SoK: Pragmatic assessment of machine learning for network intrusion detection
G Apruzzese, P Laskov… - 2023 IEEE 8th European …, 2023 - ieeexplore.ieee.org
Machine Learning (ML) has become a valuable asset to solve many real-world tasks. For
Network Intrusion Detection (NID), however, scientific advances in ML are still seen with …
Network Intrusion Detection (NID), however, scientific advances in ML are still seen with …
Towards continuous threat defense: In-network traffic analysis for IoT gateways
The widespread use of IoT devices has unveiled overlooked security risks. With the advent
of ultra-reliable lowlatency communications (URLLC) in 5G, fast threat defense is critical to …
of ultra-reliable lowlatency communications (URLLC) in 5G, fast threat defense is critical to …
[PDF][PDF] Leo: Online ML-based Traffic Classification at Multi-Terabit Line Rate.
Leo: Online ML-based Traffic Classification at Multi-Terabit Line Rate Page 1 Leo: Online
ML-based Traffic Classification at Multi-Terabit Line Rate Syed Usman Jafri Sanjay Rao …
ML-based Traffic Classification at Multi-Terabit Line Rate Syed Usman Jafri Sanjay Rao …
In Search of netUnicorn: A Data-Collection Platform to Develop Generalizable ML Models for Network Security Problems
The remarkable success of the use of machine learning-based solutions for network security
problems has been impeded by the developed ML models' inability to maintain efficacy …
problems has been impeded by the developed ML models' inability to maintain efficacy …
Everybody's Got ML, Tell Me What Else You Have: Practitioners' Perception of ML-Based Security Tools and Explanations
Significant efforts have been investigated to develop machine learning (ML) based tools to
support security operations. However, they still face key challenges in practice. A generally …
support security operations. However, they still face key challenges in practice. A generally …
Point Cloud Analysis for ML-Based Malicious Traffic Detection: Reducing Majorities of False Positive Alarms
As an emerging security paradigm, machine learning (ML) based malicious traffic detection
is an essential part of automatic defense against network attacks. Powered by dedicated …
is an essential part of automatic defense against network attacks. Powered by dedicated …
Interpreting gnn-based ids detections using provenance graph structural features
K Mukherjee, J Wiedemeier, T Wang, M Kim… - arXiv preprint arXiv …, 2023 - arxiv.org
The black-box nature of complex Neural Network (NN)-based models has hindered their
widespread adoption in security domains due to the lack of logical explanations and …
widespread adoption in security domains due to the lack of logical explanations and …
netFound: Foundation Model for Network Security
In ML for network security, traditional workflows rely on high-quality labeled data and
manual feature engineering, but limited datasets and human expertise hinder feature …
manual feature engineering, but limited datasets and human expertise hinder feature …
The Challenges of Machine Learning for Trust and Safety: A Case Study on Misinformation Detection
We examine the disconnect between scholarship and practice in applying machine learning
to trust and safety problems, using misinformation detection as a case study. We systematize …
to trust and safety problems, using misinformation detection as a case study. We systematize …
The Missing Link in Network Intrusion Detection: Taking AI/ML Research Efforts to Users
K Dietz, M Mühlhauser, J Kögel, S Schwinger… - IEEE …, 2024 - ieeexplore.ieee.org
Intrusion Detection Systems (IDS) tackle the challenging task of detecting network attacks as
fast as possible. As this is getting more complex in modern enterprise networks, Artificial …
fast as possible. As this is getting more complex in modern enterprise networks, Artificial …