Was ist das eigentlich? Cyberrisiken verständlich erklärt
Es wird viel über Cyberrisiken gesprochen. Oftmals fehlt aber das grundsätzliche Verständnis, was Cyberrisiken überhaupt sind. Ohne diese zu verstehen, lässt sich aber auch kein Versicherungsschutz gestalten.
Beinahe alle Aktivitäten des täglichen Lebens können heute über das Internet abgewickelt werden. Online-Shopping und Online-Banking sind im Alltag angekommen. Diese Entwicklung trifft längst nicht nur auf Privatleute, sondern auch auf Firmen zu. Das Schlagwort Industrie 4.0 verheißt bereits eine zunehmende Vernetzung diverser geschäftlicher Vorgänge über das Internet.
Anbieter von Cyberversicherungen für kleinere und mittelständische Unternehmen (KMU) haben Versicherungen die Erfahrung gemacht, dass trotz dieser eindeutigen Entwicklung Cyberrisiken immer noch unterschätzt werden, da sie als etwas Abstraktes wahrgenommen werden. Für KMU kann dies ein gefährlicher Trugschluss sein, da gerade hier Cyberattacken existenzbedrohende Ausmaße annehmen können. So wird noch häufig gefragt, was Cyberrisiken eigentlich sind. Diese Frage ist mehr als verständlich, denn ohne (Cyber-)Risiken bestünde auch kein Bedarf für eine (Cyber-)Versicherung.
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A00-240 test Format | A00-240 Course Contents | A00-240 Course Outline | A00-240 test Syllabus | A00-240 test Objectives
This test is administered by SAS and Pearson VUE.
60 scored multiple-choice and short-answer questions.
(Must achieve score of 68 percent correct to pass)
In addition to the 60 scored items, there may be up to five unscored items.
Two hours to complete exam.
Use test ID A00-240; required when registering with Pearson VUE.
ANOVA - 10%
Verify the assumptions of ANOVA
Analyze differences between population means using the GLM and TTEST procedures
Perform ANOVA post hoc test to evaluate treatment effect
Detect and analyze interactions between factors
Linear Regression - 20%
Fit a multiple linear regression model using the REG and GLM procedures
Analyze the output of the REG, PLM, and GLM procedures for multiple linear regression models
Use the REG or GLMSELECT procedure to perform model selection
Assess the validity of a given regression model through the use of diagnostic and residual analysis
Logistic Regression - 25%
Perform logistic regression with the LOGISTIC procedure
Optimize model performance through input selection
Interpret the output of the LOGISTIC procedure
Score new data sets using the LOGISTIC and PLM procedures
Prepare Inputs for Predictive Model Performance - 20%
Identify the potential challenges when preparing input data for a model
Use the DATA step to manipulate data with loops, arrays, conditional statements and functions
Improve the predictive power of categorical inputs
Screen variables for irrelevance and non-linear association using the CORR procedure
Screen variables for non-linearity using empirical logit plots
Measure Model Performance - 25%
Apply the principles of honest test to model performance measurement
Assess classifier performance using the confusion matrix
Model selection and validation using training and validation data
Create and interpret graphs (ROC, lift, and gains charts) for model comparison and selection
Establish effective decision cut-off values for scoring
Verify the assumptions of ANOVA
Explain the central limit theorem and when it must be applied
Examine the distribution of continuous variables (histogram, box -whisker, Q-Q plots)
Describe the effect of skewness on the normal distribution
Define H0, H1, Type I/II error, statistical power, p-value
Describe the effect of sample size on p-value and power
Interpret the results of hypothesis testing
Interpret histograms and normal probability charts
Draw conclusions about your data from histogram, box-whisker, and Q-Q plots
Identify the kinds of problems may be present in the data: (biased sample, outliers, extreme values)
For a given experiment, verify that the observations are independent
For a given experiment, verify the errors are normally distributed
Use the UNIVARIATE procedure to examine residuals
For a given experiment, verify all groups have equal response variance
Use the HOVTEST option of MEANS statement in PROC GLM to asses response variance
Analyze differences between population means using the GLM and TTEST procedures
Use the GLM Procedure to perform ANOVA
o CLASS statement
o MODEL statement
o MEANS statement
o OUTPUT statement
Evaluate the null hypothesis using the output of the GLM procedure
Interpret the statistical output of the GLM procedure (variance derived from MSE, Fvalue, p-value R**2, Levene's test)
Interpret the graphical output of the GLM procedure
Use the TTEST Procedure to compare means Perform ANOVA post hoc test to evaluate treatment effect
Use the LSMEANS statement in the GLM or PLM procedure to perform pairwise comparisons
Use PDIFF option of LSMEANS statement
