Intellicise (Intelligent and Concise) wireless network is the main direction of the evolution of future mobile communication systems, a perspective now widely acknowledged across academia and industry. As a key technology within it, Agentic AI has garnered growing attention due to its advanced cognitive capabilities, enabled through continuous perception-memory-reasoning-action cycles. This paper first analyses the unique advantages that Agentic AI introduces to intellicise wireless networks. We then propose a structured taxonomy for Agentic AI-enhanced secure intellicise wireless networks. Building on this framework, we identify emerging security and privacy challenges introduced by Agentic AI and summarize targeted strategies to address these vulnerabilities. A case study further demonstrates Agentic AI's efficacy in defending against intelligent eavesdropping attacks. Finally, we outline key open research directions to guide future exploration in this field.
To address the growing diversity of service demands in mobile communication systems while optimizing the use of limited network resources, Intellicise (intelligent and concise) wireless networks integrate foundational theories such as information theory, artificial intelligence (AI) theory, and systems theory, thus achieving intention-driven, semantic bearing, and distributed autonomy capabilities [1], [2]. The integration also introduces new security issues. For instance, sensing information such as Channel State Information (CSI) is used to enhance communication and network performance, but intelligent attackers may capture CSI samples from public environments and analyze private information about users' daily behaviors. Additionally, intelligent eavesdroppers can utilize advanced signal processing and decoding techniques to (Corresponding author: Rui Meng and Xiaodong Xu.) Rui Meng, Zhidi Zhang, Song Gao, Yaheng Wang, Xiaodong Xu, Yijing Lin, Yiming Liu, and Ping Zhang are the State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China (e-mail: buptmengrui@bupt.edu.cn; 2639134068@bupt.edu.cn; wkd251292@bupt.edu.cn; wangyaheng@bupt.edu.cn; xuxiaodong@bupt.edu.cn; yjlin@bupt.edu.cn; liuyiming@bupt.edu.cn; pzhang@bupt.edu.cn).
Chenyuan Feng is with Department of Computer Science, University of Exeter, EX4 4QF Exeter, U.K. (e-mail: c.feng@exeter.ac.uk).
Lexi Xu is with the Research Institute, China United Network Communications Corporation, Beijing, China (e-mail: davidlexi@hotmail.com).
Yi Ma and Rahim Tafazolli are with 5GIC & 6GIC, Institute for Communication Systems (ICS), University of Surrey, Guildford, GU2 7XH, United Kingdom (email: y.ma@surrey.ac.uk; r.tafazolli@surrey.ac.uk).
reconstruct private semantic information transmitted in public environments [3].
In response to security and privacy threats in intellicise wireless networks, generative AI leverages its strengths in unsupervised learning and diverse content generation to address challenges faced by discriminative AI-based defense technologies. However, static generative AI-based defenses face the following challenges. First and foremost, they exhibit inability to actively defend: relying on manual input to trigger feedback mechanisms, they cannot proactively set defense goals or define defense processes. Furthermore, they encounter difficulty in coping with dynamic attacks: while capable of generating security analysis reports, they fail to directly translate these into defensive actions, necessitating human intervention for final responses. Ultimately, they reveal a lack of continuous learning and evolutionary capabilities: struggling to dynamically optimize models based on realtime attack-defense feedback, they depend on manual data reinjection and model updates to counter emerging threats.
Against these challenges, Agentic AI, intelligent systems characterized by continuous perception-memory-reasoningaction loops, facilitates autonomous attack and threat perception, defense reasoning, and defense decision-making [4]. Firstly, Agentic AI achieves proactive defense through a perception-driven autonomous decision-making loop. Secondly, leveraging techniques such as multi-agent deep reinforcement learning (DRL), game theory, and meta-learning, Agentic AI constructs dynamic response models to enable realtime collaborative defense. Thirdly, by employing techniques like retrieval-augmented generation (RAG) to update attack samples and defense strategies, Agentic AI achieves sustained learning and evolutionary defense enhancement [5]. Nevertheless, as an emerging technique, Agentic AI may introduce additional threats to intellicise wireless networks [6]. For example, multi-agent collusion creates erroneous group consensus, coupled with premature truncation of logical chains, thus undermining the self-evolutionary and objective-driven reasoning within Intellicise wireless networks [7]. Therefore, how to defend against the potential security risks is also important.
Against this background, we present a systematic investigation into integrating intellicise wireless networks and Agentic AI from a security and privacy perspective. The primary contributions are outlined as:
• We analyze the benefits that Agentic AI brings to intel-
Complex Scenarios
In this section, we first introduce Intellicise wireless networks and Agentic AI, followed by an exploration of several key junctions between them.
As illustrated in Fig. 1 Part (A), the intellicise wireless network includes the following modules [1].
• Brain for Intellicise Wireless Networks: The brain serves as the central intelligence hub for intellicise wireless networks. By continuously ingesting and analyzing real-time data, the brain performs intelligent state inference to assess critical network parameters. Then, the system generates evolutionary strategies for network optimization. Furthermore, it translates these strat
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