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 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
Load Model
SecureBER+T has been uploaded to Huggingface framework.
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:
#!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:
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} }