Atnaujinkite slapukų nuostatas

El. knyga: Data Science For Cyber-security

Edited by (Imperial College London, Uk), Edited by (Los Alamos Nat'l Lab, Usa), Edited by (Imperial College London, Uk), Edited by (Univ Of Bristol, Uk)
  • Formatas: 304 pages
  • Serija: Security Science and Technology 3
  • Išleidimo metai: 26-Sep-2018
  • Leidėjas: World Scientific Europe Ltd
  • Kalba: eng
  • ISBN-13: 9781786345653
Kitos knygos pagal šią temą:
  • Formatas: 304 pages
  • Serija: Security Science and Technology 3
  • Išleidimo metai: 26-Sep-2018
  • Leidėjas: World Scientific Europe Ltd
  • Kalba: eng
  • ISBN-13: 9781786345653
Kitos knygos pagal šią temą:

DRM apribojimai

  • Kopijuoti:

    neleidžiama

  • Spausdinti:

    neleidžiama

  • El. knygos naudojimas:

    Skaitmeninių teisių valdymas (DRM)
    Leidykla pateikė šią knygą šifruota forma, o tai reiškia, kad norint ją atrakinti ir perskaityti reikia įdiegti nemokamą programinę įrangą. Norint skaityti šią el. knygą, turite susikurti Adobe ID . Daugiau informacijos  čia. El. knygą galima atsisiųsti į 6 įrenginius (vienas vartotojas su tuo pačiu Adobe ID).

    Reikalinga programinė įranga
    Norint skaityti šią el. knygą mobiliajame įrenginyje (telefone ar planšetiniame kompiuteryje), turite įdiegti šią nemokamą programėlę: PocketBook Reader (iOS / Android)

    Norint skaityti šią el. knygą asmeniniame arba „Mac“ kompiuteryje, Jums reikalinga  Adobe Digital Editions “ (tai nemokama programa, specialiai sukurta el. knygoms. Tai nėra tas pats, kas „Adobe Reader“, kurią tikriausiai jau turite savo kompiuteryje.)

    Negalite skaityti šios el. knygos naudodami „Amazon Kindle“.

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. This volume presents contributed papers from an international conference of the same name held at Imperial College. Experts from the field have provided their latest discoveries and review state of the art technologies.



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. This volume presents contributed papers from an international conference of the same name held at Imperial College. Experts from the field have provided their latest discoveries and review state of the art technologies.

Preface v
1 Unified Host and Network Data Set
1(22)
Melissa J. M. Turcotte
Alexander D. Kent
Curtis Hash
2 Computational Statistics and Mathematics for Cyber-Security
23(32)
David J. Marchette
3 Bayesian Activity Modelling for Network Flow Data
55(22)
Henry Clausen
Mark Briers
Niall M. Adams
4 Towards Generalisable Network Threat Detection
77(18)
Blake Anderson
Martin Vejman
David McGrew
Subharthi Paul
5 Feature Trade-Off Analysis for Reconnaissance Detection
95(32)
Harsha Kumara Kalutarage
Siraj Ahmed Shaikh
6 Anomaly Detection on User-Agent Strings
127(18)
Eirini Spyropoulou
Jordan Noble
Christoforos Anagnostopoulos
7 Discovery of the Twitter Bursty Botnet
145(16)
Juan Echeverria
Christoph Besel
Shi Zhou
8 Stochastic Block Models as an Unsupervised Approach to Detect Botnet-Infected Clusters in Networked Data
161(18)
Mark Patrick Roeling
Geoff Nicholls
9 Classification of Red Team Authentication Events in an Enterprise Network
179(16)
John M. Conroy
10 Weakly Supervised Learning: How to Engineer Labels for Machine Learning in Cyber-Security
195(32)
Christoforos Anagnostopoulos
11 Large-scale Analogue Measurements and Analysis for Cyber-Security
227(24)
George Cybenko
Gil M. Raz
12 Fraud Detection by Stacking Cost-Sensitive Decision Trees
251(16)
Alejandro Correa Bahnsen
Sergio Villegas
Djamila Aouada
Bjorn Ottersten
13 Data-Driven Decision Making for Cyber-Security
267(26)
Mike Fisk
Index 293