Zero-Crossing Analysis of Lévy Walks and a DDoS Dataset for Real-Time Feature Extraction

Kinsner, W.; Gonzalez, Jesus David Terrazas

doi:10.4018/ijssci.2016100101

Cited by 3 publications

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Forewarned is forearmed

Bharathi

2019

BIJ

View full text Add to dashboard Cite

Purpose Internet of Things (IoT) interconnects many heterogeneous devices to each other, collecting and processing large volumes of data for decision making without human intervention. However, the information security concern it brings has attracted quite a lot of attention, and, at this stage, the smart step would be to analyze the security issues of IoT platform and get to the state of readiness before embarking upon this attractive technology. The purpose of this paper is to address these issues. Design/methodology/approach IoT risk assessment through the application of the analytical hierarchy process (AHP), a favorite multi-criteria decision making technique, is proposed. The IoT risks are prioritized and ranked at different layers, before which a well-defined IoT risk taxonomy is defined comprising of 25 risks across six layers of the IoT model for developing control and mitigation plans for information security of IoT. Findings People and processes layer, network layer and applications layer are the top three critical layers with risks like the lack of awareness, malware injection, malicious code injection, denial of service and inefficient policies for IoT practice get the highest priority and rank. Pareto analysis of the overall risk factors revealed that the top ten factors contribute to 80 percent of the risks perceived by information security experts. Research limitations/implications The study focuses only on certain predefined constructs or layers of the IoT model traced from legacy studies. It is essential to re-look these constructs on a timely basis to prolong the results’ validity. The study’s empirical scope is confined only to the risk perception of select IoT experts and does not encompass a broader segment of the IoT ecosystem. Therefore, the risks assessment may not be sweeping to a bigger audience. Practical implications The study implications are two-fold: one it consolidates the earlier siloed works to intensify the need for risk assessment in the IoT domain, and second the study brings yet another contextual avenue of extending the application AHP and Pareto principle combination. The paper also draws specific critical organizational interventions about IoT risks. A comprehensive approach to prioritizing and ranking IoT risks are present in this research paper. Originality/value The contribution of this study to the benchmarking of IoT risk assessment is two-fold. One, a comprehensive risk assessment taxonomy is proposed, and two, the risks are prioritized and ranked to give a convincing reference for the organizations while making information security plans for IoT technology.

show abstract

Forewarned is forearmed

Bharathi

2019

BIJ

View full text Add to dashboard Cite

show abstract

DDoS Attacks on Various Platforms

Gupta¹,

Dahiya²

2021

Distributed Denial of Service (DDoS) Attacks

View full text Add to dashboard Cite

An Innovative Model for Detecting Brain Tumors and Glioblastoma Multiforme Disease Patterns

Guo¹,

Bhattacharya

2017

International Journal of Software Science and Computational Intelligence

View full text Add to dashboard Cite

In this article, an innovative model is proposed for detecting brain tumors and glioblastoma multiforme disease patterns (DBT-GBM) in medical imaging. The DBT-GBM model mainly includes five steps, the image conversion in the L* component of the L*a*b* space, an image sample region selection, calculation of the average values of colors, image pixel classification using the minimum distance classifier and the segmentation operation. In the approach, the minimum distance classifier is used to classify each pixel by calculating the Euclidean distance between that pixel and each color marker of the pattern. In the experiments, the authors implement the DBT-GBM model into real-time data, the samples of three anatomic sections of a T1w 3D MRI (axial, sagittal and coronal cross-sections) on the GBM-3D-Slicer datasets and the CBTC datasets. The implementation results show that the proposed DBT-GBM robustly detects the GBM disease patterns and cancer nuclei (involving the omics indicative of brain tumors pathologically) in medical imaging, leading to improved segmentation performance in comparison.

show abstract

Zero-Crossing Analysis of Lévy Walks and a DDoS Dataset for Real-Time Feature Extraction

Cited by 3 publications

References 56 publications

Forewarned is forearmed

Forewarned is forearmed

DDoS Attacks on Various Platforms

An Innovative Model for Detecting Brain Tumors and Glioblastoma Multiforme Disease Patterns

Contact Info

Product

Resources

About