LLM-Driven Cyber Threat Prediction

• LLM-driven cyber threat prediction is a proactive cybersecurity approach that uses large language models to fuse structured and unstructured data, enabling early threat detection.
• It integrates predictive analytics with semantic mapping and hierarchical, low-latency architectures deployed from edge devices to enterprise CTI systems.
• Empirical evaluations demonstrate high accuracy, effective IOC extraction, and improved lead times, underpinning robust and adaptive cyber defense strategies.

LLM-driven cyber threat prediction refers to the application of LLMs to forecast, detect, and contextualize cyberattacks, indicators of compromise, and adversarial behavior by leveraging both structured and unstructured data streams. This approach integrates LLMs with anomaly detection, proactive reasoning, and real-time intelligence aggregation, with deployments spanning from network edge devices to enterprise-scale cyber threat intelligence (CTI) systems. The paradigm advances beyond conventional reactive detection by enabling predictive analytics, rich semantic mapping (e.g., to MITRE ATT&CK or IOCs), and cross-device or organization-wide collaborative defense strategies (Hasan et al., 2024, Diaf et al., 3 Jan 2025, Diaf et al., 2024, Hans et al., 23 Oct 2025, Otoum et al., 1 May 2025, Liu et al., 28 Feb 2025, Paul et al., 1 Apr 2025, Chawla et al., 13 Jan 2026).

1. System Architectures and Dataflows

LLM-driven cyber threat prediction architectures are characterized by hierarchical, distributed, or modular layouts optimized for low-latency, real-time operation.

Edge/MEC Deployment: “Distributed Threat Intelligence at the Edge Devices” (Hasan et al., 2024) employs a three-tier hierarchy:

• Edge Devices: IoT sensors or endpoints ingest local streams (network packets, logs), run lightweight ML (e.g., TFLite), generate feature vectors $x_t$, calculate anomaly scores $s_t$ and labels $\hat{y}_t$; publishing alerts via secure MQTT.
• Edge Server: Aggregates alerts, refines classification (e.g., meta-classifiers), and coordinates mitigation (quarantine). For ambiguous threats, it escalates to the central LLM server.
• Central LLM Server: Implements in-context learning, adapts to emerging threats via prompt injection, provides high-level analytics, and distributes updated heuristics.

Proactive Pipeline Models: “BARTPredict” (Diaf et al., 3 Jan 2025) and “Beyond Detection” (Diaf et al., 2024) articulate multi-component feedback loops:

• Traffic Prediction: BART/GPT-based models generate next-packet forecasts using autoregressive decoding over header features.
• Threat Classification: BERT or LSTM-based modules assess the predicted packet (and context) for malicious attributes, propagating only high-confidence predictions to the mitigation/alert logic.

Enterprise CTI Copilots: “CyLens” (Liu et al., 28 Feb 2025) uses an agentic, modular pipeline integrating:

• Topic modeling (e.g., via BERTopic)
• Entity and relation extraction
• Retrieval-augmented generation (RAG) for integrating live feeds
• Chain-of-thought (CoT) LLM reasoning and summarization

Centralized Threat Intelligence Extraction: Systems like those described in (Chawla et al., 13 Jan 2026, Paul et al., 1 Apr 2025) implement web-scale crawling, parsing, and prompting pipelines to extract, score, and prioritize IOCs with LLMs, optionally using external vector stores and embeddings for semantic similarity in retrieval.

2. Model Architectures and Learning Objectives

Multiple LLM variants, often coordinated with conventional deep learning models, underpin threat prediction:

• BART/Transformer-based Predictors: Fine-tuned, sequence-to-sequence BART models forecast next-packet features, using embeddings and cross-attention on packet tokenized representations (Diaf et al., 3 Jan 2025). The training loss combines negative log-likelihood for prediction ($\mathcal{L}_{\mathrm{pred}}$) and cross-entropy for binary classification ($\mathcal{L}_{\mathrm{cls}}$).
• BERT-based Classifiers: Fine-tuned transformer encoders, e.g., distilBERT, assess packet pairs or summaries for probable threat labels. Binary or multi-class cross-entropy loss functions are used.
• Hybrid Feedback Loops: “Beyond Detection” introduces GPT-driven prediction, BERT-based plausibility scoring, and LSTM-based threat/attack classification in a tightly coupled feedback arrangement (Diaf et al., 2024).
• LLM-augmented RAG: Retrieval-Augmented Generation integrates external context (e.g., up-to-date CVE/KEV/EPSS data) into LLMs via embedding-based similarity search (e.g., SentenceTransformers, Milvus vector database), yielding fact-grounded responses and boosting predictive coverage for recent threats (Paul et al., 1 Apr 2025).
• Agentic Modular Pipelines: CYLENS (Liu et al., 28 Feb 2025) orchestrates reasoning across sequential specialized NLP modules (topic modeling, NER, REL, RAG, reasoning, summarization), with each step passing context and outputs to the LLM core.

