Forecasting Cyber Hacking Breaches using Time Series Analysis
G.S.C.Sirisha
, V. Sai Karthika , M. Radhika Mani
Breach, Time Series, ARIMA, forecast, Seasonality, Trend
Analyzing cyber incident data sets is an important method for deepening our understanding of the evolution of the present threat situation. This is a relatively new research topic, and many studies remain to be done. In this paper, we report a statistical analysis of a breach incident data set corresponding to 15 years (2005–2019) of cyber hacking activities that include malware attacks. We show that, in contrast to the findings reported in the literature, both hacking breach incident inter-arrival times and breach sizes should be modelled by stochastic processes, rather than by distributions because they exhibit autocorrelations. Then, we propose particular stochastic process models to, respectively, fit the inter-arrival times and the breach sizes (total number of records breached). We show that these models successfully predict the breach sizes. In order to get deeper insights into the evolution of hacking breach incidents, we conduct both qualitative and quantitative trend analyses on the data set. We draw a set of cyber security insights, including that the threat of cyber hacks is indeed getting worse in terms of their frequency, but not in terms of the magnitude of their damage.
"Forecasting Cyber Hacking Breaches using Time Series Analysis", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.5, Issue 5, page no.180 - 188, May-2020, Available :https://ijsdr.org/papers/IJSDR2005031.pdf
Volume 5
Issue 5,
May-2020
Pages : 180 - 188
Paper Reg. ID: IJSDR_191730
Published Paper Id: IJSDR2005031
Downloads: 000347287
Research Area: Engineering
Country: Rajamundry, Andhra Pradesh, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
