  Quantum computing holds tremendous potential for processing high-dimensional
data, capitalizing on the unique capabilities of superposition and parallelism
within quantum states. As we navigate the noisy intermediate-scale quantum
(NISQ) era, the exploration of quantum computing applications has emerged as a
compelling frontier. One area of particular interest within the realm of
cyberspace security is Behavior Detection and Evaluation (BDE). Notably, the
detection and evaluation of internal abnormal behaviors pose significant
challenges, given their infrequent occurrence or even their concealed nature
amidst vast volumes of normal data. In this paper, we introduce a novel quantum
behavior detection and evaluation algorithm (QBDE) tailored for internal user
analysis. The QBDE algorithm comprises a Quantum Generative Adversarial Network
(QGAN) in conjunction with a classical neural network for detection and
evaluation tasks. The QGAN is built upon a hybrid architecture, encompassing a
Quantum Generator ($G_Q$) and a Classical Discriminator ($D_C$). $G_Q$,
designed as a parameterized quantum circuit (PQC), collaborates with $D_C$, a
classical neural network, to collectively enhance the analysis process. To
address the challenge of imbalanced positive and negative samples, $G_Q$ is
employed to generate negative samples. Both $G_Q$ and $D_C$ are optimized
through gradient descent techniques. Through extensive simulation tests and
quantitative analyses, we substantiate the effectiveness of the QBDE algorithm
in detecting and evaluating internal user abnormal behaviors. Our work not only
introduces a novel approach to abnormal behavior detection and evaluation but
also pioneers a new application scenario for quantum algorithms. This paradigm
shift underscores the promising prospects of quantum computing in tackling
complex cybersecurity challenges.
