Transformers and Large Language Models for Efficient Intrusion Detection Systems: A Comprehensive Survey

Hamza Kheddar

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Transformers and Large Language Models for Efficient Intrusion Detection Systems: A Comprehensive Survey

Hamza Kheddar

arXiv:2408.07583v2Aug 14, 2024cs.CRcs.AIcs.CLcs.CVeess.AS

TL;DR

The survey addresses the need to improve cyber-threat detection as network attacks, data conditions, and deployment environments become increasingly complex. It reviews Transformer- and LLM-based IDS research through paper selection, bibliometric analysis, architectural comparison, application analysis, and discussion of challenges and future directions. The reviewed literature indicates that these models offer enhanced anomaly detection and practical potential, while data diversity, real-time processing, computational resources, interpretability, and adaptability remain important constraints.

Problem
Cyber-threat detection must address evolving attacks, low-quality or limited security data, false positives, subtle attack deviations, and diverse deployment environments.
Method
The survey combines paper selection and bibliometric analysis with a review of Transformer and LLM architectures, datasets, IDS applications, case studies, challenges, and future directions.
Results
Transformer-based IDSs have outperformed traditional detection systems and can identify subtle anomalies, while CNN/LSTM hybrids, ViT-based methods, GAN-Transformer approaches, and GPT- or BERT-based systems address complex patterns and heterogeneous data.
Takeaways & Limitations
Transformers and LLMs offer significant potential to advance intrusion detection across complex cybersecurity environments and evolving attack vectors.
Takeaways & Limitations
The surveyed approaches remain constrained by data diversity, real-time processing, computational resources, interpretability, changing network environments, and limited high-quality security datasets.

Abstract

from arXiv · show

With significant advancements in Transformers LLMs, NLP has extended its reach into many research fields due to its enhanced capabilities in text generation and user interaction. One field benefiting greatly from these advancements is cybersecurity. In cybersecurity, many parameters that need to be protected and exchanged between senders and receivers are in the form of text and tabular data, making NLP a valuable tool in enhancing the security measures of communication protocols. This survey paper provides a comprehensive analysis of the utilization of Transformers and LLMs in cyber-threat detection systems. The methodology of paper selection and bibliometric analysis is outlined to establish a rigorous framework for evaluating existing research. The fundamentals of Transformers are discussed, including background information on various cyber-attacks and datasets commonly used in this field. The survey explores the application of Transformers in IDSs, focusing on different architectures such as Attention-based models, LLMs like BERT and GPT, CNN/LSTM-Transformer hybrids, emerging approaches like ViTs, among others. Furthermore, it explores the diverse environments and applications where Transformers and LLMs-based IDS have been implemented, including computer networks, IoT devices, critical infrastructure protection, cloud computing, SDN, as well as in autonomous vehicles. The paper also addresses research challenges and future directions in this area, identifying key issues such as interpretability, scalability, and adaptability to evolving threats, and more. Finally, the conclusion summarizes the findings and highlights the significance of Transformers and LLMs in enhancing cyber-threat detection capabilities, while also outlining potential avenues for further research and development.

1. Introduction

The evolving network ecosystem, including 5G and IoT, has expanded the potential for threats and vulnerabilities. Existing ML- and DL-based intrusion-detection methods still face challenges as network technologies evolve.

5G and widespread IoT adoption have expanded the potential for network threats and vulnerabilities.
Network attacks include malware, phishing, denial-of-service, man-in-the-middle, and advanced persistent threats.
Current ML- and DL-based IDS methods continue to encounter challenges as network technologies evolve.

Acronyms and Abbreviations

The paper uses a broad set of abbreviations spanning intrusion detection, networking, machine learning, Transformers, explainability, cyberattacks, and application environments.

