Bibliographic record
Abstract
The Analysis Function of the US-CERT Control Systems Security Center (CSSC) at the Idaho National Laboratory (INL) has prepared this report to document cyber security incidents for use by the CSSC. The description and analysis of incidents reported herein support three CSSC tasks: establishing a business case; increasing security awareness and private and corporate participation related to enhanced cyber security of control systems; and providing informational material to support model development and prioritize activities for CSSC. The stated mission of CSSC is to reduce vulnerability of critical infrastructure to cyber attack on control systems. As stated in the Incident Management Tool Requirements (August 2005) ''Vulnerability reduction is promoted by risk analysis that tracks actual risk, emphasizes high risk, determines risk reduction as a function of countermeasures, tracks increase of risk due to external influence, and measures success of the vulnerability reduction program''. Process control and Supervisory Control and Data Acquisition (SCADA) systems, with their reliance on proprietary networks and hardware, have long been considered immune to the network attacks that have wreaked so much havoc on corporate information systems. New research indicates this confidence is misplaced--the move to open standards such as Ethernet, Transmission Control Protocol/Internet Protocol, and Web technologies is allowing hackers to take advantage of the control industry's unawareness. Much of the available information about cyber incidents represents a characterization as opposed to an analysis of events. The lack of good analyses reflects an overall weakness in reporting requirements as well as the fact that to date there have been very few serious cyber attacks on control systems. Most companies prefer not to share cyber attack incident data because of potential financial repercussions. Uniform reporting requirements will do much to make this information available to Department of Homeland Security (DHS) and others who require it. This report summarizes the rise in frequency of cyber attacks, describes the perpetrators, and identifies the means of attack. This type of analysis, when used in conjunction with vulnerability analyses, can be used to support a proactive approach to prevent cyber attacks. CSSC will use this document to evolve a standardized approach to incident reporting and analysis. This document will be updated as needed to record additional event analyses and insights regarding incident reporting. This report represents 120 cyber security incidents documented in a number of sources, including: the British Columbia Institute of Technology (BCIT) Industrial Security Incident Database, the 2003 CSI/FBI Computer Crime and Security Survey, the KEMA, Inc., Database, Lawrence Livermore National Laboratory, the Energy Incident Database, the INL Cyber Incident Database, and other open-source data. The National Memorial Institute for the Prevention of Terrorism (MIPT) database was also interrogated but, interestingly, failed to yield any cyber attack incidents. The results of this evaluation indicate that historical evidence provides insight into control system related incidents or failures; however, that the limited available information provides little support to future risk estimates. The documented case history shows that activity has increased significantly since 1988. The majority of incidents come from the Internet by way of opportunistic viruses, Trojans, and worms, but a surprisingly large number are directed acts of sabotage. A substantial number of confirmed, unconfirmed, and potential events that directly or potentially impact control systems worldwide are also identified. Twelve selected cyber incidents are presented at the end of this report as examples of the documented case studies (see Appendix B).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".