Using a Prediction Model to Manage Cyber Security Threats.

ScientificWorldJournal

Department of Management Studies, Anna University Regional Centre Coimbatore, Jothipuram Post, Coimbatore, Tamilnadu 641 047, India.

Published: January 2016


Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article Abstract

Cyber-attacks are an important issue faced by all organizations. Securing information systems is critical. Organizations should be able to understand the ecosystem and predict attacks. Predicting attacks quantitatively should be part of risk management. The cost impact due to worms, viruses, or other malicious software is significant. This paper proposes a mathematical model to predict the impact of an attack based on significant factors that influence cyber security. This model also considers the environmental information required. It is generalized and can be customized to the needs of the individual organization.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4433707PMC
http://dx.doi.org/10.1155/2015/703713DOI Listing

Publication Analysis

Top Keywords

cyber security
8
prediction model
4
model manage
4
manage cyber
4
security threats
4
threats cyber-attacks
4
cyber-attacks issue
4
issue faced
4
faced organizations
4
organizations securing
4

Similar Publications

ResDeepGS: A deep learning-based method for crop phenotype prediction.

Methods

September 2025

School of Computer and Information Engineering, Henan University, Kaifeng, Henan, China; Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng, Henan, China. Electronic address:

Genomic selection (GS) is a breeding technique that utilizes genomic markers to predict the genetic potential of crops and animals. This approach holds significant promise for accelerating the improvement of agronomic traits and addressing food security challenges. While traditional breeding methods based on statistical or machine learning techniques have been useful in predicting traits for some crops, they often fail to capture the complex interactions between genotypes and phenotypes.

View Article and Find Full Text PDF

Advances in nanopore direct RNA sequencing and its impact on biological research.

Biotechnol Adv

September 2025

Key Laboratory of Microbiological Metrology, Measurement & Bio-product Quality Security, State Administration for Market Regulation, China Jiliang University, Hangzhou 310018, China. Electronic address:

Nanopore direct RNA sequencing (DRS) is a transformative technology that enables full-length, single-molecule sequencing of native RNA, capturing transcript isoforms and preserving epitranscriptomic modifications without cDNA conversion. This review outlines key advances in DRS, including optimized protocols for mRNA, rRNA, tRNA, circRNA, and viral RNA, as well as analytical tools for isoform quantification, poly(A) tail measurement, fusion transcript identification, and base modification profiling. We highlight how DRS has redefined transcriptomic studies across diverse systems-from uncovering novel transcripts and alternative splicing events in cancer, plants, and parasites to enabling the direct detection of m6A, m5C, pseudouridine, and RNA editing events.

View Article and Find Full Text PDF

Pollination is essential for maintaining biodiversity and ensuring food security, and in Europe it is primarily mediated by four insect orders (Coleoptera, Diptera, Hymenoptera, Lepidoptera). However, traditional monitoring methods are costly and time consuming. Although recent automation efforts have focused on butterflies and bees, flies, a diverse and ecologically important group of pollinators, have received comparatively little attention, likely due to the challenges posed by their subtle morphological differences.

View Article and Find Full Text PDF

Optoelectronic polymer memristors with dynamic control for power-efficient in-sensor edge computing.

Light Sci Appl

September 2025

State Key Laboratory of Flexible Electronics, Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications (NUPT), Nanjing, 210023, China.

As the demand for edge platforms in artificial intelligence increases, including mobile devices and security applications, the surge in data influx into edge devices often triggers interference and suboptimal decision-making. There is a pressing need for solutions emphasizing low power consumption and cost-effectiveness. In-sensor computing systems employing memristors face challenges in optimizing energy efficiency and streamlining manufacturing due to the necessity for multiple physical processing components.

View Article and Find Full Text PDF

The widespread dissemination of fake news presents a critical challenge to the integrity of digital information and erodes public trust. This urgent problem necessitates the development of sophisticated and reliable automated detection mechanisms. This study addresses this gap by proposing a robust fake news detection framework centred on a transformer-based architecture.

View Article and Find Full Text PDF