Robust control system networks: how to achieve reliable control after Stuxnet
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
New York
Momentum Press
c2012
|
| Schlagworte: | |
| Online-Zugang: | FAW01 FAW02 Volltext |
| Beschreibung: | Includes bibliographical references (p. 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 |
| Beschreibung: | 1 Online-Ressource (xiii, 206 p.) |
| ISBN: | 1283895617 1606503006 1606503022 9781283895613 9781606503003 9781606503027 |
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| 500 | |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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| spelling | Langner, Ralph Verfasser aut Robust control system networks how to achieve reliable control after Stuxnet Ralph Langner New York Momentum Press c2012 1 Online-Ressource (xiii, 206 p.) txt rdacontent c rdamedia cr rdacarrier Includes bibliographical references (p. 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 TECHNOLOGY & ENGINEERING / Automation bisacsh TECHNOLOGY & ENGINEERING / Robotics bisacsh Robust control local Computer networks / Security measures local Automatic Control Computer Networks Robust control Computer networks Security measures http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=501116 Aggregator Volltext |
| spellingShingle | Langner, Ralph Robust control system networks how to achieve reliable control after Stuxnet TECHNOLOGY & ENGINEERING / Automation bisacsh TECHNOLOGY & ENGINEERING / Robotics bisacsh Robust control local Computer networks / Security measures local Automatic Control Computer Networks Robust control Computer networks Security measures |
| 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 | TECHNOLOGY & ENGINEERING / Automation bisacsh TECHNOLOGY & ENGINEERING / Robotics bisacsh Robust control local Computer networks / Security measures local Automatic Control Computer Networks Robust control Computer networks Security measures |
| topic_facet | TECHNOLOGY & ENGINEERING / Automation TECHNOLOGY & ENGINEERING / Robotics Robust control Computer networks / Security measures Automatic Control Computer Networks Computer networks Security measures |
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