3. Threat Types, Data Sources, and Prediction Tasks

LLM-driven frameworks operate over broad threat classes and data types:

Deployment/Domain	Primary Data	Threat Task(s)
IoT/Edge	Packets, logs	Anomaly detection, next-packet and type forecasting, real-time quarantine (Hasan et al., 2024, Diaf et al., 3 Jan 2025, Diaf et al., 2024, Otoum et al., 1 May 2025)
CTI/Enterprise	Threat reports, CVEs, IOCs, forum posts	IOC extraction and classification, actor/TTP attribution, vulnerability prioritization, campaign correlation (Liu et al., 28 Feb 2025, Chawla et al., 13 Jan 2026, Clairoux-Trepanier et al., 2024, Paul et al., 1 Apr 2025)
Social Media	Tweets, text	Multi-lingual cyber threat textual detection, sentiment/polarity-aware triage (Murad et al., 4 Feb 2025)

Significant sources include:

• Network captures (PCAP), system logs
• Threat forums (e.g., XSS, Exploit.in, RAMP) (Clairoux-Trepanier et al., 2024)
• Threat intelligence feeds (e.g., CVE, CWE, KEV, EPSS) (Paul et al., 1 Apr 2025, Liu et al., 28 Feb 2025)
• Public threat reports (CrowdStrike, Mandiant, etc.) (Chawla et al., 13 Jan 2026)
• Raw IDS logs (e.g., Suricata), mapped to ATT&CK (Hans et al., 23 Oct 2025)

Prediction objectives include not only binary attack classification but forecasting attack phases, mapping to MITRE ATT&CK techniques, cognitive trait inference, and suggesting automated or context-aware mitigation actions.

4. Model Evaluation, Quantitative Results, and Benchmarking

LLM-driven threat prediction systems have demonstrated high accuracy, recall, and proactive lead-time in diverse settings:

• Edge/IoT Packet Prediction: “BARTPredict” achieved 98.26% overall accuracy for packet binary classification. Removal of bidirectional encoders led to a ≈1.5% drop in accuracy, while omission of next-packet forecasting led to ~2% F1-score degradation (Diaf et al., 3 Jan 2025). “Beyond Detection” reported 98% accuracy, macro-F1 0.96 (binary), with precursors like GPT→BERT→LSTM outperforming stand-alone LSTM (95%) or ablated variants (Diaf et al., 2024).
• Forum-based CTI Extraction: A zero-shot GPT-3.5 pipeline achieved mean accuracy 96.23%, precision 90.0%, and recall 88.2% for ten binary/multilabel CTI variables (Clairoux-Trepanier et al., 2024).
• Proactive IOC Extraction: Gemini 1.5 Pro achieved precision 0.958, recall 1.000, specificity 0.788, F1 0.978 for IOC identification in unstructured web threat reports—outperforming Llama/Qwen and showing near-perfect recall (Chawla et al., 13 Jan 2026).
• Edge/MEC Lightweight LLMs: BERT-Small reached 99.75% accuracy, F1 99.75%, and FPR 0.15% in Docker-based IoT-simulations, while reducing latency by ~90 ms over Snort (Otoum et al., 1 May 2025).
• Cognitive/Strategic Mapping: Suricata-LLM achieved participant-level ATT&CK precision 0.88±0.05, recall 0.45±0.12, mean lead time 5.2 min over SOC incident logs (Hans et al., 23 Oct 2025).
• Enterprise CTI Pipelines: CyLens-8B reached actor attribution accuracy 87.6/83.7% (historical/zero-day), F1 93.8% on TTP-listing, >90% on all CVSS metric predictions, RMSE<5% for EPSS prediction, and advisory suggestion >98% (Liu et al., 28 Feb 2025).

Ablation and extension studies repeatedly show that the predictive, multi-step architectures (LLM-driven forecasting and semantic evaluation) consistently outperform monolithic classifiers or rule-based baselines.