Transformers and language models: Transformer and language-model terminology includes BERT, GPT, ViT-related SwinT, MLM, TNN-IDS, NLP, seq2seq, and CANINE.
Security and threat detection: Core security terms include IDS, HIDS, NIDS, IPS, SIEM, CTI, APTs, and WAF.
Networking and infrastructure: Networking and communication abbreviations include SDN, SCADA, ICS, CAN, TCP, UDP, HTTP, FTP, SMTP, SNMP, MQTT, TLS, NAT, and ICMP.
Explainability, statistics, and applications: Explainability and statistical terms include SHAP, LIME, KLD, MCC, and PDF, while application terms include IIoT, IoV, V2X, UAVs, MCU, FPGA, URL, and URLLC.
Machine learning models and methods: Machine-learning and sequence-model terms include CNN, LSTM, Bi-LSTM, RNN, MLP, FFNN, ReLU, GAN, CGAN, GMM, PCA, SMOTE, and XGBoost.

2. Background

This background section introduces IDS architectures, preprocessing, attack categories, datasets, and evaluation metrics that frame Transformer- and LLM-based intrusion detection research.

IDS methods: DL-based IDS frameworks use preprocessing followed by either signature-based or anomaly-based detection, with model training and testing completing the framework.Signature-based detection matches observed activity against known attack signatures, whereas anomaly-based detection identifies deviations from normal behavior.
Data preprocessing: Preprocessing reduces computational and memory costs through dataset downsizing, feature removal, encoding, and z-score normalization.The survey also describes k-means for reducing dataset size and SMOTE for generating minority-class samples to address imbalance.
IDS methods: Signature-based detection is effective against known attacks but struggles with new or mutated attacks and may be unsuitable for resource-constrained IoT environments.Anomaly-based methods instead monitor normal behavior and flag deviations beyond predefined thresholds, including zero-day threats.
Datasets and attacks: IDS datasets span network, host, industrial control system, IoT, cloud, edge, and smart-grid environments and contain diverse attack categories.Examples include DoS, remote-to-local, user-to-root, and probe attacks, alongside enduring vulnerabilities and protocol-specific attacks.
Evaluation metrics: Transformers and LLM-based IDS studies evaluate detection using accuracy, recall, precision, F1-score, and additional metrics such as Matthews correlation coefficient.MCC incorporates true positives, true negatives, false positives, and false negatives, supporting balanced assessment when class sizes differ substantially.

3. Transformers-based IDS methods

The survey categorizes Transformer-based IDS methods across attention, hybrid CNN/LSTM, ViT, GAN, and LLM architectures, while comparing datasets and applications. These methods target diverse intrusion-detection settings, including networks, vehicles, smart grids, and distributed environments.

Taxonomy of Transformer-based IDS methods: Transformer-based IDS research is organized into Attention, CNN/LSTM-Transformer, ViT, GAN-Transformer, GPT-based, and BERT-based methods.The survey compares their suitability, advantages, disadvantages, tasks, and reported findings.
Attention-based methods: Attention mechanisms assign different importance to input components, helping IDS models learn contextual relationships and detect threats or anomalies.The mechanism uses queries, keys, and values to calculate weighted representations of input data.
Attention-based methods: Attention-based systems support applications including SQL injection and XSS detection, malware-traffic classification, CAN-bus attack detection, and general network intrusion classification.Reported designs combine attention with CNNs, GRUs, self-attention, multi-head attention, or transfer learning.
CNN/LSTM-Transformer-based methods: Transformer hybrids extend intrusion detection to smart grids, where XTM combines Transformer and LSTM technologies to detect and locate real-time data breaches.The reported evaluation used the IEEE-14 bus system and described the model as achieving excellent detection accuracy.
ViT-based methods: ViT-based approaches transform cyber-threat data into visual representations or use ViTs for feature extraction and knowledge distillation.VINCENT reported 87.2% best overall accuracy, while DE-VIT reported a best result of 99.5% accuracy; batch-based learning may limit adaptation to evolving threats.
Other Transformer-based methods: The survey extends its coverage to federated Transformer IDS, where local training and shared model updates support distributed environments without transmitting raw data.The reviewed applications include network intrusion detection and vehicular edge nodes.

4. LLM-based methods

The survey organizes LLM-based IDS methods by Transformer architecture, emphasizing encoder-only, decoder-only, and encoder-decoder models and their cybersecurity applications. It highlights BERT and GPT applications to log, malware, threat-intelligence, and network-flow detection.

