A new method of software vulnerability detection based on a quantum neural network

Abstract

In the field of network security, although there has been related work on software vulnerability detection based on classic machine learning, detection ability is directly proportional to the scale of training data. A quantum neural network has been proven to solve the memory bottleneck problem of classical machine learning, so it has far-reaching prospects in the field of vulnerability detection. To fill the gap in this field, we propose a quantum neural network structure named QDENN for software vulnerability detection. This work is the first attempt to implement word embedding of vulnerability codes based on a quantum neural network, which proves the feasibility of a quantum neural network in the field of vulnerability detection. Experiments demonstrate that our proposed QDENN can effectively solve the inconsistent input length problem of quantum neural networks and the problem of batch processing with long sentences. Furthermore, it can give full play to the advantages of quantum computing and realize a vulnerability detection model at the cost of a small amount of measurement. Compared to other quantum neural networks, our proposed QDENN can achieve higher vulnerability detection accuracy. On the sub dataset with a small-scale interval, the model accuracy rate reaches 99%. On each subinterval data, the best average vulnerability detection accuracy of the model reaches 86.3%.

Recurrent quantum embedding neural network and its application in vulnerability detection

“…Such problems also plague applications such as vulnerability detection, as strong performance often means huge costs for complex models with extensive training. Therefore, one desire to find a more efficient computing method to optimize models and reduce costs 14 .Quantum computing is a computing method with great potential. In quantum computing, qubits are able to represent a superposition of exponentially multiple states simultaneously and allow simultaneous operations on the superposition states.…”

“…Such problems also plague applications such as vulnerability detection, as strong performance often means huge costs for complex models with extensive training. Therefore, one desire to find a more efficient computing method to optimize models and reduce costs 14 .…”

An Exploratory Study Gathering Security Requirements for the Software Development Process

“…Proactive security strategies require continuous monitoring to assess and detect potential vulnerabilities that can be exploited by attackers. For example, Zhou et al [22] propose a quantum neural network for software vulnerability detection. The proposal is based on the premise that the rapid growth of 5G networks increases the scale of unknown vulnerabilities.…”