Robust control system networks :: how to achieve reliable control after Stuxnet /
The Stuxnet computer virus, originally discovered in July 2010, qualified as a turning point for control system security. While the malware did not cause destructive damage outside its designated target, it hit the Western world like the Sputnik shock. The sophistication and aggressiveness of this c...
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Format: | Elektronisch E-Book |
Sprache: | English |
Veröffentlicht: |
New York :
Momentum Press,
©2012.
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Schlagworte: | |
Online-Zugang: | Volltext |
Zusammenfassung: | The Stuxnet computer virus, originally discovered in July 2010, qualified as a turning point for control system security. While the malware did not cause destructive damage outside its designated target, it hit the Western world like the Sputnik shock. The sophistication and aggressiveness of this computer virus was at a level that few people had anticipated. It simply popped up without warning, after 10 years of silence following the first malicious amateur-style cyber attack on wastewater control systems in Australia. Compared to office IT malware as we know it, this would be like going from 1980s-style password guessing to botnets in one step. It was, indeed, shocking. Instead of a learning curve for both the attackers and the defenders that the general development and trend of malware had been experiencing in the IT world, there was one big leap. Even if they had wanted to, operators of potential targets in critical infrastructure and in the private sector were not able to perform a similar leap in defense and protection. Despite years, reaching back to the turn of the millennium, of efforts and investments in control system security, governmental programs and organizations, industry standards, workgroups, conferences, risk assessments, and mitigation projects, the industrialized nations continue to face a significant threat from post-Stuxnet malware for which they are by no means prepared. |
Beschreibung: | 1 online resource (xiii, 206 pages) : illustrations |
Bibliographie: | Includes bibliographical references (pages 187-188) and index. |
ISBN: | 9781606503027 1606503022 1283895617 9781283895613 |
Internformat
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505 | 0 | |a 1. Introduction: the three faces of risk -- 1.1. The insurance model of risk: risk as statistical probability and projected amount of loss -- 1.2. The logical model of risk: risk as cause and consequence -- 1.3. The financial model of risk: risk as volatility -- 1.4. From risk to fragility and from security to robustness. | |
520 | 3 | |a The Stuxnet computer virus, originally discovered in July 2010, qualified as a turning point for control system security. While the malware did not cause destructive damage outside its designated target, it hit the Western world like the Sputnik shock. The sophistication and aggressiveness of this computer virus was at a level that few people had anticipated. It simply popped up without warning, after 10 years of silence following the first malicious amateur-style cyber attack on wastewater control systems in Australia. Compared to office IT malware as we know it, this would be like going from 1980s-style password guessing to botnets in one step. It was, indeed, shocking. Instead of a learning curve for both the attackers and the defenders that the general development and trend of malware had been experiencing in the IT world, there was one big leap. Even if they had wanted to, operators of potential targets in critical infrastructure and in the private sector were not able to perform a similar leap in defense and protection. Despite years, reaching back to the turn of the millennium, of efforts and investments in control system security, governmental programs and organizations, industry standards, workgroups, conferences, risk assessments, and mitigation projects, the industrialized nations continue to face a significant threat from post-Stuxnet malware for which they are by no means prepared. | |
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author | Langner, Ralph |
author_GND | http://id.loc.gov/authorities/names/n85125190 |
author_facet | Langner, Ralph |
author_role | |
author_sort | Langner, Ralph |
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contents | 1. Introduction: the three faces of risk -- 1.1. The insurance model of risk: risk as statistical probability and projected amount of loss -- 1.2. The logical model of risk: risk as cause and consequence -- 1.3. The financial model of risk: risk as volatility -- 1.4. From risk to fragility and from security to robustness. |
ctrlnum | (OCoLC)795808308 |
dewey-full | 629.8 |
dewey-hundreds | 600 - Technology (Applied sciences) |
dewey-ones | 629 - Other branches of engineering |
dewey-raw | 629.8 |
dewey-search | 629.8 |
dewey-sort | 3629.8 |
dewey-tens | 620 - Engineering and allied operations |
discipline | Mess-/Steuerungs-/Regelungs-/Automatisierungstechnik / Mechatronik |
format | Electronic eBook |
