{ "ok": true, "doi": "10.46243/jst.2023.v8.i12.pp01-6", "doi_display": "10.46243/jst.2023.v8.i12.pp01-6", "doi_url": "https://doi.org/10.46243/jst.2023.v8.i12.pp01-6", "state": "registered", "url": "https://www.jst.org.in/index.php/pub/article/view/817", "title": "A METHOD OF PREDICTING WITH MODELLING OF CYBER HACKING AND BREACHES", "version": 2, "registered_via": "crossref", "prefix": { "prefix": "10.46243", "status": "live" }, "registrant": { "name": "Longman Publishers", "kind": "publisher", "country": "India" }, "reserved_at": null, "registered_at": "2026-09-29 22:00:28", "updated_at": "2026-09-29 23:59:47", "withdrawn_at": null, "withdrawn_reason": null, "record": { "format": "smartscholars-doi-metadata/1.0", "doi": "10.46243/jst.2023.v8.i12.pp01-6", "referent": "Creation", "type": "JournalArticle", "structural_type": "Digital", "modes": [ "Visual" ], "characters": [ "Language" ], "titles": [ { "value": "A METHOD OF PREDICTING WITH MODELLING OF CYBER HACKING AND BREACHES", "type": "PrincipalTitle", "lang": "en" } ], "identifiers": [ { "type": "DOI", "value": "10.46243/jst.2023.v8.i12.pp01-6" } ], "agents": [ { "role": "author", "name": { "given": "D. Srinija", "family": "D. Srinija" }, "sequence": "first" }, { "role": "publisher", "name": { "org": "Longman Publishers" } } ], "dates": { "published": "2023-12-12", "date_type": "PublicationDate", "online": "2023-12-12" }, "language": "en", "container": { "type": "Journal", "titles": [ { "value": "Journal of Science & Technology", "type": "PrincipalTitle" } ], "identifiers": [ { "type": "ISSN", "value": "2456-5660", "medium": "electronic" } ], "volume": "8", "issue": "12", "pages": { "first": "1", "last": "6" } }, "links": [ { "url": "https://www.jst.org.in/index.php/pub/article/view/817", "return_type": "text/html", "primary": true }, { "url": "https://www.jst.org.in/index.php/pub/article/download/817/751", "purpose": "text-mining", "return_type": "application/pdf" }, { "url": "https://www.jst.org.in/index.php/pub/article/download/817/1700", "purpose": "text-mining", "return_type": "application/xml" }, { "url": "https://jst.org.in/admin/uploads/Batch%203%20CS.pdf", "purpose": "similarity-checking" } ], "abstract": { "value": "and identity theft. a data breach occurs when a cybercriminal successfully infiltrates a data source and extracts sensitive information. this can be done physically by accessing a computer or network to steal local files or by bypassing network security remotely. data breaches are becoming more and more common and some of the most recent data breaches have been the largest on record to date. DATA breaches are one of the most devastating cyber incidents. The Privacy Rights Clearinghouse reports 7,730 data breaches between 2005 and 2017, accounting for 9,919,228,821 breached records. The Identity Theft Resource Center and Cyber Scout reports 1,093 data breach incidents in 2016, which is 40% higher than the 780 data breach incidents in 2015.Data breaches expose 4.1 billion records in first six month of 2019.the first six month of 2019 have seen more than 3800 publicly disclosed breaches 1,2,3B. Tech Student, Department of CSE (Cyber Security), Malla Reddy College of Engineering and Technology, Hyderabad, India. 4Professor, Department of CSE (Cyber Security), Malla Reddy College of Engineering and Technology, Hyderabad, India. DOI:https://doi.org/10.46243/jst.2023.v8.i12.pp1-6 2 D. Srinija, K. Neeraj, C. Rajeshwari, Dr. P . Dileep: A METHOD OF PREDICTING WITH MODELLING OF CYBER HACKING AND BREACHES exposing an incredible 4.1 billion compromised records. In 2019,the number of data breaches in the united states amounted to 1,473 with over 164.68 million sensitive records exposed. data breaches have gained attention with the increasing use of digital files and companies and users large reliance on digital data. State of breach January 2020:at least 7.9 billion records, including credit card numbers, home addresses, phone numbers and other highly sensitive information, have been exposed through data breaches since 2019. EXISTING METHOD The present study is motivated by several questions that have not been investigated until now, such as: Are data breaches caused by cyber-attacks increasing, decreasing, or stabilizing? A principled answer to this question will give us a clear insight into the overall situation of cyber threats. This question was not answered by previous studies. Specifically, the dataset analyzed in only covered the time span from 2000 to 2008 and does not necessarily contain