Vulnerability detection methods use a combination of methods to identify vulnerabilities in an application or network. While some of these methods require more scanning effort than others, they all perform similar tasks. AVS, for example, is a popular vulnerability detection method, and performs a number of tests to identify vulnerabilities. However, this method can take a long time to run depending on the size of the network and how the scan is configured. PVD, on the other hand, can detect vulnerabilities with minimal scanning effort.
Another vulnerability detection method is based on program slices, which are finer-grained than functions. A program slice can contain many lines of code, and 78.7% of program slices are at least 10 lines long. This type of vulnerability detection has poor locating precision, and is often used as a first step to vulnerability assessment.
Deep learning is another technique used in vulnerability detection. It can eliminate the need for human feature engineering, and is expected to eventually replace some traditional vulnerability detection methods. The disadvantage of using deep learning in vulnerability detection is that it requires access to the source code of the application. This is not possible in most cases, and the limited number of models used to train the system limits its effectiveness. Further, existing code vulnerability detection methods often rely on 10-fold cross-validation training methods, which limit their performance on large project data.
Vulnerability Detection Methods
The two main types of vulnerability detection methods are rule-based and machine learning-based. Neither method is a complete solution, and both have their limitations. For example, rule-based vulnerability detection methods have high false-positive rates and low detection capability. However, these approaches are not yet robust, and some of them require human analysts to define their rules.
Passive vulnerability detection methods differ from Active Vulnerability Scanning, and although they should not replace each other, they can increase the coverage of vulnerabilities and increase their reliability. Passive vulnerability detection methods are often more reliable than AVS. Regardless of their advantages, vulnerability detection is an essential cyber security process.
CNN-LSTM is a composite neural network that combines a convolutional neural network and a long short-term memory. The CNN-LSTM model improves accuracy, precision, and recall scores over competing methods. The CNN-LSTM model also reduces false-positive rates and miss rates.
Traditional vulnerability detection methods require domain experts to develop feature engineering. However, with the use of deep learning technologies and machine learning, automated vulnerability detection methods can be developed to automatically detect vulnerabilities. With the help of these technologies, automated detection technology can improve software quality and productivity. If properly implemented, these methods can be used in combination with other technologies.