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spelling | Langner, Ralph. http://id.loc.gov/authorities/names/n85125190 Robust control system networks : how to achieve reliable control after Stuxnet / Ralph Langner. New York : Momentum Press, ©2012. 1 online resource (xiii, 206 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier Print version record. Includes bibliographical references (pages 187-188) and index. 1. Introduction: the three faces of risk -- 1.1. The insurance model of risk: risk as statistical probability and projected amount of loss -- 1.2. The logical model of risk: risk as cause and consequence -- 1.3. The financial model of risk: risk as volatility -- 1.4. From risk to fragility and from security to robustness. The Stuxnet computer virus, originally discovered in July 2010, qualified as a turning point for control system security. While the malware did not cause destructive damage outside its designated target, it hit the Western world like the Sputnik shock. The sophistication and aggressiveness of this computer virus was at a level that few people had anticipated. It simply popped up without warning, after 10 years of silence following the first malicious amateur-style cyber attack on wastewater control systems in Australia. Compared to office IT malware as we know it, this would be like going from 1980s-style password guessing to botnets in one step. It was, indeed, shocking. Instead of a learning curve for both the attackers and the defenders that the general development and trend of malware had been experiencing in the IT world, there was one big leap. Even if they had wanted to, operators of potential targets in critical infrastructure and in the private sector were not able to perform a similar leap in defense and protection. Despite years, reaching back to the turn of the millennium, of efforts and investments in control system security, governmental programs and organizations, industry standards, workgroups, conferences, risk assessments, and mitigation projects, the industrialized nations continue to face a significant threat from post-Stuxnet malware for which they are by no means prepared. Robust control. http://id.loc.gov/authorities/subjects/sh99013330 Computer networks Security measures. http://id.loc.gov/authorities/subjects/sh94001277 Commande robuste. Réseaux d'ordinateurs Sécurité Mesures. TECHNOLOGY & ENGINEERING Automation. bisacsh TECHNOLOGY & ENGINEERING Robotics. bisacsh Computer networks Security measures fast Robust control fast has work: Robust control system networks (Text) https://id.oclc.org/worldcat/entity/E39PCGwRjvwf4J6CYG8XXwcQdP https://id.oclc.org/worldcat/ontology/hasWork Print version: Langner, Ralph. Robust control system networks. New York : Momentum Press ; 2012 9781606503003 (OCoLC)731915143 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=501116 Volltext |
spellingShingle | Langner, Ralph Robust control system networks : how to achieve reliable control after Stuxnet / 1. Introduction: the three faces of risk -- 1.1. The insurance model of risk: risk as statistical probability and projected amount of loss -- 1.2. The logical model of risk: risk as cause and consequence -- 1.3. The financial model of risk: risk as volatility -- 1.4. From risk to fragility and from security to robustness. Robust control. http://id.loc.gov/authorities/subjects/sh99013330 Computer networks Security measures. http://id.loc.gov/authorities/subjects/sh94001277 Commande robuste. Réseaux d'ordinateurs Sécurité Mesures. TECHNOLOGY & ENGINEERING Automation. bisacsh TECHNOLOGY & ENGINEERING Robotics. bisacsh Computer networks Security measures fast Robust control fast |
subject_GND | http://id.loc.gov/authorities/subjects/sh99013330 http://id.loc.gov/authorities/subjects/sh94001277 |
title | Robust control system networks : how to achieve reliable control after Stuxnet / |
title_auth | Robust control system networks : how to achieve reliable control after Stuxnet / |
title_exact_search | Robust control system networks : how to achieve reliable control after Stuxnet / |
title_full | Robust control system networks : how to achieve reliable control after Stuxnet / Ralph Langner. |
title_fullStr | Robust control system networks : how to achieve reliable control after Stuxnet / Ralph Langner. |
title_full_unstemmed | Robust control system networks : how to achieve reliable control after Stuxnet / Ralph Langner. |
title_short | Robust control system networks : |
title_sort | robust control system networks how to achieve reliable control after stuxnet |
title_sub | how to achieve reliable control after Stuxnet / |
topic | Robust control. http://id.loc.gov/authorities/subjects/sh99013330 Computer networks Security measures. http://id.loc.gov/authorities/subjects/sh94001277 Commande robuste. Réseaux d'ordinateurs Sécurité Mesures. TECHNOLOGY & ENGINEERING Automation. bisacsh TECHNOLOGY & ENGINEERING Robotics. bisacsh Computer networks Security measures fast Robust control fast |
topic_facet | Robust control. Computer networks Security measures. Commande robuste. Réseaux d'ordinateurs Sécurité Mesures. TECHNOLOGY & ENGINEERING Automation. TECHNOLOGY & ENGINEERING Robotics. Computer networks Security measures Robust control |
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