CyberOps-Bots Framework Overview

• CyberOps-Bots Framework is a hierarchical multi-agent system designed to execute autonomous cyber operations through coordinated LLM planning and specialized RL agents.
• The framework leverages advanced planning, role specialization, and DRL algorithms to achieve high-fidelity emulation, rapid policy transfer, and efficient cyber defense.
• It integrates human-in-the-loop modules and automated governance to orchestrate countermeasures, bolster botnet detection, and sustain dynamic network resilience.

The CyberOps-Bots Framework encompasses a class of hierarchical, multi-agent architectures designed for robust, autonomous cyber operations, including attack, defense, and botnet management across realistic, dynamic environments. Such frameworks commonly integrate advanced modules for planning, coordination, learning, and execution, leveraging everything from reinforcement learning (RL) and deep RL (DRL), generative adversarial techniques, graph neural networks, and LLM-based tactical reasoning. CyberOps-Bots targets scenarios including enterprise and cloud network defense, red/blue teaming, and large-scale detection/remediation, with explicit support for human-in-the-loop (HITL) operations and multi-layer policy governance (Li et al., 2023, Peng et al., 12 Jan 2026, Kepner et al., 2022, Kong et al., 23 Jan 2025, Kadel et al., 2024).

1. Foundational Architecture and Design Principles

CyberOps-Bots frameworks are organized hierarchically, typically comprising two or three tightly integrated layers. At the upper layer, a central agent—often instantiated as an LLM-powered planner—performs global situational awareness, IPDRR-based perception, long-term memory management, and tactical planning. The lower layer consists of a pool of specialized RL agents (or collaborative multi-agent systems) trained to execute atomic operations within localized domains or regions, such as defense acts (e.g., patching, isolating, resetting nodes) or attack/penetration tactics (Peng et al., 12 Jan 2026, Li et al., 2023, Kong et al., 23 Jan 2025). Each agent’s policy $\pi_{\theta_k}:O_k\rightarrow A_k$ is optimized for its designated role via separated pre-training for increased robustness and transferability.

The architectural logic extends to a standardized orchestration and data-collection substrate capable of driving high-fidelity emulation and rapid simulation. An environment layer models the cyber terrain (e.g., simulated cloud networks with dynamic topology), enabling adversary penetration, lateral movement, and coordinated attack vectors (Li et al., 2023, Peng et al., 12 Jan 2026).

2. Multi-Agent Coordination: Planning, Role Specialization, and Communication

CyberOps-Bots achieve functional modularity through explicit agent role specialization—for example, separating tasks into reconnaissance, scanning, and exploitation for red-team pentesting, or fortifying, recovering, isolating, and purging in defense (Kong et al., 23 Jan 2025, Peng et al., 12 Jan 2026). Coordination is managed through (i) penetration task graphs (PTG) or directed acyclic dependency structures and (ii) cross-agent communication via concise, natural-language summaries, JSON envelopes, or long/short-term memory readouts. The PTG enforces the logical sequence of actions, while memory retrievers enable agents to leverage historical context and support plan updates post-failure (Kong et al., 23 Jan 2025). For defensive settings, the LLM planner leverages strategic modules (ReAct reasoning, semantic perception, HITL instructions integration), orchestrating atomic actions or dispatching RL agents to subnet domains.

Agents exchange summaries between phases, maintaining context and enabling distributed decision-making. Communication protocols are designed for lossless context propagation, enabling resilience and adaptive plan merging (via topological sorting and reflection algorithms) (Kong et al., 23 Jan 2025, Peng et al., 12 Jan 2026).

3. Environment Modeling, Observation, and Representation Transfer

CyberOps-Bots environments are typically formalized as Markov Decision Processes (MDPs) with state spaces $S=(s_0,\,s_1,\ldots)$, action spaces $A=\{a_1,\ldots,a_K\}$, transition functions $T(s'|s,a)$, and rewards $R(s,a,s')$ (Li et al., 2023). Red-team agents are exposed to varied observation-embedding schemes (ACTNeT, ACT, TACT), allowing invariant policy transfer in the face of superficial network changes such as IP/hostname scrambling, host addition/removal, or network reconfiguration (Li et al., 2023). This approach yields policies that generalize robustly over previously unseen network variants, as evidenced by 100% success rates in action-only embedding deployments.

For cloud defense, upper-layer agents organize perception using NIST IPDRR (Identify, Protect, Detect, Respond, Recover) pillars, converting high-dimensional network states into machine- and human-interpretable summaries. Tactical adaptation is supported by long-term memory modules that track historic attack chains for retrieval-augmented planning (Peng et al., 12 Jan 2026), with HITL support to fuse human instructions as part of the state vector.

4. Algorithms, Learning Protocols, and Reward Structures

CyberOps-Bots leverage DRL algorithms including Deep Q-Networks (DQN), Categorical DQN (CDQN, C51 Rainbow), and Proximal Policy Optimization (PPO), with benchmarking performed both in emulated (slow, high-fidelity; CyGIL-E) and simulated (fast, FSM-driven; CyGIL-S) environments (Li et al., 2023). CDQN demonstrates superior convergence rates and sample efficiency, reducing training times for optimal kill chains from 7–20 days (pure emulation) to 15–38 hours (hybrid E/S loop) (Li et al., 2023).

