurity has become an essential requirement rather than an option, as cyber threats continue to evolve at an alarming rate. Malicious actors constantly develop sophisticated techniques to exploit vulnerabilities, posing significant risks to individuals, organizations, and society.^(1,2,3) Modern technological advancements, including banking systems, IoT devices, and smart cities, rely heavily on cybersecurity to ensure the integrity and confidentiality of sensitive data.^{(1,2,3,4,5,6)} However, traditional security measures, while effective against known threats, struggle to detect emerging attacks such as zero-day exploits and advanced persistent threats (APTs), which capitalize on system vulnerabilities before they are patched.^{(1,2,3,4,5,6,7,8,9)}

A significant limitation of conventional cybersecurity approaches, such as signature-based detection, is their reliance on predefined patterns, making them ineffective against new and evolving threats.⁽²⁾ Additionally, fragmented security tools and anomaly-based detection systems often produce high false positives, creating inefficiencies in cyber defence mechanisms. This challenge highlights the need for advanced, adaptive, and intelligent cybersecurity solutions.^(1,2,3,4,5)

Recent advancements in artificial intelligence (AI) and machine learning (ML) offer promising solutions to enhance cybersecurity defences. AI-driven systems can analyze vast amounts of data, detect patterns, and identify potential threats in real-time, improving accuracy and reducing false positives.⁽⁵⁾ Moreover, ML algorithms continuously evolve by learning from new threats, making them more resilient to emerging cyber risks. These capabilities position AI and ML as transformative technologies for strengthening cybersecurity frameworks.^{(1,2,3,4,5,6)}

Despite the growing adoption of AI in cybersecurity, there remain critical challenges such as model interpretability, adversarial attacks, and the need for high-quality training data.^{(1,2,3,4,5,6,7,8)} Therefore, this study aims to systematically review the role of AI and ML in cybersecurity, with a focus on intrusion detection, malware analysis, and anomaly detection in IoT and cloud environments. The review will also explore the limitations and challenges associated with AI-driven cybersecurity solutions, providing insights into their effectiveness in mitigating modern cyber threats.

METHOD

This systematic literature review (SLR) rigorously examines the existing body of research on the applications of machine learning (ML) and artificial intelligence (AI) in security defence mechanisms, adhering to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. PRISMA provides a structured and transparent approach for conducting systematic reviews, ensuring reproducibility and credibility in research synthesis. The study employs a multi-stage filtering process to extract, analyze, and synthesize data from scholarly sources published over the last decade (2014-2024), offering insights into trends, methodologies, and advancements in ML- and AI-driven cybersecurity defence mechanisms.

Systematic Selection Process

The selection process was carried out systematically in four key stages to ensure methodological rigour and comprehensive coverage of relevant literature:

1. Identification: A structured and exhaustive search was performed across multiple digital databases to identify relevant studies. The search strategy incorporated a combination of controlled vocabulary (e.g., MeSH terms) and free-text keywords tailored to cybersecurity, AI, and ML applications in defence mechanisms.

2. Screening: Titles and abstracts of retrieved articles were reviewed for relevance. To eliminate irrelevant studies, articles that did not align with the scope of ML- and AI-based security mechanisms were removed at this stage.

3. Eligibility: The full texts of shortlisted studies were thoroughly examined against predefined eligibility criteria to ensure methodological soundness and relevance. Studies with insufficient empirical or experimental contributions were excluded.

4. Inclusion and Exclusion: A final selection was made, including studies that met all relevance and quality criteria. Redundant and duplicate studies were removed using reference management software.

Database and Search Strategy

A comprehensive search was conducted across multiple academic databases, including ACM Digital Library, IEEE Xplore, ScienceDirect, ResearchGate, and Google Scholar. The search strategy integrated Boolean operators and structured search terms to refine the results. The keywords employed included:

• “Machine Learning in Cybersecurity”

• “AI Security Defense Mechanisms”

• “AI-based Intrusion Detection”

• “ML in Threat Detection”

• “Adversarial Machine Learning in Cybersecurity”

Search filters were applied to limit results to peer-reviewed journal articles and conference proceedings published between 2014 and 2024, ensuring the inclusion of state-of-the-art research. Additional manual searches were performed to cross-validate findings and include any seminal works that might have been overlooked by automated queries.^(9,10,11,12)

Inclusion and Exclusion Criteria

To maintain methodological rigor and ensure relevance, studies were assessed based on strict inclusion and exclusion criteria.

