Root Exploit Detection and Features Optimization: Mobile Device and Blockchain Based Medical Data Management

Abstract

The increasing demand for Android mobile devices and blockchain has motivated malware creators to develop mobile malware to compromise the blockchain. Although the blockchain is secure, attackers have managed to gain access into the blockchain as legal users, thereby comprising important and crucial information. Examples of mobile malware include root exploit, botnets, and Trojans and root exploit is one of the most dangerous malware. It compromises the operating system kernel in order to gain root privileges which are then used by attackers to bypass the security mechanisms, to gain complete control of the operating system, to install other possible types of malware to the devices, and finally, to steal victims' private keys linked to the blockchain. For the purpose of maximizing the security of the blockchain-based medical data management (BMDM), it is crucial to investigate the novel features and approaches contained in root exploit malware. This study proposes to use the bio-inspired method of practical swarm optimization (PSO) which automatically select the exclusive features that contain the novel android debug bridge (ADB). This study also adopts boosting (adaboost, realadaboost, logitboost, and multiboost) to enhance the machine learning prediction that detects unknown root exploit, and scrutinized three categories of features including (1) system command, (2) directory path and (3) code-based. The evaluation gathered from this study suggests a marked accuracy value of 93% with Logitboost in the simulation. Logitboost also helped to predicted all the root exploit samples in our developed system, the root exploit detection system (RODS).

A Decentralized Peer-to-Peer Remote Health Monitoring System

“…Guardtime [32] is regularly cited as the most successful medical record-keeping project, and it secures over a million patient records in Estonia. To secure medical records collected at the patients' end through mobile devices, the authors in [33] propose a blockchain-based data management system, with practical swarm optimization for root exploit detection and feature optimization.…”

“…Fernandez et al [42] propose cryptocurrency for incentivizing users to crowdsource medical data for health monitoring as well as medical research. To secure medical records collected at the patients' end through mobile devices, the authors in [33] propose a blockchain-based data management system, with practical swarm optimization for root exploit detection and feature optimization. Dwivedi et al [43] leverage the security features of blockchains to develop a health monitoring system; however, data transactions all occur over the blockchain, which is not a scalable solution.…”

Phishing detection system using nachine learning classifiers

“…Deceptive phishing is the messages required to verify account information, requesting that users re-enter their 1167 information, bogus account charges, unwanted account changes, new free services requiring immediate action, and many other malicious sites are sent to many recipients in the hope that the unsuspecting person will react by clicking on a link to or signing on a fake site [15]. Malware-based phishing refers to attacks that lead to the installation and execution of malicious software on computers of users [18,19]. Malware is generally introduced as an email attachment that can be downloaded.…”

‘Fit-for-purpose?’ – challenges and opportunities for applications of blockchain technology in the future of healthcare

“…Beyond these core features, blockchains can also enable other technologies such as distributed applications (those that run on multiple computers in a network) and smart contracts (computer code that can execute terms of a contract between parties) as well as the use of cryptocurrencies (digital or virtual currencies) [2, 6–8]. Blockchains can also act as a digital backbone for other technologies able to interface with blockchain systems such as cloud computing, artificial intelligence, eHealth and mHealth devices/applications, and the broader Internet of Medical Things (IoMT) [6, 9, 10]. Thus, the blockchain environment is expansive and modular, and has the flexibility to be adopted for various use cases in healthcare and beyond (see technology architecture summary in Fig.…”