REVIEW 8 cited by
HuntGPT: Integrating Machine Learning-Based Anomaly Detection and Explainable AI with Large Language Models (LLMs)
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Machine learning (ML) is crucial in network anomaly detection for proactive threat hunting, reducing detection and response times significantly. However, challenges in model training, maintenance, and frequent false positives impact its acceptance and reliability. Explainable AI (XAI) attempts to mitigate these issues, allowing cybersecurity teams to assess AI-generated alerts with confidence, but has seen limited acceptance from incident responders. Large Language Models (LLMs) present a solution through discerning patterns in extensive information and adapting to different functional requirements. We present HuntGPT, a specialized intrusion detection dashboard applying a Random Forest classifier using the KDD99 dataset, integrating XAI frameworks like SHAP and Lime for user-friendly and intuitive model interaction, and combined with a GPT-3.5 Turbo, it delivers threats in an understandable format. The paper delves into the system's architecture, components, and technical accuracy, assessed through Certified Information Security Manager (CISM) Practice Exams, evaluating response quality across six metrics. The results demonstrate that conversational agents, supported by LLM and integrated with XAI, provide robust, explainable, and actionable AI solutions in intrusion detection, enhancing user understanding and interactive experience.
Forward citations
Cited by 8 Pith papers
-
Cybersecurity Detection Classification with Reasoning-enabled Language Models
CoT-trained triage plus a separate reasoning calibrator reaches 82.6% accuracy and large high-confidence recall gains over direct-label LLM classifiers on real SOC detections.
-
Distilling Knowledge from Large Language Models into Lightweight Reinforcement Learning Agents for Autonomous Cyber Operations
LLM cyber-defense policies, obtained by prompt engineering alone, can be behavior-cloned into a 64,910-parameter RL agent that matches a heavily trained PPO baseline in the CybORG simulator.
-
(EC)2: Event-Centric Explainability for Cybersecurity Through Multi-Agent LLM Investigations
An event-centric, multi-agent LLM framework explains network alerts through hypothesis-driven, retrieval-augmented investigation and claims to improve explanation quality and boundary-case classification.
-
ThreatVisionAI: A Hybrid CNN-ViT Framework for Image-Based Malware Classification
A three-branch CNN-wavelet-ViT ensemble with soft voting reaches 98.01% accuracy and 0.9742 weighted F1 on Malimg, with wavelet features improving discrimination of similar families.
-
Large Language Models for Security Operations Centers: A Comprehensive Survey
A systematic review of 138 papers classifying LLM applications in SOC workflows by phase, model family, datasets, and maturity.
-
Large Language Models in Cybersecurity: Applications, Vulnerabilities, and Defense Techniques
A survey that maps LLM applications, vulnerabilities, and defenses across eight cybersecurity domains, but with significant citation and rigor problems.
-
Large Language Models for Network Intrusion Detection Systems: Foundations, Implementations, and Future Directions
A survey of LLM-based network intrusion detection that proposes a cognitive NIDS taxonomy and an LLM-centered controller architecture.
-
AnomalyExplainer Explainable AI for LLM-based anomaly detection using BERTViz and Captum
A chatbot that classifies HDFS log lines with fine-tuned RoBERTa and provides BERTViz/Captum visual explanations was benchmarked and tested with 13 users.
Discussion (0). Sign in to comment.