Temporal Blindness in AI-Secured IoT Systems: Missed Multi Stage Attacks in Cloud-Centric Detection Pipelines

Author: Parth Talaviya, Manya Parikh, Deep Solanki, and Jigar Gajjar
Published Online: July 1, 2026
DOI: http://doi.org/10.63766/spujstmr.26.000084
Abstract
References

The increase in the use of Internet of Things (IoT) technology has brought forth new possibilities for connectivity, but it has also brought forth the possibility of complex cyber threats. To counter these threats, there has been a growing need for the implementation of Artificial Intelligence (AI) security systems that possess the capability to process large amounts of data, identify anomalies, and implement automated response systems. Although these systems have brought forth greater efficiency and enhanced the capabilities of security systems, recent studies have also indicated that these systems may bring forth new threats. Temporal blindness, which refers to the inability of detection algorithms to identify low-intensity events over a period of time, is one such threat.This paper discusses how cloud-based detection systems, despite their efficiency and processing capabilities, may inadvertently bring forth the fragmentation of security telemetry data. This may result in complex multi-step attacks that change over a period of time going undetected until considerable damage has been caused. Through a critical analysis of existing literature and an analysis of the current scenario, this paper discusses the importance of implementing temporal correlation functions along with real-time analytics to enhance situational awareness. Finally, this paper concludes that there is a growing need to integrate intelligent systems with human intelligence to develop effective AI secured IoT networks that can counter complex cybersecurity threats.

Keywords: Blindness, AI, IOT System, Cloud, Attacks, Detection Pipelines, Temporal
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