The rapid advancement of technologies such as artificial intelligence has led to increasing complexity in human computer fusion systems, which poses significant challenges to the security of these systems. Recent developments in hardware, software, and algorithms have exacerbated the security landscape of human computer fusion complex systems across multiple dimensions, underscoring the need for a comprehensive review of attack and defense technologies in this domain. In this paper, we systematically review security issues in human computer fusion complex systems from various perspectives, with the aim of summarizing the current state of the field and encouraging further exploration by researchers. Specifically, our review is organized into the following key areas based on the security protection targets: (1) Human-machine interaction safety. We explore methods for measuring human-machine trust and discuss strategies for its repair and recalibration in dynamic contexts. (2) Human-computer collaborative safety. We discuss potential issues arising during human-device interactions and communication, alongside security challenges of intelligent algorithms and data privacy. Following a review of representative works, we discuss the experimental findings. Finally, we summarize the challenges in each area and point out some promising directions.

The rapid expansion of Web APIs presents developers with significant challenges in selecting optimal API compositions. To address this issue, keyword-based API composition recommendation techniques have been proposed. However, these methods often suffer from popularity bias due to the influence of historical datasets and recommendation models. This bias leads to the disproportionate recommendation of popular APIs over less popular ones, potentially causing the Matthew effect and impeding the balanced development of the API ecosystem. Although several studies have identified and attempted to mitigate popularity bias, they have largely relied on static analysis without accounting for the dynamic nature of API recommendations. In this paper, we introduce a dynamic simulation framework for API composition recommendations, designed to explore the evolution of popularity bias within recommendation results, and propose a debiasing method for dynamic recommendations by combining the enhanced API correlation graph with the Determinantal Point Process (DPP) method. Finally, extensive experiments on real datasets show that the algorithm effectively alleviates the popularity bias problem while guaranteeing high recommendation accuracy.