5. Advanced Reasoning, Contextualization, and Cognitive Inference

LLM-driven systems enable capabilities not accessible to reactive signature-based approaches:

• Semantic Enrichment and Cross-Layer Reasoning: Prompt-driven segmentation and mapping enable models to transform IDS logs into MITRE ATT&CK labels with quantified confidences, bridging the gap between low-level telemetry and high-level technique reasoning (Hans et al., 23 Oct 2025).
• Cognitive Trait Inference: Behavioral segmentation with LLMs can model adversarial cognitive biases, e.g., loss aversion, risk tolerance, goal persistence. Formal definitions such as $LA(a), RT(a), GP(a)$ are derived from log sequence attributes and mapped to “high/low/medium” classifications via binned thresholds. Logistic regression is used to correlate observed features with trait predictions, enabling strategy-adaptive defensive responses (e.g., adjusting risk-weighting or firewall policies in real time) (Hans et al., 23 Oct 2025).
• Retrieval-Augmented Generation (RAG) and Real-time Fusion: LLMs equipped with external context, e.g., via sentence-transformer-based dense retrieval (all-mpnet-base-v2, Milvus), can reason over the latest vulnerabilities, surface top-EPSS threats, and output context-grounded answers. Explicit cosine similarity, softmax weighting, and boosted EPSS scoring tailor ranking to operational risk, allowing up-to-the-minute threat assessment (Paul et al., 1 Apr 2025).
• Multi-Stage NLP Modules: Pipelines like CyLens combine topic modeling, NER, relation extraction, and summarization for robust, explainable CTI generation, outperforming both generalist LLMs and specialist cyber agents, with all reasoning steps traceable for analyst review (Liu et al., 28 Feb 2025).

6. Limitations, Edge Constraints, and Research Directions

Key challenges and ongoing research areas are identified across the primary literature:

• Resource Constraints: Large LLMs incur high compute/memory requirements at the edge/MEC; mitigation strategies include using TinyBERT/BERT-Mini, model quantization/distillation (TinyBART, DistilCAM), and federated fine-tuning (Diaf et al., 3 Jan 2025, Otoum et al., 1 May 2025). “Bi-LSTM” architectures occasionally outperform LLMs when labeled data is limited or when the LLM is not domain-fine-tuned (Murad et al., 4 Feb 2025).
• Domain Adaptation and Generalization: LLMs fine-tuned on general or out-of-domain corpora exhibit lower recall or collapse on minority classes; continuous curriculum pre-training, domain-adaptive transfer (e.g., MLM on threat reports), and scoped fine-tuning are essential (Liu et al., 28 Feb 2025).
• Error Modes and Guidance: Common inference failures include tense and context misalignment in prompt design (historical vs ongoing sales), chunking artifacts, and ambiguous concept definitions (e.g., “large organization”) (Clairoux-Trepanier et al., 2024). Prompt revisions, explicit context enrichment, and post-processing rules are suggested.
• Evaluation and Fine-Tuning: Improved specificity in IOC extraction could hinge on retrieval-augmentation or labeled fine-tuning rather than strict zero-shot inference. Measures to reduce semantic confusion in domain names and across language boundaries are areas for further study (Chawla et al., 13 Jan 2026, Murad et al., 4 Feb 2025).

Emerging directions include automated pipeline construction (modular containerization), continuous vector store updates for RAG, explainable multi-hop reasoning (CoT prompting), and cognitive-adaptive SOC workflows integrating LLM-based behavioral cues.

7. Implications for Operational Cyber Defense

LLM-driven cyber threat prediction frameworks constitute a shift from retrospective or signature-based security models toward real-time, semantically rich, and adaptable defense strategies:

• Operational Impact: Early detection (lead times >1 min), low false-positive rates, and context-aware mitigation translate to material gains in enterprise and IoT security operations (Hasan et al., 2024, Otoum et al., 1 May 2025, Paul et al., 1 Apr 2025).
• Collaborative Intelligence: Peer-to-peer model, collaborative fine-tuning, federated learning, and secure aggregation at the edge/server level are highlighted as pathways to robust, scalable defense (Hasan et al., 2024).
• Analyst Workflow Integration: Human-in-the-loop systems, with explainable output (traceable reasoning trees or dashboards), support high-confidence SOC triage and informed response planning (Liu et al., 28 Feb 2025, Hans et al., 23 Oct 2025).
• Extensibility: Modular pipelines and domain adaptation strategies permit generalization to supply-chain risk assessment, insider threat profiling, multi-lingual abuse detection, and proactive post-incident forensics.

In summary, LLM-driven cyber threat prediction is distinguished by hierarchical, adaptive architectures; semantically aware, multi-stage reasoning; proactive and predictive analytics; and demonstrated empirical superiority over classical and static-detection paradigms. The field continues to evolve in response to advances in both LLM architectures and cyber threat intelligence methodologies.