# Vulnerability Detection Using Two-Stage Deep Learning Models

arXiv: https://arxiv.org/abs/2305.09673 · HTML: https://mlsanity.com/paper/2305.09673/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 64 references that are in the map.
- First posted on arXiv 8 May 2023.

## Builds on (most cited first, 18 of 18)

- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Transformers in Vision: A Survey](https://mlsanity.com/paper/2101.01169/) (2021)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [Pre-trained Models for Natural Language Processing: A Survey](https://mlsanity.com/paper/2003.08271/) (2020)
- [Deep Learning based Vulnerability Detection: Are We There Yet?](https://mlsanity.com/paper/2009.07235/) (2020)
- [LogBERT: Log Anomaly Detection via BERT](https://mlsanity.com/paper/2103.04475/) (2021)
- [Automated Vulnerability Detection in Source Code Using Deep Representation Learning](https://mlsanity.com/paper/1807.04320/) (2018)
- [SySeVR: A Framework for Using Deep Learning to Detect Software Vulnerabilities](https://mlsanity.com/paper/1807.06756/) (2018)
- [CVEfixes: Automated Collection of Vulnerabilities and Their Fixes from Open-Source Software](https://mlsanity.com/paper/2107.08760/) (2021)
- [Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network](https://mlsanity.com/paper/1902.01019/) (2019)
- [Combining Graph Neural Networks with Expert Knowledge for Smart Contract Vulnerability Detection](https://mlsanity.com/paper/2107.11598/) (2021)
- [A Deeper Look at Facial Expression Dataset Bias](https://mlsanity.com/paper/1904.11150/) (2019)
- [Security Vulnerability Detection Using Deep Learning Natural Language Processing](https://mlsanity.com/paper/2105.02388/) (2021)
- [Packet2Vec: Utilizing Word2Vec for Feature Extraction in Packet Data](https://mlsanity.com/paper/2004.14477/) (2020)
- [Big data analysis and distributed deep learning for next-generation intrusion detection system optimization](https://mlsanity.com/paper/2209.13961/) (2022)
- [Anomaly detection optimization using big data and deep learning to reduce false-positive](https://mlsanity.com/paper/2209.13965/) (2022)
- [Deep Learning For Smile Recognition](https://mlsanity.com/paper/1602.00172/) (2016)
- [Multi-context Attention Fusion Neural Network for Software Vulnerability Identification](https://mlsanity.com/paper/2104.09225/) (2021)

## Abstract (arXiv)

Application security is an essential part of developing modern software, as lots of attacks depend on vulnerabilities in software. The number of attacks is increasing globally due to technological advancements. Companies must include security in every stage of developing, testing, and deploying their software in order to prevent data breaches. There are several methods to detect software vulnerability Non-AI-based such as Static Application Security Testing (SAST) and Dynamic Application Security Testing (DAST). However, these approaches have substantial false-positive and false-negative rates. On the other side, researchers have been interested in developing an AI-based vulnerability detection system employing deep learning models like BERT, BLSTM, etc. In this paper, we proposed a two-stage solution, two deep learning models were proposed for vulnerability detection in C/C++ source codes, the first stage is CNN which detects if the source code contains any vulnerability (binary classification model) and the second stage is CNN-LTSM that classifies this vulnerability into a class of 50 different types of vulnerabilities (multiclass classification model). Experiments were done on SySeVR dataset. Results show an accuracy of 99% for the first and 98% for the second stage.