Table 1. Inclusion and Exclusion Criteria
Inclusion Criteria	Exclusion Criteria
Peer-reviewed journal articles and conference papers focusing explicitly on ML and AI techniques applied to cybersecurity defence mechanisms.	Non-peer-reviewed articles, preprints, and grey literature.
Studies published between 2014 and 2024 reflect contemporary advancements.	Studies that provide only theoretical recommendations, guidelines, or frameworks without empirical validation.
Comparative studies that evaluate ML- and AI-based cybersecurity defence mechanisms against traditional approaches.	Duplicate studies presenting the same findings in multiple conferences or journals.
Papers categorizing cybersecurity threats, adversarial attacks, and AI-driven countermeasures.	Articles lacking sufficient methodological details or experimental evaluations.
Studies providing empirical analysis and performance metrics on AI-driven security systems.	Papers addressing AI and ML applications in domains outside cybersecurity.

Data Extraction and Analysis

A systematic data extraction framework was employed to ensure consistency and thoroughness throughout the analysis. Each selected article was meticulously examined to identify key attributes, beginning with the study objectives and scope, which involved pinpointing the core research questions and the overall scope of the investigation. The methodological approaches were then assessed, focusing on the machine learning (ML) and artificial intelligence (AI) techniques utilized, such as supervised learning, unsupervised learning, reinforcement learning, or hybrid approaches.

Additionally, the security domain focus of each study was categorized based on its application within cybersecurity, including areas like intrusion detection, anomaly detection, adversarial defence, malware classification, and risk assessment. Performance metrics and evaluation criteria were also extracted, with particular attention to reported values for accuracy, precision, recall, F1-score, and computational efficiency of the proposed models. Finally, the analysis included an examination of the challenges and future directions highlighted in the studies, identifying limitations, unresolved issues, and emerging trends in AI-driven security mechanisms. This comprehensive approach ensured a detailed and structured understanding of the research landscape in this domain.

Data analysis was performed using a comparative synthesis approach, systematically evaluating methodological trends, effectiveness, and key findings across studies. The extracted data were subjected to statistical and qualitative analysis to identify recurring themes, gaps, and advancements in ML- and AI-based security mechanisms.

DEVELOPMENT

Machine Learning Applications in Security Defence

Anomaly Detection

 Anomaly detection is one of the primary applications of machine learning (ML) in cybersecurity. ML models can learn typical patterns of network traffic or user behaviour and detect deviations that may signal potential threats. Common algorithms used in anomaly detection include support vector machines (SVM), neural networks, and clustering techniques like K-means.^(8,9) These models are advantageous as they can automatically adapt to new threats over time, thus providing a dynamic defence mechanism.

Studies indicate that supervised learning approaches such as decision trees and random forests effectively detect anomalies in network traffic where labelled data is available. In contrast, unsupervised methods like autoencoders and K-means clustering enable the detection of previously unknown threats in situations with limited labelled data.^{(10,11,12,13,14)} Sharma and Gupta⁽⁴⁾ demonstrate that supervised learning models provide high accuracy but struggle with detecting new and emerging threats due to their reliance on predefined patterns. Meanwhile, Wang et al.⁽⁶⁾ propose the use of autoencoders and K-means clustering, which facilitate unsupervised anomaly detection and can identify novel threats without requiring extensive labelled data.

