A Risk-Driven Model to Minimize the Effects of Human Factors on Smart Devices

Gupta, Sandeep; Buriro, Attaullah; Crispo, Bruno

doi:10.1007/978-3-030-39749-4_10

Cited by 4 publications

(2 citation statements)

References 32 publications

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“…Attacks on EC systems can be surfaced due to human errors or perpetuated by insiders and adversaries. The stimuli for cyber-attacks could be the system's limitations, incorrect configurations, design flaws, vulnerabilities, and inability to analyze potential threats to them [93]. The artifacts, i.e., attack targets, can be virtualization technologies, IoT applications, data storage, network connections, interfaces, and connectors across the EC layers.…”

Section: System's Availabilitymentioning

confidence: 99%

Non-functional requirements elicitation for edge computing

Gupta

2022

Internet of Things

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Section: System's Availabilitymentioning

confidence: 99%

Non-functional requirements elicitation for edge computing

Gupta

2022

Internet of Things

Self Cite

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“…For example, in a recent work, authors have discussed the human factors (misconfiguration of various features such as permissions, network access etc.) for Android security threats and proposed solutions to minimize human roles by analyzing the granted security at runtime and revoking them as required; many of these misconfigurations are also caused by the physical appearance of the smartphone [5].…”

Section: Introductionmentioning

confidence: 99%

PACER: Platform for Android Malware Classification, Performance Evaluation and Threat Reporting

Kumar¹,

Agarwal

Shandilya³

et al. 2020

Future Internet

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Android malware has become the topmost threat for the ubiquitous and useful Android ecosystem. Multiple solutions leveraging big data and machine-learning capabilities to detect Android malware are being constantly developed. Too often, these solutions are either limited to research output or remain isolated and incapable of reaching end users or malware researchers. An earlier work named PACE (Platform for Android Malware Classification and Performance Evaluation), was introduced as a unified solution to offer open and easy implementation access to several machine-learning-based Android malware detection techniques, that makes most of the research reproducible in this domain. The benefits of PACE are offered through three interfaces: Representational State Transfer (REST) Application Programming Interface (API), Web Interface, and Android Debug Bridge (ADB) interface. These multiple interfaces enable users with different expertise such as IT administrators, security practitioners, malware researchers, etc. to use their offered services. In this paper, we propose PACER (Platform for Android Malware Classification, Performance Evaluation, and Threat Reporting), which extends PACE by adding threat intelligence and reporting functionality for the end-user device through the ADB interface. A prototype of the proposed platform is introduced, and our vision is that it will help malware analysts and end users to tackle challenges and reduce the amount of manual work.

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