Text Classification
Transformers
PyTorch
distilbert
Network Intrusion Detection
Cybersecurity
Network Packets
text-embeddings-inference
Instructions to use rdpahalavan/bert-network-packet-flow-header-payload with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rdpahalavan/bert-network-packet-flow-header-payload with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rdpahalavan/bert-network-packet-flow-header-payload")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rdpahalavan/bert-network-packet-flow-header-payload") model = AutoModelForSequenceClassification.from_pretrained("rdpahalavan/bert-network-packet-flow-header-payload", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Parent(s): 4940f40
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- Network Packets
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This model was fine-tuned for
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- Network Packets
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This model was fine-tuned for classifying a network packet into the following categories:
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'Analysis'
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'Backdoor'
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'Bot'
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'DDoS'
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'DoS'
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'DoS GoldenEye'
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'DoS Hulk'
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'DoS SlowHTTPTest'
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'DoS Slowloris'
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'Exploits'
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'FTP Patator'
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'Fuzzers'
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'Generic'
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'Heartbleed'
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'Infiltration'
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'Normal'
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'Port Scan'
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'Reconnaissance'
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'SSH Patator'
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'Shellcode'
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'Web Attack - Brute Force'
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'Web Attack - SQL Injection'
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'Web Attack - XSS'
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'Worms'
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