Intrusion Detection Systems (IDS)

Intrusion Detection Systems (IDS) identify and respond to unauthorized access or activity within a network. Machine learning models, such as k-nearest neighbours (k-NN) and deep learning models (e.g., convolutional neural networks), have been widely adopted in IDS due to their ability to process and analyze complex data patterns.^{(15,16,17,18)}

In IDS, supervised ML models are often trained using labelled normal and malicious network activity datasets. Studies have shown that hybrid models, combining multiple ML techniques (e.g., combining decision trees with support vector machines), can improve intrusion detection accuracy by leveraging the strengths of individual models. White et al.⁽⁷⁾ advocate for deep learning models like CNNs for IDS due to their ability to analyze complex attack patterns. However, Zhang⁽¹³⁾ warns about the vulnerability of these models to adversarial attacks, where slight perturbations in input data can lead to incorrect classifications. A comparative analysis by Evans⁽¹¹⁾ suggests that reinforcement learning (RL) techniques offer a promising approach to IDS by enabling adaptive responses to detected threats (table 2).

Table 2. Machine Learning Models for IDS and Anomaly Detection
Model	Application	Advantages	Limitations
Support Vector Machines (SVM)	Anomaly Detection, IDS	High accuracy in binary classification, effective in high-dimensional spaces	High computational cost, sensitive to kernel choice, limited scalability
Neural Networks (NN)	Anomaly Detection, IDS	High adaptability, captures complex patterns	Requires significant computational resources, risk of overfitting, susceptible to adversarial attacks
K-Nearest Neighbors (K-NN)	Anomaly Detection	Simple to implement, non-parametric	Computationally expensive during inference, less effective with high-dimensional data
Decision Trees	Anomaly Detection, IDS	Easy to interpret, fast training and inference	Prone to overfitting, lower performance on complex datasets
Random Forests	Anomaly Detection, IDS	Robust, reduces overfitting, handles missing values	High computation and memory requirements
Naive Bayes	Anomaly Detection	Low computational cost, fast training and prediction	Assumes feature independence, lower accuracy with correlated data
Autoencoders	Anomaly Detection, IDS	Suitable for unsupervised anomaly detection	Requires significant training data and resources, sensitive to input distribution changes
Long Short-Term Memory (LSTM)	IDS, Anomaly Detection	Effective for sequential data	Computationally intensive, complex to train
Gradient Boosting (e.g., XGBoost)	Anomaly Detection, IDS	High accuracy, robust to noise	Slower training process, more resource-intensive

Artificial Intelligence for Threat Intelligence and Response

Threat Intelligence with Natural Language Processing (NLP)

Threat intelligence gathers data on potential or ongoing threats to analyze patterns, origins, and methods. Artificial intelligence (AI), specifically natural language processing (NLP), plays a significant role in threat intelligence by automating the analysis of large volumes of unstructured data, such as threat reports, social media posts, and news articles. NLP models extract relevant information, such as threat actors and targeted systems, allowing for more efficient threat intelligence processing.^{(15,16,17,18,19,20,21)}

Recent advances in NLP, including Transformer models like BERT, have improved the accuracy of analyzing complex language patterns to identify emerging threats. However, these models require large amounts of training data, which may present limitations in regions with restricted data access.

AI-Driven Automated Response

AI-driven automated response systems leverage reinforcement learning (RL) to make real-time decisions on threat mitigation. When a threat is detected, RL models can automatically apply countermeasures, such as isolating affected systems, blocking malicious IPs, or alerting administrators.^{(22,23,24,25)} Studies indicate that such systems are particularly effective in environments with high-frequency, repetitive threats (e.g., Distributed Denial of Service (DDoS) attacks). However, a significant challenge is the need for extensive training data to develop accurate RL models, making deployment more difficult in resource-limited settings (table 3).

