Multi-stage deep learning-based intrusion detection system for automotive Ethernet networks
LFM da Luz, PF de Araujo-Filho, DR Campelo - Ad Hoc Networks, 2024 - Elsevier
Modern automobiles are increasing the demand for automotive Ethernet as a high-
bandwidth and flexible in-vehicle network technology. However, since Ethernet does not …
bandwidth and flexible in-vehicle network technology. However, since Ethernet does not …
Demystifying Behavior-Based Malware Detection at Endpoints
Machine learning is widely used for malware detection in practice. Prior behavior-based
detectors most commonly rely on traces of programs executed in controlled sandboxes …
detectors most commonly rely on traces of programs executed in controlled sandboxes …
A NetAI Manifesto (Part II): Less Hubris, more Humility
The application of the latest techniques from artificial intelligence (AI) and machine learning
(ML) to improve and automate the decision-making required for solving real-world network …
(ML) to improve and automate the decision-making required for solving real-world network …
A Comparison of Neural-Network-Based Intrusion Detection against Signature-Based Detection in IoT Networks
M Schrötter, A Niemann, B Schnor - Information, 2024 - mdpi.com
Over the last few years, a plethora of papers presenting machine-learning-based
approaches for intrusion detection have been published. However, the majority of those …
approaches for intrusion detection have been published. However, the majority of those …
A Novel Self-Supervised Framework Based on Masked Autoencoder for Traffic Classification
Traffic classification is a critical task in network security and management. Recent research
has demonstrated the effectiveness of the deep learning-based traffic classification method …
has demonstrated the effectiveness of the deep learning-based traffic classification method …
Non-uniformity is All You Need: Efficient and Timely Encrypted Traffic Classification With ECHO
With 95% of Internet traffic now encrypted, an effective approach to classifying this traffic is
crucial for network security and management. This paper introduces ECHO--a novel …
crucial for network security and management. This paper introduces ECHO--a novel …
Genos: General In-Network Unsupervised Intrusion Detection by Rule Extraction
Anomaly-based network intrusion detection systems (A-NIDS) use unsupervised models to
detect unforeseen attacks. However, existing A-NIDS solutions suffer from low throughput …
detect unforeseen attacks. However, existing A-NIDS solutions suffer from low throughput …
[PDF][PDF] Attributions for ML-based ICS anomaly detection: From theory to practice
Industrial Control Systems (ICS) govern critical infrastructure like power plants and water
treatment plants. ICS can be attacked through manipulations of its sensor or actuator values …
treatment plants. ICS can be attacked through manipulations of its sensor or actuator values …
No pictures, please: Using eXplainable Artificial Intelligence to demystify CNNs for encrypted network packet classification
E Luis-Bisbé, V Morales-Gómez, D Perdices… - Applied Sciences, 2024 - mdpi.com
Featured Application The results of this work can be applied to improve machine learning-
based network packet classification. Abstract Real-time traffic classification is one of the …
based network packet classification. Abstract Real-time traffic classification is one of the …
Taming the Elephants: Affordable Flow Length Prediction in the Data Plane
Machine Learning (ML) shows promising potential for enhancing networking tasks by
providing early traffic predictions. However, implementing an ML-enabled system is a …
providing early traffic predictions. However, implementing an ML-enabled system is a …