Multi‐aspects <scp>AI</scp>‐based modeling and adversarial learning for cybersecurity intelligence and robustness: A comprehensive overview

Sarker, Iqbal H.

doi:10.1002/spy2.295

Cited by 27 publications

(7 citation statements)

References 156 publications

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“…In the evolving landscape of cybersecurity, the implementation of Artificial Intelligence (AI) in risk assessment processes marks a significant advancement. AI methodologies, including analytical, functional, interactive, textual, and visual AI, offer a computational edge in addressing cybersecurity challenges, enhancing system intelligence and robustness against adversarial attacks (Sarker, 2023).…”

Section: Framework For Leveraging Ai In Cybersecurity Risk Assessmentmentioning

confidence: 99%

Harnessing adversarial machine learning for advanced threat detection: AI-driven strategies in cybersecurity risk assessment and fraud prevention

Onuh Matthew Ijiga,

Idoko Peter Idoko,

Godslove Isenyo Ebiega

et al. 2024

Open Access Res. J. Sci. Technol.

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The abstract is "The rapid evolution of cyber threats necessitates innovative defenses, particularly in the domains of risk assessment and fraud detection. This paper explores the integration of Artificial Intelligence (AI) and Adversarial Machine Learning (ML) techniques as a formidable strategy against increasingly sophisticated cyber-attacks. We present a comprehensive framework that leverages AI to dynamically assess cybersecurity risks and detect fraudulent activities with unprecedented accuracy and speed. Firstly, we delve into the foundational principles of adversarial machine learning, outlining how these techniques can be employed to simulate potential cyber threats, thereby enabling the development of more resilient AI-driven cybersecurity systems. We highlight the dual role of adversarial ML in both enhancing security defenses and potentially serving as a vector for sophisticated attacks, underscoring the importance of developing robust, adversarial-resistant models. Subsequently, we introduce a novel adaptive risk assessment methodology that incorporates real-time data analysis, machine learning algorithms, and predictive modeling to accurately identify and prioritize threats. This method adapts to the evolving digital landscape, ensuring that cybersecurity measures are always one step ahead of potential attackers. In the context of fraud detection, we explore -how AI algorithms can sift through vast datasets to detect anomalies and patterns indicative of fraudulent behavior. Through case studies and empirical analysis, we demonstrate the effectiveness of AI in identifying fraud across various sectors, from financial transactions to online identity verification processes. Our research contributes to the cybersecurity field by providing a detailed examination of how AI and adversarial ML can be harnessed to fortify digital defenses, improve risk assessment techniques, and enhance fraud detection capabilities. The insights garnered from this study not only advance theoretical understanding but also offer practical guidance for organizations seeking to implement AI-driven security solutions. As cyber threats continue to evolve, the integration of AI and adversarial ML in cybersecurity strategies will be paramount in safeguarding digital assets and maintaining the integrity of online systems."

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Section: Framework For Leveraging Ai In Cybersecurity Risk Assessmentmentioning

confidence: 99%

Harnessing adversarial machine learning for advanced threat detection: AI-driven strategies in cybersecurity risk assessment and fraud prevention

Onuh Matthew Ijiga,

Idoko Peter Idoko,

Godslove Isenyo Ebiega

et al. 2024

Open Access Res. J. Sci. Technol.

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“…To improve security, one should use artificial intelligence-based security solutions, such as artificial intelligence-based intrusion detection and network behavior analysis [49]. To detect and prevent security attacks, then carry out real-time network monitoring using an artificial intelligence system that can find unusual threats and attack patterns, as well as carry out training and security awareness for network users to reduce the risk of phishing attacks, malware, and other attacks.…”

Section: ) Improved Securitymentioning

confidence: 99%

The Influence of Artificial Intelligence Technology, Infrastructure and Human Resource Competence on Internet Access Networks

Awaludin,

Yasin,

Risyda

2024

Inf. J. Ilm. Bid. Teknol. Inf. dan Komun.

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The influence of artificial intelligence technology, infrastructure, and human resource competence on Internet access networks has been examined in a scientific publication, which is the outcome of a literature review in the information systems sector. The purpose of this study is to generate an influence hypothesis related to factors that can be applied in further investigations. The research's subjects include academic media, Google Scholar, Mendeley, and online libraries. Publicly accessible e-books and e-journals are the source of the research methodology that makes use of library institution searches. The following is a descriptive qualitative analysis of this article's findings: The influence of artificial intelligence technology on networks for Internet access, the influence of infrastructure on networks for Internet access, and the Influence of HR Competency on Internet Access Networks. Research findings regarding the impact of advances in infrastructure, human resource competence, and artificial intelligence technology on internet network access include improving user experience, extensive network scalability, increasing network efficiency and security, as well as increasing human resource knowledge and developing network architecture research findings regarding the impact of advances in infrastructure, human resource competence, and artificial intelligence technology on internet network access include improving user experience, extensive network scalability, increasing network efficiency and security, as well as increasing human resource knowledge and developing network architecture.

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“…There are various reasons why cybersecurity can be breached, such as malicious activity, phishing, intrusion, spam, ransomware, and spyware, among others, all perpetrated by cyber attackers. Artificial intelligence is currently the most promising field to safeguard the cyber world against these threats [ 10 ]. Industries and critical infrastructure are undergoing automation and digitization.…”

Section: Related Workmentioning

confidence: 99%

Automated Sensor Node Malicious Activity Detection with Explainability Analysis

Zubair,

Janicke,

Mohsin

et al. 2024

Sensors

Self Cite

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Cybersecurity has become a major concern in the modern world due to our heavy reliance on cyber systems. Advanced automated systems utilize many sensors for intelligent decision-making, and any malicious activity of these sensors could potentially lead to a system-wide collapse. To ensure safety and security, it is essential to have a reliable system that can automatically detect and prevent any malicious activity, and modern detection systems are created based on machine learning (ML) models. Most often, the dataset generated from the sensor node for detecting malicious activity is highly imbalanced because the Malicious class is significantly fewer than the Non-Malicious class. To address these issues, we proposed a hybrid data balancing technique in combination with a Cluster-based Under Sampling and Synthetic Minority Oversampling Technique (SMOTE). We have also proposed an ensemble machine learning model that outperforms other standard ML models, achieving 99.7% accuracy. Additionally, we have identified the critical features that pose security risks to the sensor nodes with extensive explainability analysis of our proposed machine learning model. In brief, we have explored a hybrid data balancing method, developed a robust ensemble machine learning model for detecting malicious sensor nodes, and conducted a thorough analysis of the model’s explainability.

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Multi‐aspects AI‐based modeling and adversarial learning for cybersecurity intelligence and robustness: A comprehensive overview

Cited by 27 publications

References 156 publications

Harnessing adversarial machine learning for advanced threat detection: AI-driven strategies in cybersecurity risk assessment and fraud prevention

Harnessing adversarial machine learning for advanced threat detection: AI-driven strategies in cybersecurity risk assessment and fraud prevention

The Influence of Artificial Intelligence Technology, Infrastructure and Human Resource Competence on Internet Access Networks

Automated Sensor Node Malicious Activity Detection with Explainability Analysis

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