User modeling in the era of large language models: Current research and future directions
User modeling (UM) aims to discover patterns or learn representations from user data about
the characteristics of a specific user, such as profile, preference, and personality. The user …
the characteristics of a specific user, such as profile, preference, and personality. The user …
When llms meet cybersecurity: A systematic literature review
The rapid advancements in large language models (LLMs) have opened new avenues
across various fields, including cybersecurity, which faces an ever-evolving threat landscape …
across various fields, including cybersecurity, which faces an ever-evolving threat landscape …
ShieldGPT: An LLM-based framework for DDoS mitigation
The constantly evolving Distributed Denial of Service (DDoS) attacks pose a significant
threat to the cyber realm, which underscores the importance of DDoS mitigation as a pivotal …
threat to the cyber realm, which underscores the importance of DDoS mitigation as a pivotal …
Xg-nid: Dual-modality network intrusion detection using a heterogeneous graph neural network and large language model
In the rapidly evolving field of cybersecurity, the integration of flow-level and packet-level
information for real-time intrusion detection remains a largely untapped area of research …
information for real-time intrusion detection remains a largely untapped area of research …
Machine Learning with Computer Networks: Techniques, Datasets and Models
Machine learning has found many applications in network contexts. These include solving
optimisation problems and managing network operations. Conversely, networks are …
optimisation problems and managing network operations. Conversely, networks are …
DoLLM: How Large Language Models Understanding Network Flow Data to Detect Carpet Bombing DDoS
Q Li, Y Zhang, Z Jia, Y Hu, L Zhang, J Zhang… - arXiv preprint arXiv …, 2024 - arxiv.org
It is an interesting question Can and How Large Language Models (LLMs) understand non-
language network data, and help us detect unknown malicious flows. This paper takes …
language network data, and help us detect unknown malicious flows. This paper takes …
Through the Thicket: A Study of Number-Oriented LLMs derived from Random Forest Models
M Romaszewski, P Sekuła, P Głomb… - arXiv preprint arXiv …, 2024 - arxiv.org
Large Language Models (LLMs) have shown exceptional performance in text processing.
Notably, LLMs can synthesize information from large datasets and explain their decisions …
Notably, LLMs can synthesize information from large datasets and explain their decisions …
Exploring Large Language Models Capabilities to Explain Decision Trees
Decision trees are widely adopted in Machine Learning tasks due to their operation
simplicity and interpretability aspects. However, following the decision process path taken by …
simplicity and interpretability aspects. However, following the decision process path taken by …
Towards Autonomous Intrusion Detection: Leveraging Artificial Intelligence
MJ Rex, S Chattopadhyay, KS Kumar… - 2024 15th …, 2024 - ieeexplore.ieee.org
The growth of cyber-attacks and records breaches, there is a growing want for effective and
efficient intrusion detection structures (IDS). Conventional IDS are based heavily on human …
efficient intrusion detection structures (IDS). Conventional IDS are based heavily on human …
Enhancing Machine Learning Model Interpretability in Intrusion Detection Systems through SHAP Explanations and LLM-Generated Descriptions
A Khediri, H Slimi, A Yahiaoui… - … on Pattern Analysis …, 2024 - ieeexplore.ieee.org
Intrusion Detection Systems (IDS) are critical for detecting and mitigating cyber threats, yet
the opaqueness of machine learning models used within these systems poses challenges …
the opaqueness of machine learning models used within these systems poses challenges …