---
license: cc-by-nc-4.0
language:
- en
tags:
- cybersecurity
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+
This model represents an improved version of the [SecureBERT](https://huggingface.co/ehsanaghaei/SecureBERT) model, trained on a corpus eight times larger than its predecessor, leveraging the computational power of 8xA100 GPUs. This version, known as SecureBERT+, brings forth an average improvment of 9% in the performance of the Masked Language Model (MLM) task. This advancement signifies a substantial stride towards achieving heightened proficiency in language understanding and representation learning within the cybersecurity domain.


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.

## Dataset
![image/png](https://cdn-uploads.huggingface.co/production/uploads/6340b0bd77fd972573eb2f9b/pO-v6961YI1D0IPcm0027.png)

## Load Model
SecureBER+T has been uploaded to [Huggingface](https://huggingface.co/ehsanaghaei/SecureBERT_Plus) framework. 

```python
from transformers import RobertaTokenizer, RobertaModel
import torch

tokenizer = RobertaTokenizer.from_pretrained("ehsanaghaei/SecureBERT_Plus")
model = RobertaModel.from_pretrained("ehsanaghaei/SecureBERT_Plus")

inputs = tokenizer("This is SecureBERT Plus!", return_tensors="pt")
outputs = model(**inputs)

last_hidden_states = outputs.last_hidden_state
```

## Fill Mask (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_Plus")
model = transformers.RobertaForMaskedLM.from_pretrained("ehsanaghaei/SecureBERT_Plus")

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")
```

Other model variants:

[SecureGPT](https://huggingface.co/ehsanaghaei/SecureGPT)

[SecureDeBERTa](https://huggingface.co/ehsanaghaei/SecureDeBERTa)

[SecureBERT](https://huggingface.co/ehsanaghaei/SecureBERT)


# 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} }