Improving False Positive Detection with Neural Networks

Table of Contents:

1. Introduction
2. The Problem of False Positives
3. Applying Machine Learning to False Positive Detection
4. The Goal of the Research
5. Scope and Limitations
6. Previous Approaches to False Positive Detection
7. Introducing Neural Networks
8. The Architecture of the Network
9. Preparing and Training the Network
10. Tuning the System
11. Addressing Legitimate False Positives
12. Conclusion

False Positive Detection Using Neural Networks

False positives are a common issue in various domains, including application security and network security. When a detection system mistakenly identifies legitimate actions or behaviors as threats, it can lead to unnecessary restrictions or disruptions. To address this problem, we have applied machine learning techniques, specifically recurrent neural networks, to improve false positive detection. In this article, we will discuss our research and the steps we took to design and train a neural network for this purpose.

1. Introduction

False positive detection is a critical aspect of security systems. It involves distinguishing between legitimate user actions and potential threats. Traditional detection methods often rely on static rules or signatures, which can be limited in their effectiveness. By using machine learning and neural networks, we aim to build a system that can adapt and improve its detection logic over time.

2. The Problem of False Positives

False positives occur when a security system incorrectly identifies a benign action as malicious. This can happen due to various reasons, such as the use of complex Patterns or insufficient understanding of context. False positives can cause inconvenience to users and lead to unnecessary investigations and resource consumption. Therefore, mitigating false positives is crucial for the efficiency and usability of security systems.

3. Applying Machine Learning to False Positive Detection

We believe that machine learning, and specifically neural networks, can help in reducing false positives. Neural networks are capable of learning complex patterns and contexts, allowing them to make more accurate decisions. By training a neural network on a diverse dataset of malicious and benign activities, we can teach it to recognize the characteristics of genuine threats and avoid false positives.

4. The Goal of the Research

Our research aims to automate the process of handling false positives in security systems. We want to replace the manual incident response process with a system that can identify false positives and correct the underlying detection logic automatically. Additionally, we aim to make this system independent of the specific detection logic, allowing it to adapt to different types of threats and technologies.

5. Scope and Limitations

For this research, we focused on the detection of false positives related to SQL injection payloads. However, the methods and principles discussed can be applied to other types of security threats as well. It is important to note that our system does not address false positives caused by intentionally designed vulnerabilities, such as systems interpreting data as comments. Nevertheless, we plan to tackle this issue in future development.

6. Previous Approaches to False Positive Detection

Before applying machine learning, we explored traditional methods for false positive detection. These methods included defining formal logic for attack payloads, understanding the semantics of different syntaxes, and addressing performance challenges. While these approaches provided valuable insights, they had limitations in terms of adaptability and 100% accuracy.

7. Introducing Neural Networks

Neural networks offer a promising solution for false positive detection. In our research, we chose recurrent neural networks (RNNs) due to their ability to handle variable-length payloads. We implemented an attention mechanism in our network to focus on specific parts of the payload that are more indicative of malicious activities. Additionally, we used bidirectional RNNs to capture the semantics of the payload by analyzing both preceding and following words.

8. The Architecture of the Network

Our neural network architecture consists of an embedding layer, multiple layers of bidirectional LSTM cells, max pooling and average pooling layers, and an attention layer. This architecture allows the network to learn the Relevant features of the payload and make predictions based on them. We experimented with different configurations and found this architecture to be effective in achieving accurate detection results.

9. Preparing and Training the Network

To train the neural network, we need to preprocess the data and create a vocabulary based on the tokens in our dataset. We also employ the tandem algorithm to remove Lisp-style breaks and ensure consistent parsing. Training the network requires TensorFlow 1.1+ and can be performed using our provided training script. We have made the code and a sample dataset available on GitHub for further exploration and experimentation.

10. Tuning the System

To achieve the best results, the system can be tuned by adjusting various hyperparameters. These include the number of Hidden layers and neurons, the size of the attention hidden layer, and the dropout rate. However, it is essential to find a balance between accuracy and resource consumption, as increasing the complexity of the network may significantly impact training time and computational requirements.

11. Addressing Legitimate False Positives

One of the challenges in false positive detection is distinguishing between legitimate false positives and actual threats. For example, websites like Stack Overflow may have legitimate reasons to accept certain types of payloads that our system may classify as false positives. To address this, we are working on incorporating metadata related to specific applications and reducing legitimate false positives using request parameters and custom syntaxes.

12. Conclusion

In conclusion, false positive detection is a critical aspect of security systems, and machine learning techniques, specifically neural networks, offer promising solutions. By training a neural network on a diverse dataset, we can improve the accuracy of threat detection and reduce false positives. Our research has provided valuable insights into the application of recurrent neural networks for false positive detection. We encourage further exploration and development in this field to enhance the security and efficiency of detection systems.

Highlights:

• False positives are a common issue in security systems, leading to unnecessary disruptions and resource consumption.
• Machine learning, specifically neural networks, can help improve false positive detection by learning complex patterns and contexts.
• Our research aims to automate the incident response process and make the system adaptable to different threats and technologies.
• Recurrent neural networks, with attention mechanisms and bidirectional layers, provide an effective architecture for false positive detection.
• Tuning the system requires finding the right balance between accuracy and resource consumption.
• Addressing legitimate false positives is an ongoing challenge, requiring the incorporation of metadata and custom syntaxes.

FAQ:

Q: Can the neural network be trained on different types of security threats? A: Yes, the principles and methods discussed in our research can be applied to various types of security threats.

Q: How can I tune the system to achieve better results? A: You can adjust hyperparameters such as the number of hidden layers and neurons, the size of the attention hidden layer, and the dropout rate. However, be mindful of the impact on training time and resource consumption.

Q: Can I use the system to detect legitimate false positives specific to my application? A: Yes, we are working on incorporating metadata and custom syntaxes to reduce legitimate false positives. This will allow you to customize the system for your application's unique requirements.