A study on detection and defence of malicious code under network security over biomedical devices

Liu, Tianqing; Neware, Rahul; Bhatt, Mohammed Wasim; Shabaz, Mohammad

doi:10.1049/tje2.12153

Cited by 3 publications

(2 citation statements)

References 43 publications

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“…Literature has proposed and implemented a taint analysis method based on offline indexing of execution traces. Taking bytes as the granularity, the problem of taint loss caused by instant translation execution was described and solved for the first time [15]. Literature has implemented a novel approach to static taint analysis that is accurate and scalable, treating taint analysis as a demand-driven problem that only computes vulnerable information flows instead of computing complete data flow solutions.…”

Section: Related Workmentioning

confidence: 99%

An Optimized Systematic Approach to Identify Bugs in Cloud-Based Software

Marappan

Kollu²,

Keshta

et al. 2022

Scientific Programming

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The resolution of a software bug depends on the severity of the defect report. Open-source software defect tracking solutions have taken over as the principal means of processing enormous amounts of defect information data due to the ongoing increase in software scale. Dealing with software faults requires analyzing the implications of defect report severity in the data warehouse. Thus, the authors have proposed an optimized systematic approach through the research and analysis of Bugzilla defect tracking system data in this study, where it is found that the attribute characteristics of different projects are quite different and the statistical features of the repair rate, resolution time, developers, components, and other attributes are consistent. This technique, therefore, assumes that a rise in the severity of software defect reports will result in a rise in the defect repair rate and that the severity is normally based on the severity distribution of various components and projects. According to the study’s findings, developers hold the most defects when the repair rate is low and the defect resolution time is shortest.

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Section: Related Workmentioning

confidence: 99%

An Optimized Systematic Approach to Identify Bugs in Cloud-Based Software

Marappan

Kollu²,

Keshta

et al. 2022

Scientific Programming

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“…However, physical resources like storage and processing power are heavily taxed by deploying IoT and auxiliary computing. All of these factors contribute to the ineffectiveness of generalized monitoring [4]. A subset of the IoT enables online realtime communication between gadgets by linking up machines and physical objects all over the globe in real time, and this facilitates web administration and dealing with a broad variety of tasks.…”

Section: Introductionmentioning

confidence: 99%

Implementing Machine Learning-based Autonomic Cyber Defense for IoT-enabled Healthcare Devices

Manimaran¹,

Figueroa

et al. 2023

JAIT

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Smart homes present a serious challenge for the aged and those with mobility issues due to the environment's inherent danger. Unwary people have the propensity to fall over when bending over in these settings. Here, they show two time-based reasoning models to identify incidents of potentially fatal falls that have not been accounted for (CM-I and CM-II). The ubiquitous use of IoT altimeter watches among the elderly provides a wealth of data that can be used by these algorithms to predict the likelihood of a fall based on categorization criteria. They compared actual and simulated data involving missteps, mishaps, and crashes to gauge the programmers’ performance. Results suggest that using such logic models to help healthcare providers determine if senior people living in smart homes have fallen is a potential field for future study. The CM-II model had the highest prediction accuracy of any model identified in the literature, at 0.98 when compared to the test parameter. Since the number of devices linked to the IoT can be quickly extended in contrast to the number of devices connected to conventional computers, the number of hacks aimed at the IoT has grown dramatically. There is no way to fix the issue that hacked IoT devices create until they figure out how to track down the source of the attacks. Pursuing a deeper understanding of the technologies, protocols, and architecture of IoT systems, as well as the potential consequences of using infected IoT devices, is the overarching goal of this study. There are many Internet of Things (IoT) systems vulnerable to cybercriminal manipulation, so this study also explores a range of machine learning and deep learning-based methods that can be used to detect such compromise.

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Physical Layer Security for 6G: Toward Achieving Intelligent Native Security at Layer-1

Ara,

Kelley

2024

IEEE Access

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A study on detection and defence of malicious code under network security over biomedical devices

Cited by 3 publications

References 43 publications

An Optimized Systematic Approach to Identify Bugs in Cloud-Based Software

An Optimized Systematic Approach to Identify Bugs in Cloud-Based Software

Implementing Machine Learning-based Autonomic Cyber Defense for IoT-enabled Healthcare Devices

Physical Layer Security for 6G: Toward Achieving Intelligent Native Security at Layer-1

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