## 2 Related Work
### Intrusion Detection Systems  
**Network IDS:** Machine learning-based approaches and, in particular, deep learning (DL) methods [39; 40; 41; 42], are being used to produce cybersecurity solutions for securing communication for the Internet of Things and cyber-physical systems in the face of intrusions. To detect GPS spoofing, [PERSON] et al. [43] used supervised learning and artificial neural networks and [PERSON] et al. [44] developed a semi-supervised and unsupervised framework for intrusion detection. To increase the accuracy of classification, essential characteristics, including Doppler shift and SNRratio, have been chosen using feature engineering on the well-known KDD Cup 99 dataset and its variations [45; 46]. To give an IDS improved generalization capabilities, [PERSON] et al. [45] combined group convolution networks with snapshot ensemble learning. In another work, an IDS for UAV networks was created by [PERSON] et al. [47] using the LSTM recurrent neural network. A further advanced strategy that combines XGBoost and DNN was proposed by [PERSON] et al. [46]. While the deep neural network is used to create the classification, XGBoost is useful for extracting features and dimensionality reductions. A hybrid optimization-driven ensemble classification was created in [48] using a fog computing environment that combines multiple individual classifiers in order to improve overall prediction accuracy. A hybrid approach proposed in [49] combines association rule mining and classification methods to enhance the privacy and security levels of the network environment and improve the accuracy of intrusion detection. The aim of the study in [50] is to examine and assess intrusion detection systems designed for Agriculture 4.0 cybersecurity. The focus is on discussing the cybersecurity threats that these systems face and the metrics used to evaluate their performance.  
**UAV IDS:** In critical situations, UAV systems are frequently utilized to communicate crucial data. The limits of the UAV infrastructure's computing and communication capacities, meanwhile, make them vulnerable to intrusions and attacks. Intrusion detection systems for UAVs or UAV IDS are often designed to recognize a variety of anomalies and vulnerability types, including malware such as ransomware, signals, route modification, tracking attacks, and GPS spoofing [51] in UAV systems. [PERSON] et al. [52] conducted a thorough study of machine learning algorithms for dealing with different UAV challenges, including channel modeling and resources [53], and employed LSTM and convolutional neural network techniques to create spatial-temporal deep learning on communication graphs. [PERSON] et al. [41] studied an unsupervised learning-based technique to detect persistent spoofing in UAV interconnected networks. In contrasting to typical machine learning frameworks, the use of deep learning has greatly improved the detection accuracy for recently created intrusions that are challenging to identify using conventional machine learning methods [54]. Automated hyperparameter optimization (HPO) has grown in popularity in both research and commercial applications as a way to remove the impediments for average consumers [55]. Intrusion detection based on hyperparameter optimization has been previously investigated [56] and has shown to be very promising. The research in [57] discusses an agricultural information security framework that incorporates UAV technology and machine learning for data collection. The integration of these technologies promotes the development of the Internet and information security. The paper also proposes the use of a Double Deep Q-network algorithm to efficiently optimize the deployment of UAVs and a smart agricultural information management system for intrusion detection. We extended traditional hyperparameter optimization methods and used an advanced hyperparameter optimization technique for intrusion detection in this research.