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The Internet of Medical Things (IoMT) is an extended genre of the Internet of Things (IoT) where the Things collaborate to provide remote patient health monitoring, also known as the Internet of Health (IoH). Smartphones and IoMTs are expected to maintain secure and trusted confidential patient record exchange while managing the patient remotely. Healthcare organizations deploy Healthcare Smartphone Networks (HSN) for personal patient data collection and sharing among smartphone users and IoMT nodes. However, attackers gain access to confidential patient data via infected IoMT nodes on the HSN. Additionally, attackers can compromise the entire network via malicious nodes. This article proposes a Hyperledger blockchain-based technique to identify compromised IoMT nodes and safeguard sensitive patient records. Furthermore, the paper presents a Clustered Hierarchical Trust Management System (CHTMS) to block malicious nodes. In addition, the proposal employs Elliptic Curve Cryptography (ECC) to protect sensitive health records and is resilient against Denial-Of-Service (DOS) attacks. Finally, the evaluation results show that integrating blockchains into the HSN system improved detection performance compared to the existing state of the art. Therefore, the simulation results indicate better security and reliability when compared to conventional databases.

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Android platform security is an active area of research where malware detection techniques continuously evolve to identify novel malware and improve the timely and accurate detection of existing malware. Adversaries are constantly in charge of employing innovative techniques to avoid or prolong malware detection effectively. Past studies have shown that malware detection systems are susceptible to evasion attacks where adversaries can successfully bypass the existing security defenses and deliver the malware to the target system without being detected. The evolution of escape-resistant systems is an open research problem. This paper presents a detailed taxonomy and evaluation of Android-based malware evasion techniques deployed to circumvent malware detection. The study characterizes such evasion techniques into two broad categories, polymorphism and metamorphism, and analyses techniques used for stealth malware detection based on the malware’s unique characteristics. Furthermore, the article also presents a qualitative and systematic comparison of evasion detection frameworks and their detection methodologies for Android-based malware. Finally, the survey discusses open-ended questions and potential future directions for continued research in mobile malware detection.