ABSTRACT Underwater wireless sensor networks (UWSNs) have been used increasingly for critical tasks such as environment surveillance and underwater exploration. Nevertheless, their peculiar working environment, which entails their use of acoustic communication, high message latency, low bandwidth, mobility, stringent energy resources, and noisy communication channels, renders them susceptible to complex cyber threats including Sybil, Denial of Service (DoS), and traffic analysis attacks. This reality is a major drawback to the use of traditional intrusion detection systems used in other wireless communication networks. This manuscript addresses these issues through the presentation of an optimized cyberattack detection framework in UWSNs using an equivariant quantum convolutional neural network integrated with flamingo jellyfish search optimization (CD‐UWSN‐EQCNN‐FJSO). In this approach, network traffic data from the NSL‐KDD dataset are normalized using Bayesian boundary trend filtering (BBTF) to handle noise and uncertainty. Bitterling fish optimization (BFO) is then applied for feature selection, with further statistical feature extraction made by the double probability integral transform (DPIT). Here, the designed equivariant quantum convolutional neural network (EQCNN) is well leverage equivariance properties to perform robust detection under dynamic underwater network conditions, while the flamingo jellyfish search optimization (FJFO) approach dynamically fine‐tunes the weights within the network for improved detection accuracy and lower false alarm rates. The experimental results indicate that the proposed CD‐UWSNs‐EQCNN‐FJSO approach is able to provide much 27.15%, 26.09%, and 28.10% higher accuracy and 29.03%, 25.23%, and 29.1% higher recall capabilities, as well as much lower rates of 20.09%, 22.24%, and 20.01% lower false positive rate than other UWSN‐based cyber security solutions available in the existing methods.
Chenthil et al. (Mon,) studied this question.