Use ADJUST option of the LSMEANS statement (TUKEY and DUNNETT)
Interpret diffograms to evaluate pairwise comparisons
Interpret control plots to evaluate pairwise comparisons
Compare/Contrast use of pairwise T-Tests, Tukey and Dunnett comparison methods Detect and analyze interactions between factors
Use the GLM procedure to produce reports that will help determine the significance of the interaction between factors. MODEL statement
LSMEANS with SLICE=option (Also using PROC PLM)
ODS SELECT
Interpret the output of the GLM procedure to identify interaction between factors:
p-value
F Value
R Squared
TYPE I SS
TYPE III SS
Linear Regression - 20%
Fit a multiple linear regression model using the REG and GLM procedures
Use the REG procedure to fit a multiple linear regression model
Use the GLM procedure to fit a multiple linear regression model
Analyze the output of the REG, PLM, and GLM procedures for multiple linear regression models
Interpret REG or GLM procedure output for a multiple linear regression model:
convert models to algebraic expressions
Convert models to algebraic expressions
Identify missing degrees of freedom
Identify variance due to model/error, and total variance
Calculate a missing F value
Identify variable with largest impact to model
For output from two models, identify which model is better
Identify how much of the variation in the dependent variable is explained by the model
Conclusions that can be drawn from REG, GLM, or PLM output: (about H0, model quality, graphics)
Use the REG or GLMSELECT procedure to perform model selection
Use the SELECTION option of the model statement in the GLMSELECT procedure
Compare the differentmodel selection methods (STEPWISE, FORWARD, BACKWARD)
Enable ODS graphics to display graphs from the REG or GLMSELECT procedure
Identify best models by examining the graphical output (fit criterion from the REG or GLMSELECT procedure)
Assign names to models in the REG procedure (multiple model statements)
Assess the validity of a given regression model through the use of diagnostic and residual analysis
Explain the assumptions for linear regression
From a set of residuals plots, asses which assumption about the error terms has been violated
Use REG procedure MODEL statement options to identify influential observations (Student Residuals, Cook's D, DFFITS, DFBETAS)
Explain options for handling influential observations
Identify collinearity problems by examining REG procedure output
Use MODEL statement options to diagnose collinearity problems (VIF, COLLIN, COLLINOINT)
Logistic Regression - 25%
Perform logistic regression with the LOGISTIC procedure
Identify experiments that require analysis via logistic regression
Identify logistic regression assumptions
logistic regression concepts (log odds, logit transformation, sigmoidal relationship between p and X)
Use the LOGISTIC procedure to fit a binary logistic regression model (MODEL and CLASS statements)
Optimize model performance through input selection
Use the LOGISTIC procedure to fit a multiple logistic regression model
LOGISTIC procedure SELECTION=SCORE option
Perform Model Selection (STEPWISE, FORWARD, BACKWARD) within the LOGISTIC procedure
Interpret the output of the LOGISTIC procedure
Interpret the output from the LOGISTIC procedure for binary logistic regression models: Model Convergence section
Testing Global Null Hypothesis table
Type 3 Analysis of Effects table
Analysis of Maximum Likelihood Estimates table
Association of Predicted Probabilities and Observed Responses
Score new data sets using the LOGISTIC and PLM procedures
Use the SCORE statement in the PLM procedure to score new cases
Use the CODE statement in PROC LOGISTIC to score new data
Describe when you would use the SCORE statement vs the CODE statement in PROC LOGISTIC
Use the INMODEL/OUTMODEL options in PROC LOGISTIC
Explain how to score new data when you have developed a model from a biased sample
Prepare Inputs for Predictive Model
Performance - 20%
Identify the potential challenges when preparing input data for a model
Identify problems that missing values can cause in creating predictive models and scoring new data sets
Identify limitations of Complete Case Analysis
Explain problems caused by categorical variables with numerous levels
Discuss the problem of redundant variables
Discuss the problem of irrelevant and redundant variables
Discuss the non-linearities and the problems they create in predictive models
Discuss outliers and the problems they create in predictive models
Describe quasi-complete separation
Discuss the effect of interactions
Determine when it is necessary to oversample data
Use the DATA step to manipulate data with loops, arrays, conditional statements and functions
Use ARRAYs to create missing indicators
Use ARRAYS, LOOP, IF, and explicit OUTPUT statements
Improve the predictive power of categorical inputs
Reduce the number of levels of a categorical variable
