# Machine-Learning-Enabled Intrusion Detection System for Cellular Connected UAV Networks
## 1 Introduction
Moreover, due to complexity, heterogeneity, and many interconnected resources, providing security in the UAV networks has become a big challenge in space-based networks. Some security issues that imply security in space-based information networks are hand-off security, transmission control security, and routing protocol security, which have been addressed in [13]. In [14], the security requirements of satellite-based wireless networks have been studied systematically, and the appropriate model for attacks are proposed for a satellite platform according to the MIL-STD-1553B bus, which is significantly used in an internal bus of spacecraft. There are two main ways for increasing security in satellite-based UAV networks: (1) encryption algorithms and (2) machine learning (ML) techniques, which are used to mitigate anomalies [15; 16]. Encryption algorithms can protect the networks against external attacks by using authenticated packets from the source node and have two classifications, which are content-based and stream-based. An automation level should be considered in 5G-based UAV networks because of its complexity, volatility, and scalability, which has never been seen earlier. Blockchain, which has been presented as a secure, decentralized, and distributed ledge, r can be used for ensuring satellite security [17; 18]. Unlike previous methods, which uploaded data to a cloud server or stored it in a single location, blockchain creates several small chunks of an original block and distributes them to the different parts of networks. Blockchain can be deployed as a means for providing secure transactions in the network infrastructures; on the other hand, machine learning and artificial intelligence methods can be used to prevent the network from violent threats [19; 20]. Blockchain can encrypt everything that exits in the network so that the data cannot be altered across the transmission. In the encryption-based security methods, the key management scheme consumes energy in nodes, and it is considered a noticeable challenge. Furthermore, these methods should maintain all the keys in the whole network that waste the limited energy of the nodes [21]. Thus, cryptographic approaches increase network cost and node overhead in order to support internal attacks with cryptographic keys. In this paper, we will discuss alternative security solutions based on Intrusion Detection System (IDS) integrated with machine learning methods. Machine learning is a significant method for security protection, which can provide security orchestration required to detect new threats in the UAV networks supported by satellite communication. Moreover, the IDS is effectively useful as a security scheme to increase accuracy in the networks and protect drones against intruders because both internal and external attacks can be accurately detected by IDS [22; 23]. Anomaly detection is one of the IDS approaches for detecting new attacks that had never been seen before that instance. Anomaly detection uses a supervised machine learning (ML) algorithm [24; 25; 26] to create normal data behavior patterns. A real-time model was presented for detection of outgoing Denial of Service (DoS) attacks in [27], in which many ML algorithms are used and compared in terms of speed, accuracy, and weighting calculation. Machine learning algorithms are an important approach to handle the security problems in 5G-based UAV networks [28]. ML is a kind of artificial intelligence that applies various learning techniques to train devices without definite programming. ML can be employed efficiently in the UAV networks for the following reasons: (1) There is no need for a mathematical model for complex UAV environments. (2) Some applications, which require datasets, can be correlated. (3) ML algorithms are able to adjust with the dynamics and unforeseen patterns of UAV systems. (4) ML algorithms can eliminate human interventions, which does not fit for the UAV networks.