Preparing for Unknown Cyber Threats: A Comprehensive Review of Framework for Speculative Threat Intelligence Using Cross-Domain Indicators

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.