## 4 Taxonomy of Smart Healthcare Security Solutions
### Software-Based Attacks  
Software-based attacks directly impact healthcare resources, where an outdated software/firmware/tool/utility/operating system leads to propagating security attacks in smart healthcare systems. They are categorized as malware, outdated software, fake firmware updates, and phishing; their countermeasures are explained below. * Malware--These are malicious executable, files, or code that disrupts the behaviour of the smart healthcare system. Thus, to counter such attacks on the healthcare ecosystem, ref. [82] presented attention and deep learning (DL)-based detection and classification approach to find IoT-based malware in healthcare devices. The authors extract the byte sequence from the malware executable and automate the feature selection. Their proposed approach was evaluated using malware detection and classification accuracy, achieving 95% and 94%, respectively, compared to the existing approaches. Recently, the android-based smartphone and wearables have been popularized by integrating predictive and intelligent services, such as AI-based diet planning recommendations, digital well-being, and tracking vital signs for any particular diseases. Nevertheless, the wearables are susceptible to malware attacks that diminish the performance of predictive services. [PERSON] et al. in [83] designed a secure analysis system for medical wearables. It first performs a matching analysis between smartphones and wearables to ensure that a safe application is installed. Further, they conduct similarity analysis of malicious applications using the oversampling method. Finally, their proposed work is evaluated on the Google play store, where they found 44 applications that have permission mismatches. * Ransomware--Ransomware is a special type of malware that is installed from backdoors or by clicking on an illegitimate web link. It encrypts the sensitive files using asymmetric key encryption, which is decrypted using a private key that the victims can obtain after the ransom is paid. Researchers have adopted AI-based algorithms to tackle ransomware in the smart healthcare sectors. For instance, [PERSON] et al. in [84] stated that modern detection systems are not capable of detecting the anomalies and malware signatures promptly, and by the time these are detected, the ransomware has already infected and exfiltrated a large number of healthcare resources. They studied the behavioural properties (e.g., network activities) of ransomware, especially \"Locky\" ransomware which is one of the most rampant families of ransomware. Subsequently, they developed an intrusion detection system that analyses the packet and flow levels of the network, and based on that; they analysed the ransomware behaviour. Their detection system has better accuracy and low false positive rates and efficiently tracks ransomware network activities. Further, [PERSON] et al. in [85] studied control systems and their security vulnerabilities, especially in terms of ransomware. Smart healthcare systems also use control systems, such as supervisory control and data acquisition (SCADA), to control various items of medical equipment. Ransomware targets different operations performed by the SCADA system to jeopardize the overall performance of the healthcare ecosystem. The authorshighlighted various security loopholes in SCADA systems where the ransomware can be directly attacked; also, it shows different countermeasures to tackle the attack. The authors of [86] proposed a blockchain-based ransomware defence system, where all healthcare devices are associated with the blockchain network. Here, if the attackers lock the system and personal files, the locked system can fetch the data from the blockchain node (data recovery using blockchain backups). The proposed system also saves the ransomware signatures in the blockchain to detect it and prevent it from attacking the smart healthcare systems.