Explain thresholding
Explain Greenacre's method
Cluster the levels of a categorical variable via Greenacre's method using the CLUSTER procedure
o METHOD=WARD option
o FREQ, VAR, ID statement
Use of ODS output to create an output data set
Convert categorical variables to continuous using smooth weight of evidence
Screen variables for irrelevance and non-linear association using the CORR procedure
Explain how Hoeffding's D and Spearman statistics can be used to find irrelevant variables and non-linear associations
Produce Spearman and Hoeffding's D statistic using the CORR procedure (VAR, WITH statement)
Interpret a scatter plot of Hoeffding's D and Spearman statistic to identify irrelevant variables and non-linear associations Screen variables for non-linearity using empirical logit plots
Use the RANK procedure to bin continuous input variables (GROUPS=, OUT= option; VAR, RANK statements)
Interpret RANK procedure output
Use the MEANS procedure to calculate the sum and means for the target cases and total events (NWAY option; CLASS, VAR, OUTPUT statements)
Create empirical logit plots with the SGPLOT procedure
Interpret empirical logit plots
Measure Model Performance - 25%
Apply the principles of honest test to model performance measurement
Explain techniques to honestly assess classifier performance
Explain overfitting
Explain differences between validation and test data
Identify the impact of performing data preparation before data is split Assess classifier performance using the confusion matrix
Explain the confusion matrix
Define: Accuracy, Error Rate, Sensitivity, Specificity, PV+, PV-
Explain the effect of oversampling on the confusion matrix
Adjust the confusion matrix for oversampling
Model selection and validation using training and validation data
Divide data into training and validation data sets using the SURVEYSELECT procedure
Discuss the subset selection methods available in PROC LOGISTIC
Discuss methods to determine interactions (forward selection, with bar and @ notation)
Create interaction plot with the results from PROC LOGISTIC
Select the model with fit statistics (BIC, AIC, KS, Brier score)
Create and interpret graphs (ROC, lift, and gains charts) for model comparison and selection
Explain and interpret charts (ROC, Lift, Gains)
Create a ROC curve (OUTROC option of the SCORE statement in the LOGISTIC procedure)
Use the ROC and ROCCONTRAST statements to create an overlay plot of ROC curves for two or more models
Explain the concept of depth as it relates to the gains chart
Establish effective decision cut-off values for scoring
Illustrate a decision rule that maximizes the expected profit
Explain the profit matrix and how to use it to estimate the profit per scored customer
Calculate decision cutoffs using Bayes rule, given a profit matrix
Determine optimum cutoff values from profit plots
Given a profit matrix, and model results, determine the model with the highest average profit
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Warum sind Cyberrisiken so schwer greifbar?
Als mehr oder weniger neuartiges Phänomen stellen Cyberrisiken Unternehmen und Versicherer vor besondere Herausforderungen. Nicht nur die neuen Schadenszenarien sind abstrakter oder noch nicht bekannt. Häufig sind immaterielle Werte durch Cyberrisiken in Gefahr. Diese wertvollen Vermögensgegenstände sind schwer bewertbar.
Obwohl die Gefahr durchaus wahrgenommen wird, unterschätzen viele Firmen ihr eigenes Risiko. Dies liegt unter anderem auch an den Veröffentlichungen zu Cyberrisiken. In der Presse finden sich unzählige Berichte von Cyberattacken auf namhafte und große Unternehmen. Den Weg in die Presse finden eben nur die spektakulären Fälle. Die dort genannten Schadenszenarien werden dann für das eigene Unternehmen als unrealistisch eingestuft. Die für die KMU nicht minder gefährlichen Cyberattacken werden nur selten publiziert.
Aufgrund der fehlenden öffentlichen Meldungen von Sicherheitsvorfällen an Sicherheitsbehörden und wegen der fehlenden Presseberichte fällt es schwer, Fakten und Zahlen zur Risikolage zu erheben. Aber ohne diese Grundlage fällt es schwer, in entsprechende Sicherheitsmaßnahmen zu investieren.
Erklärungsleitfaden anhand eines Ursache-Wirkungs-Modells
Häufig nähert man sich dem Thema Cyberrisiko anlass- oder eventbezogen, also wenn sich neue Schadenszenarien wie die weltweite WannaCry-Attacke entwickeln. Häufig wird auch akteursgebunden beleuchtet, wer Angreifer oder Opfer sein kann. Dadurch begrenzt man sich bei dem Thema häufig zu sehr nur auf die Cyberkriminalität. Um dem Thema Cyberrisiko jedoch gerecht zu werden, müssen auch weitere Ursachen hinzugezogen werden.
Mit einer Kategorisierung kann das Thema ganzheitlich und nachvollziehbar strukturiert werden. Ebenso hilft eine solche Kategorisierung dabei, eine Abgrenzung vorzunehmen, für welche Gefahren Versicherungsschutz über eine etwaige Cyberversicherung besteht und für welche nicht.
