Big Data Sanitization and Cyber Situational Awareness: A Network Telescope Perspective

BOU-HARB, Elias, Martin HUSÁK, Mourad DEBBABI a Chadi ASSI. Big Data Sanitization and Cyber Situational Awareness: A Network Telescope Perspective. IEEE Transactions on Big Data. IEEE, 2019, roč. 5, č. 4, s. 439-453. ISSN 2332-7790. Dostupné z: https://dx.doi.org/10.1109/TBDATA.2017.2723398.

Základní údaje

• Originální název: Big Data Sanitization and Cyber Situational Awareness: A Network Telescope Perspective
• Autoři: BOU-HARB, Elias (124 Kanada), Martin HUSÁK (203 Česká republika, garant, domácí), Mourad DEBBABI (124 Kanada) a Chadi ASSI (124 Kanada).
• Vydání: IEEE Transactions on Big Data, IEEE, 2019, 2332-7790.

Další údaje

• Originální jazyk: angličtina
• Typ výsledku: Článek v odborném periodiku
• Obor: 10201 Computer sciences, information science, bioinformatics
• Stát vydavatele: Spojené státy
• Utajení: není předmětem státního či obchodního tajemství
• WWW: URL
• Kód RIV: RIV/00216224:14610/19:00108740
• Organizační jednotka: Ústav výpočetní techniky
• Doi: http://dx.doi.org/10.1109/TBDATA.2017.2723398
• UT WoS: 000501301600003
• Klíčová slova anglicky: Darknet sanitization;Time series analytics;Security analytics;Cyber threat intelligence
• Štítky: J-Q1, rivok
• Příznaky: Mezinárodní význam, Recenzováno

Anotace

This paper addresses the problems of data sanitization and cyber situational awareness by analyzing 910 GB of real Internet-scale traffic, which has been passively collected by monitoring close to 16.5 million darknet IP addresses from a /8 and a /13 network telescopes. First, the paper offers a novel probabilistic darknet preprocessing model, which aims at sanitizing darknet data to prepare it for effective use in the task of cyber threat intelligence generation. Such model has been engineered using a distributed multithreaded approach, rendering it highly effective on darknet big data. Second, the paper further contributes by presenting an innovative approach to infer large-scale orchestrated probing campaigns by leveraging darknet data, for Internet cyber situational awareness. The approach uniquely reduces the dimensionality of such big data by utilizing its artifacts, instead of processing the actual raw data. This is accomplished by extracting and analyzing probing time series using formal methods rooted in Fourier transform and Kalman filtering. Thorough empirical evaluations indeed validate the accuracy and the performance of the proposed methods. We assert that such approaches are of significant value, given their highly applicable nature to the field of Internet measurements for cyber security in the era of big data.