# A Review of Digital Twin Technology for Electric and Autonomous Vehicles
## 4 Current Issues in Electric Vehicles
### Intrusion Detection Systems  
Network security has become a critical research subject due to the advanced development of internet and communication technologies over the previous decade. It uses firewalls, antivirus software, and intrusion detection systems (IDSs) to keep the network and its assets safe in cyberspace. Therefore, researchers have conducted many studies on network security, with various approaches and technologies employed to develop algorithms for detecting unusual activities on different network platforms [77].  
To meet the network security needs in electromobility, various IDS have been developed. IDS is a security framework that continuously monitors network traffic to detect any abnormal behavior that may violate the network policy and threaten its confidentiality, integrity, or availability. The vehicular IDS architecture proposed by [PERSON] et al. [78] ensures onboard IDS, collaborative detection, and offloaded intrusion detection; onboard IDS is where the vehicle identifies illegal behavior on networks on its own; collaborative detection is where vehicles collaborate to determine whether they are under attack or not; offloaded intrusion detection is where the detection process is conducted in the cloud. In this context, [PERSON] et al. [79] developed end-to-end intrusion detection using a DL algorithm to detect malware intrusions for onboard units. Different from previous intrusion detection methods, the proposed method only requires raw traffic instead of private information features extracted by humans. The performance was compared with previous methods on a public dataset and a simulated real-life VANET dataset. The experimentations show that the model can achieve higher performance with minimum resource requirements.  
[PERSON] et al. [80] introduced a trust-aware-based IDS; they used the combination of a modified promiscuous mode and support vector machine to ensure the safety of vehicles and detect malicious behavior in any network node. [PERSON] et al. [81] proposed an intelligent IDS model based on the hidden Markov methodology to filter out malicious messages and reduce the detection time without affecting the detection rate. The authors claim that it is the first work in the literature to model the state pattern of each vehicle in VANET as a hidden Markov model (HMM) to quickly filter the messages from the vehicles instead of detecting these messages. It consists of three modules: schedule, filter, and update. In the schedule module, the Baum-Welch algorithm is used to produce a HMM and its parameters for each neighbor vehicle. In the filter module, multiple HMMs are used with their parameters to forecast the future states of neighbor vehicles in which the messages from them are filtered. In the update module, a timeliness method is used to update HMMs and their parameters. The performed experiments show that the IDS with FM-HMM performs better in terms of the detection rate, detection time, and overhead. [PERSON] et al. [82] designed an efficient, lightweight IDS simulator for the vehicular network. The model can protect vehicular ad hoc networks against denial-of-service attacks, false alert generation, and integrity target attacks. They used the NS-3 simulator (a discrete event network simulator for Internet systems) to present the detection mechanism's performance analysis. The simulation framework shows high-level security and an accurate detection rate.