Root Exploit Detection and Features Optimization: Mobile Device and Blockchain Based Medical Data Management.
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...
| Published in: | Journal of Medical Systems Vol. 42; no. 6; pp. 1 - 2 |
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| Main Authors: | , , , , , |
| Format: | equations & formulas pictorial research tables/charts Journal Article |
| Published: |
Springer Nature
Jun2018
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=129928841&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 129928841 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Jun2018 vid: 42 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 129928841 129928841 129928841 10.1007/s10916-018-0966-x 129928841 ppf: 1 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Root Exploit Detection and Features Optimization: Mobile Device and Blockchain Based Medical Data Management. aug: au: Firdaus, Ahmad Anuar, Nor Badrul Razak, Mohd Faizal Ab Hashem, Ibrahim Abaker Targio Bachok, Syafiq Sangaiah, Arun Kumar affil: Faculty of Computer Systems and Software Engineering, Universiti Malaysia Pahang, 26300, Kuantan, Pahang, Malaysia sug: subj: Computer Viruses Data Security Methods Computers, Hand-Held Health Information Machine Learning Mobile Applications Human Funding Source ab: 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). pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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