the breach incidents that are caused by cyber-attacks; the dataset analyzed in is more recent, but contains two kinds of incidents: negligent breaches (i.e., incidents caused by lost, discarded, stolen devices and other reasons) and malicious breaching. Since negligent breaches represent more human errors than cyber-attacks, we do not consider them in the present study. Because the malicious breaches studied in [9] contain four sub-categories: hacking (including malware), insider, payment card fraud, and unknown, this study will focus on the hacking sub-category (called hacking breach dataset thereafter), while noting that the other three subcategories are interesting on their own and should be analyzed separately.Recently, researchers started modeling data breach incidents. Maillart and Sornette studied the statistical properties of the personal identity losses in the United States between year 2000 and 2008. They found that the number of breach incidents dramatically increases from 2000 to July 2006 but remains stable thereafter. Edwards et al. analyzed a dataset containing 2,253 breach incidents that span over a decade (2005 to 2015). They found that neither the size nor the frequency of data breaches has increased over the years. Wheatley et al., analyzed a dataset that is combined from corresponds to organizational breach incidents between year 2000 and 2015. They found that the frequency of large breach incidents (i.e., the ones that breach more than 50,000 records) occurring to US firms is independent of time, but the frequency of large breach incidents occurring to non-US firms exhibits an increasing trend. PROPOSED SYSTEM In this paper, we make the following three contributions. First, we show that both the hacking breach incident inter arrival times (reflecting incident frequency) and breach sizes should be modeled by stochastic processes, rather than by distributions. We find that a particular point process can adequately describe the evolution of the hacking breach incidents inter-arrival times and that a particular ARMA-GARCH model can adequately describe the evolution of the hacking breach sizes, where ARMA is acronym for “Auto Regressive and Moving Average” and GARCH is acronym for “Generalized Auto Regressive Conditional Hetero skedasticity.” We show that these stochastic process models can predict the inter-arrival times and the breach sizes. To the best of our knowledge, this is the first paper showing that stochastic processes, rather than distributions, should be used to model these cyber threat factors. Second, we disc", "lang": "en" }, "license": { "url": "https://creativecommons.org/licenses/by/4.0/", "start": "2023-12-12", "applies_to": "vor" }, "references": [ { "key": "ref1", "unstructured": "P. R. Clearinghouse. Privacy Rights Clearinghouse’s Chronology of Data Breaches. Accessed: Nov. 2017. [Online]. Available: https://www.privacyrights.org/data-breaches" }, { "key": "ref2", "unstructured": "ITR Center. Data Breaches Increase 40 Percent in 2016, Finds New Report From Identity Theft Resource Center and CyberScout. Accessed: Nov. 2017. [Online]. Available: http://www. idtheftcenter.org/ 2016databreaches.html" }, { "key": "ref3", "unstructured": "C. R. Center. Cybersecurity Incidents. Accessed: Nov. 2017. [Online]. Available: https:// www.opm.gov/cybersecurity/cybersecurityincidents" }, { "key": "ref4", "unstructured": "IBM Security. Accessed: Nov. 2017. [Online]. 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Forrest, “Hype and heavy tails: A closer look at data breaches,” J. Cybersecur., vol. 2, no. 1, pp. 3–14, 2016" }, { "key": "ref10", "doi": "10.1140/epjb/e2015-60754-4", "unstructured": "S. Wheatley, T. Maillart, and D. Sornette, “The extreme risk of personal data breaches and the erosion of privacy,” Eur. Phys. J. B, vol. 89, no. 1, p. 7, 2016" }, { "key": "ref11", "doi": "10.1007/978-3-642-33483-2", "unstructured": "P. Embrechts, C. Klüppelberg, and T. Mikosch, Modelling Extremal Events: For Insurance and Finance, vol. 33. Berlin, Germany: Springer-Verlag, 2013" }, { "key": "ref12", "unstructured": "R. Böhme and G. Kataria, “Models and measures for correlation in cyber-insurance,” in Proc. Workshop Econ. Inf. Secur. (WEIS), 2006, pp. 1–26" }, { "key": "ref13", "unstructured": "H. Herath and T. Herath, “Copula-based actuarial model for pricing cyber-insurance policies,” Insurance Markets Companies: Anal. Actuarial Comput., vol. 2, no. 1, pp. 7–20, 2011. Fig.3.5. 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