Research Challenges in ML and AI for Security

Table 3. Challenges in ML and AI for Security
Challenge	Description	Impact	Mitigation Strategies
Adversarial Attacks	Inputs crafted by attackers to deceive ML models	Reduces model accuracy, risk of breaches	Adversarial training, robust model architectures, input validation
Data Privacy	Handling sensitive data securely	Risks privacy violations, compliance issues	Privacy-preserving ML techniques (e.g., federated learning, differential privacy)
Data Quality	ML models require high-quality, labeled data	This leads to biased or inaccurate models	Data cleaning, augmentation, and robust validation processes
Model Interpretability	Difficulty understanding complex models	Limits trust and transparency	Use interpretable models (e.g., decision trees), posthoc analysis (e.g., SHAP, LIME)
Scalability	Difficulty applying models to large datasets	Reduces efficiency	Scalable architectures, distributed ML
Latency and Real-Time Processing	Delays in threat detection and response	Increased risk during active threats	Use of lightweight models, efficient algorithms
Evolving Threats	Rapid changes in attack methods	Decreases detection accuracy over time	Regular model updates, online learning
False Positives/Negatives	Incorrect threat detections	Decreases operational efficiency	Balanced training datasets, ensemble methods

Potential for ML/AI in Enhancing Cybersecurity in Zambia

Zambia’s evolving cybersecurity landscape faces growing threats but has limited resources and expertise in ML/AI. Despite challenges, Zambia’s commitment to digital transformation creates a promising environment for adopting innovative defence mechanisms. AI and ML implementations could address challenges such as rapid threat detection in Zambia’s critical sectors.⁽²⁴⁾ Collaborative efforts with international organizations and capacity-building initiatives could support ML and AI applications in Zambia (table 4).⁽²⁵⁾

Included Studies in This Research

Table 4. Relevant Studies Included in This Research
Author	Year	Study Focus	Key Findings
Sharma & Gupta	2022	Anomaly Detection	High accuracy using feature engineering and ensemble learning
White et al.	2019	Intrusion Detection	Deep neural networks improve IDS adaptability
Evans	2022	Automated Cybersecurity Response	Reinforcement learning enables faster threat mitigation
Zhang	2020	Adversarial Training	Improves model robustness but increases training time
Mwansa	2021	AI in Cybersecurity in Zambia	Highlights regional challenges and solutions
Thomas	2021	Adversarial ML Techniques	Reviews attack strategies and defensive mechanisms

CONCLUSION

This systematic literature review highlights the transformative potential of machine learning (ML) and artificial intelligence (AI) in enhancing cybersecurity mechanisms. By leveraging advanced ML techniques such as neural networks, support vector machines, and reinforcement learning, AI-driven systems significantly improve threat detection, anomaly identification, and automated response capabilities. These technologies excel in addressing evolving cyber threats, including zero-day attacks and advanced persistent threats (APTs), which traditional methods struggle to counter. However, challenges such as adversarial attacks, data privacy concerns, and the need for high-quality training data persist. The study underscores the importance of continuous advancements in model robustness, interpretability, and ethical considerations to maximize AI’s potential in cybersecurity. Future research should focus on refining AI-driven security mechanisms, addressing adversarial vulnerabilities, and improving regulatory frameworks. For regions like Zambia, where cybersecurity resources are limited, adopting AI and ML presents a promising opportunity to strengthen digital defences, provided there is adequate international collaboration and capacity-building support. Overall, AI and ML are pivotal in building resilient cybersecurity frameworks, but their successful implementation requires ongoing innovation and strategic planning.

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FINANCING

The authors did not receive financing for the development of this research.

CONFLICT OF INTEREST

The authors declare that there is no conflict of interest.

AUTHORSHIP CONTRIBUTION

Conceptualization: Fredrick Kayusi, Petros Chavula, Hockings Mambwe.

Data curation: Fredrick Kayusi, Petros Chavula, Gilbert Lungu.

Formal analysis: Petros Chavula, Hockings Mambwe.

Research: Fredrick Kayusi, Petros Chavula, Agnes Uwimbabazi.

Methodology: Hockings Mambwe.

Software: Fredrick Kayusi, Petros Chavula, Srinivas Kasulla.

Validation: Gilbert Lungu, Agnes Uwimbabazi, Hockings Mambwe.

Display: Fredrick Kayusi, Petros Chavula.

Drafting - original draft: Fredrick Kayusi, Petros Chavula.

Writing - proofreading and editing: Fredrick Kayusi, Petros Chavula.