# Exploratory Data Analysis with CISSM Cyber Attacks Database - Part 2

**Part 2 of 2**

**Introduction**

In part 2 of our exploratory data analysis (EDA), a mission critical task underpinning the predominance of detection development and preparation for cybersecurity-centric machine learning, a remimder that there are a number of actions that analysts can take to better understand a particular data set and ready it for more robust utilization. That includes forecasting, which we will perform, again using the University of Maryland’s Center for International and Security Studies (CISSM) Cyber Attacks Database, an ideal candidate for experimental exploration. Per the dataset description, the database “brings together open-source information surrounding a range of publicly acknowledged cyber events on private and public organizations. Events from 2014 through present have been coded to standardize information on threat actor, threat actor country, motive, target, end effects, industry, and country of impact. Source links to the news source are also provided” (Harry & Gallagher, 2018).

We continue this exploratory data analytics journey with forecasting models and plots to show how you might predict future attack volumes.

**Models**

Next, we model the CISSM CAD data for time series forecasting with three well known methods selected for performance with the CISSM CAD dataset. These include naive, SES, and ARIMA.

Naive forecasting uses the most recent observation as the forecast for the next observation.

Simple exponential smoothing (SES) is the method of time series forecasting used with univariate data with no trend and no seasonal pattern.

Autoregressive integrated moving average, or ARIMA, is a statistical analysis model that uses time series data to either better understand the data set or to predict future trends.

Before we initiate the forecasts we use each of the methods to determine which one performs best. For brevity here, we’ll only run the models and plots on exploitative data, but the forecasts_CISSM.R script and the Jupyter/Colab notebook in the GitHub repo run all models and plots with *disruptive* and *exploitative* data. Note again that these are subsets of a much broader dataset represented by CISSM CAD. Explore further to your liking.

Important to the modeling that follows, note the evaluation metrics RMSE and MAE. Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) are used to evaluate regression models, tell us how accurate our predictions are, and the amount of deviation from actual values (Acharya, 2021). In essence, the lower the score, the better the performance.

```
> naive_model_exploitative <- naive(exploitative, h = 12)
> summary(naive_model_exploitative)
Forecast method: Naive method
Model Information:
Call: naive(y = exploitative, h = 12)
Residual sd: 20.5334
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set -0.06481481 20.5334 15.41667 -Inf Inf 0.6228308 -0.3349566
Forecasts:
Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
Feb 2023 23 -3.314606 49.31461 -17.24472 63.24472
Mar 2023 23 -14.214473 60.21447 -33.91463 79.91463
Apr 2023 23 -22.578235 68.57824 -46.70590 92.70590
May 2023 23 -29.629213 75.62921 -57.48944 103.48944
Jun 2023 23 -35.841249 81.84125 -66.98992 112.98992
Jul 2023 23 -41.457358 87.45736 -75.57902 121.57902
#snipped
```

The naive model yields an RMSE score of 20.5 and an MAE score 15.4.

```
> ses_model_exploitative <- ses(exploitative$Exploitative, h = 12) # RMSE = 18.8, MAE = 13.9
> summary(ses_model_exploitative)
Forecast method: Simple exponential smoothing
Model Information:
Simple exponential smoothing
Call:
ses(y = exploitative$Exploitative, h = 12)
Smoothing parameters:
alpha = 0.529
Initial states:
l = 32.3171
sigma: 19.0189
AIC AICc BIC
1157.442 1157.671 1165.517
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 0.2068036 18.84358 13.92841 -Inf Inf 0.9034647 0.03522811
Forecasts:
Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
110 44.24184 19.8681717 68.61551 6.965531 81.51815
111 44.24184 16.6677635 71.81592 2.070929 86.41275
112 44.24184 13.8020028 74.68168 -2.311874 90.79555
113 44.24184 11.1837445 77.29994 -6.316154 94.79983
114 44.24184 8.7581582 79.72552 -10.025768 98.50945
115 44.24184 6.4880896 81.99559 -13.497539 101.98122
#snipped
```

The SES model yields an RMSE score of 18.8 and an MAE score of 13.9.

```
> arima_model_exploitative <- auto.arima(exploitative)
> summary(arima_model_exploitative)
Series: exploitative
ARIMA(2,1,1)
Coefficients:
ar1 ar2 ma1
0.4744 0.1671 -0.9653
s.e. 0.1021 0.1007 0.0323
sigma^2 = 343.6: log likelihood = -467.68
AIC=943.35 AICc=943.74 BIC=954.08
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 2.152378 18.19375 13.5603 -Inf Inf 0.5478338 -0.01933472
```

Finally, the ARIMA model yields an RMSE score of 18.2 and an MAE score of 13.6. Ultimately, by a small margin, the ARIMA model is most likely to provide the best forecast. Next, we forecast and plot the results. Given that ARIMA is most reliable under these circumstances, we’ll focus on visualizing ARIMA results; you can experiment with naive and SES plots on your own via the scripts or the notebook.

**Forecasts & Plots**

Generating a plot of *AllEvents* is as easy as:

```
autoplot(as.ts(AllEvents))
```

**Figure 6:** CISSM CAD AllEvents plot

This is just as easy with *disruptive* or *exploitative* events exclusively with the likes of `autoplot(as.ts(disruptive))`

or `autoplot(as.ts(exploitative))`

.

To forecast the *exploitative* ARIMA model in an individual plot, utilize:

```
forecast(arima_model_exploitative) %>% autoplot()
```

**Figure 7:** CISSM CAD exploitative events forecast plot

The light and dark areas correspond to the 95% and 80% confidence intervals (CI) respectively.

You can join multiple plots to compare outcomes side by side as follows.

```
naiveEXP = forecast(naive_model_exploitative) %>% autoplot()
sesEXP = forecast(ses_model_exploitative) %>% autoplot()
arimaEXP = forecast(arima_model_exploitative) %>% autoplot()
multi.pageEXP <- ggarrange(naiveEXP, sesEXP, arimaEXP, nrow = 3, ncol = 1)
multi.pageEXP
```

**Figure 8:** CISSM CAD exploitative events multi-model forecast plot

You may be wondering what ARIMA(2,1,1) refers to in our plots. A nonseasonal ARIMA model, which this is, is classified as an “ARIMA(p,d,q)” model, where: p is the number of autoregressive terms, d is the number of nonseasonal differences needed for stationarity, and q is the number of lagged forecast errors in the prediction equation. Therefore, in this case, (2,1,1) is p,d,q found by the *auto.arima* process indicating that we have two auto-regessive terms, one difference, and one moving average term in our series (Nau, 2020).

**Conclusion**

Hopefully, this effort has been useful and insightful for security analysts as well as fledgling data scientists in the security realm. It’s no surprise that I orient towards the practices of visualization; I have found all methods deployed here to be useful, effective, and durable for future use. It is my desire that you benefit similarly, and that this opens some doors for you, literally and figuratively.

Cheers…until next time.

Russ McRee | @holisticinfosec | infosec.exchange/@holisticinfosec | LinkedIn.com/in/russmcree

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