Authors :
Adeyemi Afolayan Adesola
Volume/Issue :
Volume 10 - 2025, Issue 4 - April
Google Scholar :
https://tinyurl.com/59ymxds4
DOI :
https://doi.org/10.38124/ijisrt/25apr2255
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
The cybersecurity landscape is changing so fast. We need advanced threat intelligence frameworks. They should
predict, detect, and prevent emerging risks in various domains. Thus, this review aimed to examine frameworks for cyber
environments. These include cyber-physical systems (CPS), IoT networks, blockchain platforms, and cloud infrastructures.
We aimed to evaluate their effectiveness and find gaps. Then, we would propose ways to improve cybersecurity resilience.
Our study used a systematic review of the literature. It analyzed frameworks that use technologies like AI, ML, and
automation. We found some strengths in the existing frameworks. They include real-time threat detection, adaptive defenses,
and cross-domain collaboration via unified taxonomies. The key limitations, however, were high implementation costs,
technical complexity, and scalability challenges. We thus concluded that while current frameworks have noteworthy
capabilities, their adoption is generally limited by resource and technical barriers. We recommend that simplifying
deployment processes, fostering interdisciplinary collaborations, and leveraging emerging technologies can help create
scalable and effective cybersecurity solutions. To address the gaps identified, we proposed a hypothetical Adaptive
Multimodal Threat Intelligence Framework (AMTIF), aimed at mitigating the laxities of existing frameworks. AMTIF
combines data standardization, predictive analytics, behavioral simulations, and secure cross-domain data sharing. Using
emerging technologies, such as blockchain, quantum computing, and self-supervised learning, we expect AMTIF to advance
speculative threat intelligence.
Keywords :
Cyber-Physical Systems, Predictive Analytics, Cross-Domain Collaboration, Machine Learning, Adaptive Defenses, IoT Security, Blockchain Threat Intelligence.
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The cybersecurity landscape is changing so fast. We need advanced threat intelligence frameworks. They should
predict, detect, and prevent emerging risks in various domains. Thus, this review aimed to examine frameworks for cyber
environments. These include cyber-physical systems (CPS), IoT networks, blockchain platforms, and cloud infrastructures.
We aimed to evaluate their effectiveness and find gaps. Then, we would propose ways to improve cybersecurity resilience.
Our study used a systematic review of the literature. It analyzed frameworks that use technologies like AI, ML, and
automation. We found some strengths in the existing frameworks. They include real-time threat detection, adaptive defenses,
and cross-domain collaboration via unified taxonomies. The key limitations, however, were high implementation costs,
technical complexity, and scalability challenges. We thus concluded that while current frameworks have noteworthy
capabilities, their adoption is generally limited by resource and technical barriers. We recommend that simplifying
deployment processes, fostering interdisciplinary collaborations, and leveraging emerging technologies can help create
scalable and effective cybersecurity solutions. To address the gaps identified, we proposed a hypothetical Adaptive
Multimodal Threat Intelligence Framework (AMTIF), aimed at mitigating the laxities of existing frameworks. AMTIF
combines data standardization, predictive analytics, behavioral simulations, and secure cross-domain data sharing. Using
emerging technologies, such as blockchain, quantum computing, and self-supervised learning, we expect AMTIF to advance
speculative threat intelligence.
Keywords :
Cyber-Physical Systems, Predictive Analytics, Cross-Domain Collaboration, Machine Learning, Adaptive Defenses, IoT Security, Blockchain Threat Intelligence.