Network Intrusion Detection
47 papers with code • 5 benchmarks • 12 datasets
Network intrusion detection is the task of monitoring network traffic to and from all devices on a network in order to detect computer attacks.
Benchmarks
These leaderboards are used to track progress in Network Intrusion Detection
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Libraries
Use these libraries to find Network Intrusion Detection models and implementationsDatasets
UNSW-NB15
CICIDS2017
SIDD-Image
CIC IoT Dataset 2022
IoT Network Intrusion Dataset
IoT Benign and Attack Traces
IoT ENVIRONMENT DATASET
Latest papers with no code
SecureBERT and LLAMA 2 Empowered Control Area Network Intrusion Detection and Classification
Numerous studies have proved their effective strength in detecting Control Area Network (CAN) attacks.
Explaining Tree Model Decisions in Natural Language for Network Intrusion Detection
Finally, we show LLM generated decision tree explanations correlate highly with human ratings of readability, quality, and use of background knowledge while simultaneously providing better understanding of decision boundaries.
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 selection, leading to models struggling to capture crucial relationships and generalize effectively.
The Efficacy of Transformer-based Adversarial Attacks in Security Domains
The transferability of these adversarial examples is measured by evaluating each set on other models to determine which models offer more adversarial strength, and consequently, more robustness against these attacks.
Give and Take: Federated Transfer Learning for Industrial IoT Network Intrusion Detection
The rapid growth in Internet of Things (IoT) technology has become an integral part of today's industries forming the Industrial IoT (IIoT) initiative, where industries are leveraging IoT to improve communication and connectivity via emerging solutions like data analytics and cloud computing.
ByteStack-ID: Integrated Stacked Model Leveraging Payload Byte Frequency for Grayscale Image-based Network Intrusion Detection
Notably, our approach is exclusively grounded in packet-level information, a departure from conventional Network Intrusion Detection Systems (NIDS) that predominantly rely on flow-based data.
Untargeted White-box Adversarial Attack with Heuristic Defence Methods in Real-time Deep Learning based Network Intrusion Detection System
In this research work, we aim to cover important aspects related to NIDS, adversarial attacks and its defence mechanism to increase the robustness of the ML and DL based NIDS.
Detecting Unknown Attacks in IoT Environments: An Open Set Classifier for Enhanced Network Intrusion Detection
The widespread integration of Internet of Things (IoT) devices across all facets of life has ushered in an era of interconnectedness, creating new avenues for cybersecurity challenges and underscoring the need for robust intrusion detection systems.
TII-SSRC-23 Dataset: Typological Exploration of Diverse Traffic Patterns for Intrusion Detection
The effectiveness of network intrusion detection systems, predominantly based on machine learning, are highly influenced by the dataset they are trained on.
Efficient Network Representation for GNN-based Intrusion Detection
The last decades have seen a growth in the number of cyber-attacks with severe economic and privacy damages, which reveals the need for network intrusion detection approaches to assist in preventing cyber-attacks and reducing their risks.