Die Ursachen sind dabei die Risiken, während finanzielle bzw. nicht finanzielle Verluste die Wirkungen sind. Cyberrisiken werden demnach in zwei Hauptursachen eingeteilt. Auf der einen Seite sind die nicht kriminellen Ursachen und auf der anderen Seite die kriminellen Ursachen zu nennen. Beide Ursachen können dabei in drei Untergruppen unterteilt werden.
Nicht kriminelle Ursachen
Höhere Gewalt
Häufig hat man bei dem Thema Cyberrisiko nur die kriminellen Ursachen vor Augen. Aber auch höhere Gewalt kann zu einem empfindlichen Datenverlust führen oder zumindest die Verfügbarkeit von Daten einschränken, indem Rechenzentren durch Naturkatastrophen wie beispielsweise Überschwemmungen oder Erdbeben zerstört werden. Ebenso sind Stromausfälle denkbar.
Menschliches Versagen/Fehlverhalten
Als Cyberrisiken sind auch unbeabsichtigtes und menschliches Fehlverhalten denkbar. Hierunter könnte das versehentliche Veröffentlichen von sensiblen Informationen fallen. Möglich sind eine falsche Adressierung, Wahl einer falschen Faxnummer oder das Hochladen sensibler Daten auf einen öffentlichen Bereich der Homepage.
Technisches Versagen
Auch Hardwaredefekte können zu einem herben Datenverlust führen. Neben einem Überhitzen von Rechnern sind Kurzschlüsse in Systemtechnik oder sogenannte Headcrashes von Festplatten denkbare Szenarien.
Kriminelle Ursachen
Hackerangriffe
Hackerangriffe oder Cyberattacken sind in der Regel die Szenarien, die die Presse dominieren. Häufig wird von spektakulären Datendiebstählen auf große Firmen oder von weltweiten Angriffen mit sogenannten Kryptotrojanern berichtet. Opfer kann am Ende aber jeder werden. Ziele, Methoden und auch das Interesse sind vielfältig. Neben dem finanziellen Interesse können Hackerangriffe auch zur Spionage oder Sabotage eingesetzt werden. Mögliche Hackermethoden sind unter anderem: Social Engineering, Trojaner, DoS-Attacken oder Viren.
Physischer Angriff
Die Zielsetzung eines physischen Angriffs ist ähnlich dem eines Hackerangriffs. Dabei wird nicht auf die Tools eines Hackerangriffs zurückgegriffen, sondern durch das physische Eindringen in Unternehmensgebäude das Ziel erreicht. Häufig sind es Mitarbeiter, die vertrauliche Informationen stehlen, da sie bereits den notwendigen Zugang zu den Daten besitzen.
Erpressung
Obwohl die Erpressung aufgrund der eingesetzten Methoden auch als Hackerangriff gewertet werden könnte, ergibt eine Differenzierung Sinn. Erpressungsfälle durch Kryptotrojaner sind eines der häufigsten Schadenszenarien für kleinere und mittelständische Unternehmen. Außerdem sind auch Erpressungsfälle denkbar, bei denen sensible Daten gestohlen wurden und ein Lösegeld gefordert wird, damit sie nicht veröffentlicht oder weiterverkauft werden.
Ihre Cyberversicherung sollte zumindet folgende Schäden abdecken:
Cyber-Kosten:
- Soforthilfe und Forensik-Kosten (Kosten der Ursachenermittlung, Benachrichtigungskosten und Callcenter-Leistung)
- Krisenkommunikation / PR-Maßnahmen
- Systemverbesserungen nach einer Cyber-Attacke
- Aufwendungen vor Eintritt des Versicherungsfalls
Cyber-Drittschäden (Haftpflicht):
- Befriedigung oder Abwehr von Ansprüchen Dritter
- Rechtswidrige elektronische Kommunikation
- Ansprüche der E-Payment-Serviceprovider
- Vertragsstrafe wegen der Verletzung von Geheimhaltungspflichten und Datenschutzvereinbarungen
- Vertragliche Schadenersatzansprüche
- Vertragliche Haftpflicht bei Datenverarbeitung durch Dritte
- Rechtsverteidigungskosten
Cyber-Eigenschäden:
- Betriebsunterbrechung
- Betriebsunterbrechung durch Ausfall von Dienstleister (optional)
- Mehrkosten
- Wiederherstellung von Daten (auch Entfernen der Schadsoftware)
- Cyber-Diebstahl: elektronischer Zahlungsverkehr, fehlerhafter Versand von Waren, Telefon-Mehrkosten/erhöhte Nutzungsentgelte
- Cyber-Erpressung
- Entschädigung mit Strafcharakter/Bußgeld
- Ersatz-IT-Hardware
- Cyber-Betrug