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---
license: bigscience-openrail-m
widget:
- text: >-
Native API functions such as <mask>, may be directed invoked via system
calls/syscalls, but these features are also often exposed to user-mode
applications via interfaces and libraries..
example_title: Native API functions
- text: >-
One way of explicitly assigning the PPID of a new process is via the <mask>
API call, which supports a parameter that defines the PPID to use.
example_title: Assigning the PPID of a new process
- text: >-
Enable Safe DLL Search Mode to force search for system DLLs in directories
with greater restrictions (e.g. %<mask>%) to be used before local directory
DLLs (e.g. a user's home directory)
example_title: Enable Safe DLL Search Mode
- text: >-
GuLoader is a file downloader that has been used since at least December
2019 to distribute a variety of <mask>, including NETWIRE, Agent Tesla,
NanoCore, and FormBook.
example_title: GuLoader is a file downloader
language:
- en
tags:
- cybersecurity
- cyber threat intellignece
---
# SecureBERT: A Domain-Specific Language Model for Cybersecurity
SecureBERT is a domain-specific language model based on RoBERTa which is trained on a huge amount of cybersecurity data and fine-tuned/tweaked to understand/represent cybersecurity textual data.
[SecureBERT](https://link.springer.com/chapter/10.1007/978-3-031-25538-0_3) is a domain-specific language model to represent cybersecurity textual data which is trained on a large amount of in-domain text crawled from online resources. ***See the presentation on [YouTube](https://www.youtube.com/watch?v=G8WzvThGG8c&t=8s)***
See details at [GitHub Repo](https://github.com/ehsanaghaei/SecureBERT/blob/main/README.md)
## SecureBERT can be used as the base model for any downstream task including text classification, NER, Seq-to-Seq, QA, etc.
* SecureBERT has demonstrated significantly higher performance in predicting masked words within the text when compared to existing models like RoBERTa (base and large), SciBERT, and SecBERT.
* SecureBERT has also demonstrated promising performance in preserving general English language understanding (representation).
# How to use SecureBERT
SecureBERT has been uploaded to [Huggingface](https://huggingface.co/ehsanaghaei/SecureBERT) framework. You may use the code below
```python
from transformers import RobertaTokenizer, RobertaModel
import torch
tokenizer = RobertaTokenizer.from_pretrained("ehsanaghaei/SecureBERT")
model = RobertaModel.from_pretrained("ehsanaghaei/SecureBERT")
inputs = tokenizer("This is SecureBERT!", return_tensors="pt")
outputs = model(**inputs)
last_hidden_states = outputs.last_hidden_state
## Fill Mask
SecureBERT has been trained on MLM. Use the code below to predict the masked word within the given sentences:
```python
#!pip install transformers
#!pip install torch
#!pip install tokenizers
import torch
import transformers
from transformers import RobertaTokenizer, RobertaTokenizerFast
tokenizer = RobertaTokenizerFast.from_pretrained("ehsanaghaei/SecureBERT")
model = transformers.RobertaForMaskedLM.from_pretrained("ehsanaghaei/SecureBERT")
def predict_mask(sent, tokenizer, model, topk =10, print_results = True):
token_ids = tokenizer.encode(sent, return_tensors='pt')
masked_position = (token_ids.squeeze() == tokenizer.mask_token_id).nonzero()
masked_pos = [mask.item() for mask in masked_position]
words = []
with torch.no_grad():
output = model(token_ids)
last_hidden_state = output[0].squeeze()
list_of_list = []
for index, mask_index in enumerate(masked_pos):
mask_hidden_state = last_hidden_state[mask_index]
idx = torch.topk(mask_hidden_state, k=topk, dim=0)[1]
words = [tokenizer.decode(i.item()).strip() for i in idx]
words = [w.replace(' ','') for w in words]
list_of_list.append(words)
if print_results:
print("Mask ", "Predictions : ", words)
best_guess = ""
for j in list_of_list:
best_guess = best_guess + "," + j[0]
return words
while True:
sent = input("Text here: \t")
print("SecureBERT: ")
predict_mask(sent, tokenizer, model)
print("===========================\n")
```
# Reference
@inproceedings{aghaei2023securebert,
title={SecureBERT: A Domain-Specific Language Model for Cybersecurity},
author={Aghaei, Ehsan and Niu, Xi and Shadid, Waseem and Al-Shaer, Ehab},
booktitle={Security and Privacy in Communication Networks:
18th EAI International Conference, SecureComm 2022, Virtual Event,
October 2022,
Proceedings},
pages={39--56},
year={2023},
organization={Springer} } |