LLM categories: LLMs are grouped into encoder-only, decoder-only, and encoder-decoder architectures according to the Transformer components they primarily use.Encoder-only models support contextual representation and classification, decoder-only models generate sequences autoregressively, and encoder-decoder models map input sequences to output sequences.
Encoder-only-based methods: Encoder-only models such as BERT use masked language modeling to learn contextual representations by predicting masked tokens from surrounding sequence context.The objective maximizes the likelihood of the original tokens at masked positions.
Decoder-only-based methods: Decoder-only models such as GPT predict each next token from preceding context using autoregressive modeling and cross-entropy-based training.Training maximizes sequence likelihood, equivalently minimizing negative log-likelihood.
Encoder-only-based methods: BERT-based IDS applications analyze logs, alerts, and textual security data to identify subtle anomalous patterns and support intrusion detection.The survey describes BERT’s contextual understanding and reports a compressed IoT model achieving high accuracy with 788 parameters after a 90% reduction.
Decoder-only-based methods: GPT and BERT are applied to threat-intelligence reporting and network-sequence detection, while DoLLM structures network flows as sequences for LLM-based DDoS detection.DoLLM achieved an F1 score of 96.4% in zero-shot scenarios, but incurs computational overhead, external-processing reliance, and latency challenges.

5. Environments and applications for Transformers- and LLM-based IDS

Transformer- and LLM-based IDS research spans computer networks and IoT, using network, host, textual, numeric, and categorical data. The surveyed applications include Transformer architectures for traffic analysis, feature integration, and anomaly detection.

Computer networks: Computer-network IDSs include HIDS and NIDS, analyzing network logs containing tabular fields such as addresses, ports, and timestamps alongside payload data.Payloads may be binary or encoded text, motivating models that can process heterogeneous network information.
Computer networks: Hybrid architectures combine specialized processing for numeric, categorical, and text data to address heterogeneous network and host information.One approach combines MLP and CANINE components, while another embeds host messages with BERT and aggregates them with network features.
Computer networks: FlowTransformer uses GPT 2.0 and BERT to process complex network-traffic patterns for Transformer-based network intrusion detection.The framework evaluates generative and encoder models across multiple network datasets for sophisticated threat detection.
IoT: IoT IDS research includes adaptive Transformer attention and Transformer neural networks for MQTT environments, addressing feature importance and imbalanced training data.These studies illustrate Transformer-based IDS deployment in constrained and specialized IoT communication settings.
Comparative summary: The survey summarizes environment-specific performance and limitations in Table 6, with multiple-test cases represented by their best reported performance.The table covers applications across the surveyed environments and deployments.

5.3. Critical infrastructure

Critical infrastructure—including smart grids, SCADA systems, and web industries—requires robust IDS because cyber threats can disrupt essential services and operations. The surveyed work addresses high dimensionality and data imbalance in industrial-control datasets.

Critical infrastructure: Smart grids, SCADA systems, and web industries are identified as critical infrastructures vulnerable to unauthorized access, data breaches, and operational disruption.The survey frames IDS as necessary for protecting essential services and operations in these environments.
Critical infrastructure: One surveyed approach tackles high dimensionality and data imbalance in industrial-control-system datasets using information-gain-based feature selection.The cited method is presented as an approach for improving IDS processing of ICS data.
Cloud and SDN: Cloud and SDN systems face intrusion risks associated with centralized control, dynamic resource allocation, and multi-tenant environments.The survey describes Transformers and LLMs as analyzing large traffic volumes, contextual relationships, and anomalies in these settings.
Autonomous vehicles: Autonomous vehicles rely on wireless sensor and electronic-component signals, which increase exposure to malicious disruptions.The survey identifies Transformers and LLMs as relevant technologies for improving detection and response in AV infrastructures.

6. Real-world case studies of Transformers and LLM-based IDSs

The reviewed case studies examine Transformer- and LLM-based IDSs in testbed and dashboard deployments. They demonstrate alert aggregation, attack prediction, explainable anomaly detection, and modular operation, while also exposing infrastructure and adaptability constraints.

