Speaker recognition is a task that identifies the speaker from multiple audios. Recently, advances in deep learning have considerably boosted the development of speech signal processing techniques. Speaker or speech recognition has been widely adopted in such applications as smart locks, smart vehicle-mounted systems, and financial services. However, deep neural network-based speaker recognition systems (SRSs) are susceptible to adversarial attacks, which fool the system to make wrong decisions by small perturbations, and this has drawn the attention of researchers to the security of SRSs. Unfortunately, there is no systematic review work in this domain. In this work, we conduct a comprehensive survey to fill this gap, which includes the development of SRSs, adversarial attacks and defenses against SRSs. Specifically, we first introduce the mainstream frameworks of SRSs and some commonly used datasets. Then, from the perspectives of adversarial example generation and evaluation, we introduce different attack tasks, the prior knowledge of attacks, perturbation objects, perturbation constraints, and attack effect evaluation indicators. Next, we focus on some effective defense strategies, including adversarial training, attack detection, and input refactoring against existing attacks, and analyze their strengths and weaknesses in terms of fidelity and robustness. Finally, we discuss the challenges posed by audio adversarial examples in SRSs and some valuable research topics in the future.

Deep-neural-network-based autonomous driving perception systems in the Industrial Internet of Things remain vulnerable to adversarial attacks despite their critical role in Industry 4.0. While numerous studies have investigated adversarial attacks on individual perception tasks such as monocular depth estimation or object detection in static situations, real-world complex industrial scenarios introduce two understudied challenges: 1) dynamic scenarios with moving objects and changing viewpoints and 2) simultaneous attacks across multiple perception tasks. In this article, we make three key contributions to address these challenges. First, we introduce a systematic study of environment-aware adversarial attacks in dynamic scenarios by introducing three adversarial attack tasks: object away attack, object close attack, and object creation attack. Second, we propose a novel dynamic scene-oriented adversarial road patch generation framework that accounts for real-world environmental variations. Third, we develop a comprehensive technical framework featuring: improved adversarial loss functions, a dynamic optimization architecture, and an integrated approach combining expectation over transformation with advanced physical augmentation optimization. Extensive experimental results demonstrate the robustness of our proposed method in digital, simulation, and real-world physical domains, as well as under different weather conditions.

Deep neural networks (DNNs) have been widely adopted in many fields, and they greatly promote the Internet of Health Things (IoHT) systems by mining health‐related information. However, recent studies have shown the serious threat to DNN‐based systems posed by adversarial attacks, which has raised widespread concerns. Attackers maliciously craft adversarial examples (AEs) and blend them into the normal examples (NEs) to fool the DNN models, which seriously affects the analysis results of the IoHT systems. Text data is a common form in such systems, such as the patients’ medical records and prescriptions, and we study the security concerns of the DNNs for textural analysis. As identifying and correcting AEs in discrete textual representations is extremely challenging, the available detection techniques are still limited in performance and generalizability, especially in IoHT systems. In this paper, we propose an efficient and structure‐free adversarial detection method, which detects AEs even in attack‐unknown and model‐agnostic circumstances. We reveal that sensitivity inconsistency prevails between AEs and NEs, leading them to react differently when important words in the text are perturbed. This discovery motivates us to design an adversarial detector based on adversarial features, which are extracted based on sensitivity inconsistency. Since the proposed detector is structure‐free, it can be directly deployed in off‐the‐shelf applications without modifying the target models. Compared to the state‐of‐the‐art detection methods, our proposed method improves adversarial detection performance, with an adversarial recall of up to 99.7% and an F1‐score of up to 97.8%. In addition, extensive experiments have shown that our method achieves superior generalizability as it can be generalized across different attackers, models, and tasks.

Security operations centre (SOC) analysts must investigate alerts, correlate threat intelligence and interpret heterogeneous telemetry under tight timing constraints. Although large language models (LLMs) offer strong understanding capabilities, directly applying them to SOC environments remains challenging due to semantic ambiguity in analyst queries, fragmented multisource event data, limited domain‐specific reasoning and reliability concerns associated with unconstrained query generation. We present a task‐driven knowledge‐augmented framework designed to produce verifiable and contextually grounded responses for SOC workflows. The framework integrates four components: (i) contrastive context task recognition that mitigates semantic ambiguity by mapping analyst queries to predefined SOC task types; (ii) expert‐guided knowledge augmentation that fuses dense and sparse retrieval to bridge the semantic gap; (iii) schema‐aligned event retrieval combined with entity‐centric evidence profiling to ensure reliable and secure access to heterogeneous telemetry and (iv) verifiable task‐aware generation that constrains model outputs to retrieved knowledge and security events. To assess the framework, we construct a benchmark of 12,500 validated question–answer pairs derived through semiautomated synthesis over more than 34 million real SOC records. Experiments across multiple foundation models demonstrate consistent improvements in relevance and grounding quality. Our results indicate that the four proposed components substantially enhance LLMs' reliability in practical SOC analysis.