transformers pytorch roberta fill-mask cybersecurity cyber threat intellignece en doi:10.57967/hf/0042 license:bigscience-openrail-m autotrain_compatible endpoints_compatible region:us
---
language:
- en
license: bigscience-openrail-m
tags:
- cybersecurity
- cyber threat intellignece
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
---
# 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)
 

![image](https://user-images.githubusercontent.com/46252665/195998237-9bbed621-8002-4287-ac0d-19c4f603d919.png)

## 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} }