Transformer-based method: The Transformer-based case study correlates and aggregates alerts, then predicts attacks from variable-length inputs in a VMware testbed with servers, hosts, network equipment, and defense devices.Firewalls, a Snort-simulated Transformer NIDS, and TCPdump supported malicious-behavior capture; delayed, non-overlapping attacks approximated staged exploitation.
LLM-based method: The LLM-based HuntGPT case study combines a KDD99-trained random forest with SHAP, LIME, and a GPT-3.5 Turbo conversational agent in an IDS dashboard.The design presents detected threats in understandable form and supports inspection of anomaly data and interaction with an AI assistant.
System architecture: The case-study system uses modular layers that can be developed, maintained, and scaled independently, supporting flexibility and operational adaptability.Its autonomous components are described as contributing to robustness and adaptability.
Deployment limitations: Real-world deployment can require substantial hardware and network infrastructure because Transformer and LLM models are computationally complex and demanding.The associated infrastructure requirements may increase energy consumption and operational costs.
Deployment limitations: Adaptation across network environments remains difficult because distinct threat profiles require continual updating, training, data collection, and analysis.Maintaining accuracy as new threats emerge can strain resources and standardized IDS configurations may not represent every environment.

7. Research challenges and future directions

Transformers and LLMs offer new capabilities for intrusion detection, but deployment across heterogeneous environments remains constrained by data quality, computational demands, interpretability, privacy, and domain adaptation. Future directions emphasize adaptive agents, dynamic threat-intelligence integration, efficient architectures, and broader validation.

Transformers challenges: Transformers-based IDS face heterogeneous intrusion categories, specialized protocols, inconsistent data formats, and environment-specific preprocessing requirements.Challenges span signature- and anomaly-based detection, industrial legacy systems, SDN control-plane data, flow statistics, configuration changes, and diverse host logs.
Transformers challenges: Real-time Transformer IDS deployment is difficult because large models require substantial computation while SDN and IoT environments demand low-latency responses.The paper specifically identifies real-time or near-real-time processing and ultra-reliable low-latency communications as difficult to achieve with computationally demanding models.
LLM challenges: LLM performance and generalization depend on large, diverse, high-quality datasets, while limited or biased security data can produce skewed predictions and benchmark contamination can inflate metrics.The survey also highlights privacy concerns when training data contain personal information.
LLM challenges: LLMs require fine-tuning, validation, and continuous feedback across security tasks because broad pre-training may not provide specialized IDS expertise.Without these measures, models risk overfitting to training data and reduced efficacy in diverse real-world scenarios.
LLM challenges: Opaque LLM decision-making and ethical risks, including privacy breaches, misinformation, fake content, and exploitable code, constrain trustworthy IDS adoption.The survey links interpretability and trustworthiness to stakeholder willingness to rely on LLM-generated outputs and recommendations.
Perspectives and future directions: Future IDS research should combine LLM agents with reinforcement learning, dynamic threat intelligence, collaborative defenses, knowledge distillation, and lightweight models.LLM agents are presented as complements to classical reinforcement learning, while distillation and small language models target efficiency, scalability, and real-time processing.

8. Conclusion

Transformer-based models strengthen intrusion detection by identifying subtle anomalies in complex networks and supporting context-aware detection across heterogeneous cybersecurity environments. The survey also emphasizes unresolved challenges in data diversity, real-time processing, computational resources, and interpretability.

Attention mechanisms help Transformer-based models identify subtle anomalies within complex network environments.
CNN/LSTM-Transformer approaches capture spatial and temporal anomalies while reducing false positive rates and enhancing detection accuracy.
Data diversity, real-time processing, computational resources, and model interpretability remain important barriers to broader Transformer-based IDS adoption.
GPT-based and BERT-based IDSs improve context-aware adaptability for heterogeneous data sources and advanced attack vectors.
Real-world case studies assess practical effectiveness, deployment challenges, and opportunities for replication and extension in operational settings.

CRediT authorship contribution statement

The CRediT statement assigns Hamza Kheddar contributions spanning conceptualization, methodology, investigation, writing, validation, supervision, and project administration.

Hamza Kheddar contributed across research design, data curation, investigation, visualization, writing, validation, supervision, and project administration.