Rewards are designed to reflect operational objectives, e.g., single sparse reward for domain-admin escalation in red-team settings, or composite asset, security, and cost terms for defense. For hierarchical defense, overall objective is

$$\max_{\pi_{LLM},\,\{\theta_k\}}\,\mathbb{E}_{\tau\sim\pi}\left[\sum_{t=0}^T\gamma^tR(s_t,a_t,s_{t+1})\right]$$

with transition dynamics factored between attacker and defender moves (Peng et al., 12 Jan 2026). Heterogeneous separated pre-training stabilizes MARL workflows, with each agent $\phi$ trained on tailored scenarios and sub-rewards (Peng et al., 12 Jan 2026).

For bot/human discrimination (BOTracle), technical-feature discriminators leverage semi-supervised GANs (SGANs), while behavioral-stage graph convolutional neural nets (DGCNNs) analyze session traversal graphs. Heuristic exclusion stages and confidence thresholds are applied for efficient scalable inference (Kadel et al., 2024).

5. Practical Workflows, Evaluation, and Benchmark Metrics

CyberOps-Bots frameworks are instantiated on a variety of experimental platforms:

• Penetration testing: AUTOPENBENCH (33 synthetic/CVE tasks), AI-Pentest-Benchmark (6 real machines); task graph logic with sequential agent specialization (Kong et al., 23 Jan 2025). VulnBot–Llama3.1 models achieve subtask success rates (single run: 69.0%; aggregate 49.9%) outperforming GPT-4o baselines. Retrieval-augmented planning (RAG) further improves exploitation rates on real machines.
• Red-team emulation: CyGIL-E/–S testbeds with rapid policy transfer; cyclical real–sim loops enable continual adaptation (Li et al., 2023).
• Cloud defense: Yawning Titan simulator, AWS enterprise datasets (450 nodes, 6 subnets), Attack policies Recon/Penetrate/Impact, RL agent roles fortify/recover/purge/block (Peng et al., 12 Jan 2026). CyberOps-Bots maintains network availability 68.5% higher and gains 34.7% jumpstart performance on dynamic scale shifts compared to MAPPO, IPPO, QMIX, and VDN baselines. Efficiency is demonstrated by linear scaling of LLM tokens/step, and diminished hallucination rates under ReAct reasoning and STM augmentation.
• Botnet detection: BOTracle pipeline achieves ≥ 98% classification accuracy and recall, outperforming Botcha-MAM and matching Botcha-RAM, using strictly passive, multi-stage detection (heuristics, SGAN, DGCNN), and proves robust against most session-level evasion techniques (Kadel et al., 2024).

Operational metrics include mean reward, availability ratio, episode length, jumpstart gain, AUROC, accuracy, recall, F₁, computational efficiency (tokens/step, time/step), and resilience measures.

6. Countermeasure Orchestration and Policy Governance

CyberOps-Bots frameworks for botnet defense instantiate Phase I–III architectures for observe–pursue–counter operations (Kepner et al., 2022):

• Phase I: Network observatories deploy AI-enabled anomaly detectors on anonymized sparse traffic matrices; performance measured by Detection Rate (DR), False-Alarm Rate (FAR), Observation Coverage ($\eta_{\text{obs}}$), Detection Latency ($L_{\text{det}}$).
• Phase II: Alerts aggregated, deanonymized under legal authority, clustered and attributed via network science engines and threat intelligence. Pursuit workflow is organized for rapid sinkholing and client remediation, tracked by Pursuit Latency, Attribution Accuracy, Resource Utilization.
• Phase III: Orchestrated countermeasures involve sinkholing C&C, client patching, updating network filters, post-action verification, and performance metrics including countermeasure efficacy, time-to-remediate, resurgence rate, and cost–benefit ratio.
• Integrated governance structures (e.g., CyDef Committee, International Botnet Takedown Community) ensure compliance, privacy, and cross-border operational synchronization.

7. Limitations, Generalization, and Future Directions

CyberOps-Bots architectures, while empirically robust across multiple operational dimensions (structure, scale, attack policies, temporal dynamics), identify several open challenges:

• Centralized LLM modules pose single points of failure; latency increases versus pure RL.
• FSM synthesis and retraining triggers require principled statistical modeling for optimal simulator–emulator cycles, especially for large-scale or multi-agent coordination demands (Li et al., 2023).
• Advanced bots mimicking human timing and technical features may evade current passive detection pipelines; adversarial training and extended feature engineering (keystroke, mouse dynamics) are shown as future research paths (Kadel et al., 2024).
• HITL integration afford flexible tactical overrides but increase complexity; future work includes mini-agent decentralization, tool learning, and guaranteed bounds on model hallucination rates (Peng et al., 12 Jan 2026).
• Policy frameworks must evolve to support growing international cooperation, liability protections, and binding data-sharing agreements at scale (Kepner et al., 2022).

The CyberOps-Bots Framework demonstrates a convergence of multi-agent specialization, hierarchical planning, rapid adaptation to dynamic environments, and rigorous policy-governed workflows, setting technical benchmarks for autonomous cyber operation agents and defenses (Li et al., 2023, Peng et al., 12 Jan 2026, Kepner et al., 2022, Kong et al., 23 Jan 2025, Kadel et al., 2024).