Название: Data Science for Cyber-Security
Автор: Nick Heard, Niall Adams, Patrick Rubin-Delanchy
Издательство: World Scientific Publishing
Серия: Security Science and Technology (Book 3)
Год: 2019
Страниц: 305
Язык: английский
Формат: pdf (true)
Размер: 20.4 MB

Cyber-security is a matter of rapidly growing importance in industry and government. This book provides insight into a range of data science techniques for addressing these pressing concerns.

The application of statistical and broader data science techniques provides an exciting growth area in the design of cyber defences. Networks of connected devices, such as enterprise computer networks or the wider so-called Internet of Things, are all vulnerable to misuse and attack, and data science methods offer the promise to detect such behaviours from the vast collections of cyber traffic data sources that can be obtained. In many cases, this is achieved through anomaly detection of unusual behaviour against understood statistical models of normality.

The lack of data sets derived from operational enterprise networks continues to be a critical deficiency in the cyber-security research community. Unfortunately, releasing viable data sets to the larger community is challenging for a number of reasons, primarily the difficulty of balancing security and privacy concerns against the fidelity and utility of the data.

Botnets consist of devices connected to the internet, supervised by a botnet owner, performing malicious tasks. The significant impact of botnets on corporate, governmental and civilian operations has resulted in a lot of attention from the machine learning community. However, most studies to date do not respect the linked structure of network data and rely heavily on the availability of a labelled data set. This study applies Stochastic Block Models (SBM) as an Unsupervised Approach to botnet data with the aim of identifying infected clusters without the need for a labelled data set.