Exploratory Data Analysis with CISSM Cyber Attacks Database - Part 2

Published: 2023-05-09
Last Updated: 2023-05-10 00:09:39 UTC
by Russ McRee (Version: 1)
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Part 2 of 2

Part 1


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.


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

         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

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 

 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

    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

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 

         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:


AllEvents Plot

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)

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).


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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Microsoft May 2023 Patch Tuesday

Published: 2023-05-09
Last Updated: 2023-05-09 17:41:35 UTC
by Renato Marinho (Version: 1)
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This month we got patches for 49 vulnerabilities. Of these, 6 are critical, and 2 are already being exploited, according to Microsoft.

One of the exploited vulnerabilities is a Win32k Elevation of Privilege Vulnerability (CVE-2023-29336). This vulnerability has low attack complexity, low privilege, and none user interaction. The attack vector is local, the CVSS is 7.8, and the severity is Important.

The second exploited vulnerability is Secure Boot Security Feature Bypass Vulnerability (CVE-2023-24932). According to the advisory, to exploit the vulnerability, an attacker who has physical access or Administrative rights to a target device could install an affected boot policy. The CVSS for this vulnerability is 6.7 and its severity is Important.

About the critical vulnerabilities, there is a Remote Code Execution (RCE) affecting Windows Network File System (CVE-2023-24941). According to the advisory, this vulnerability could be exploited over the network by making an unauthenticated, specially crafted call to a Network File System (NFS) service to trigger a Remote Code Execution (RCE). The advisory also details a mitigation procedure. The CVSS for this vulnerability is 9.8 – the highest for this month.

A second critical vulnerability worth mentioning is an RCE affecting Windows Lightweight Directory Access Protocol (LDAP) (CVE-2023-28283). According to the advisory, an unauthenticated attacker who successfully exploited this vulnerability could gain code execution through a specially crafted set of LDAP calls to execute arbitrary code within the context of the LDAP service. The attack complexity is high, which means that successful exploitation of this vulnerability requires an attacker to win a race condition. The CVSS for this vulnerability is 8.1.

See my dashboard for a more detailed breakout: https://patchtuesdaydashboard.com/

May 2023 Security Updates

CVE Disclosed Exploited Exploitability (old versions) current version Severity CVSS Base (AVG) CVSS Temporal (AVG)
AV1 Video Extension Remote Code Execution Vulnerability
CVE-2023-29340 No No - - Important 7.8 6.8
CVE-2023-29341 No No - - Important 7.8 6.8
Chromium: CVE-2023-2459 Inappropriate implementation in Prompts
CVE-2023-2459 No No - - -    
Chromium: CVE-2023-2460 Insufficient validation of untrusted input in Extensions
CVE-2023-2460 No No - - -    
Chromium: CVE-2023-2462 Inappropriate implementation in Prompts
CVE-2023-2462 No No - - -    
Chromium: CVE-2023-2463 Inappropriate implementation in Full Screen Mode
CVE-2023-2463 No No - - -    
Chromium: CVE-2023-2464 Inappropriate implementation in PictureInPicture
CVE-2023-2464 No No - - -    
Chromium: CVE-2023-2465 Inappropriate implementation in CORS
CVE-2023-2465 No No - - -    
Chromium: CVE-2023-2466 Inappropriate implementation in Prompts
CVE-2023-2466 No No - - -    
Chromium: CVE-2023-2467 Inappropriate implementation in Prompts
CVE-2023-2467 No No - - -    
Chromium: CVE-2023-2468 Inappropriate implementation in PictureInPicture
CVE-2023-2468 No No - - -    
Microsoft Access Denial of Service Vulnerability
CVE-2023-29333 No No - - Important 3.3 2.9
Microsoft Edge (Chromium-based) Elevation of Privilege Vulnerability
CVE-2023-29350 No No Less Likely Less Likely Important 7.5 6.5
Microsoft Edge (Chromium-based) Security Feature Bypass Vulnerability
CVE-2023-29354 No No Less Likely Less Likely Moderate 4.7 4.1
Microsoft Excel Remote Code Execution Vulnerability
CVE-2023-24953 No No - - Important 7.8 6.8
Microsoft Office Remote Code Execution Vulnerability
CVE-2023-29344 No No - - Important 7.8 6.8
Microsoft Remote Desktop app for Windows Information Disclosure Vulnerability
CVE-2023-28290 No No - - Important 5.3 4.6
Microsoft SharePoint Server Information Disclosure Vulnerability
CVE-2023-24954 No No - - Important 6.5 5.7
Microsoft SharePoint Server Remote Code Execution Vulnerability
CVE-2023-24955 No No - - Critical 7.2 6.3
Microsoft SharePoint Server Spoofing Vulnerability
CVE-2023-24950 No No - - Important 6.5 5.7
Microsoft Teams Information Disclosure Vulnerability
CVE-2023-24881 No No - - Important 6.5 5.7
Microsoft Word Security Feature Bypass Vulnerability
CVE-2023-29335 No No - - Important 7.5 6.5
Remote Desktop Client Remote Code Execution Vulnerability
CVE-2023-24905 No No - - Important 7.8 6.8
Remote Procedure Call Runtime Denial of Service Vulnerability
CVE-2023-24942 No No - - Important 7.5 6.5
Secure Boot Security Feature Bypass Vulnerability
CVE-2023-24932 Yes Yes - - Important 6.7 6.2
Server for NFS Denial of Service Vulnerability
CVE-2023-24939 No No - - Important 7.5 6.5
SysInternals Sysmon for Windows Elevation of Privilege Vulnerability
CVE-2023-29343 No No - - Important 7.8 6.8
Visual Studio Code Information Disclosure Vulnerability
CVE-2023-29338 No No - - Important 5.0 4.5
Win32k Elevation of Privilege Vulnerability
CVE-2023-24902 No No - - Important 7.8 6.8
CVE-2023-29336 No Yes - - Important 7.8 6.8
Windows Backup Service Elevation of Privilege Vulnerability
CVE-2023-24946 No No - - Important 7.8 6.8
Windows Bluetooth Driver Elevation of Privilege Vulnerability
CVE-2023-24948 No No - - Important 7.4 6.4
Windows Bluetooth Driver Information Disclosure Vulnerability
CVE-2023-24944 No No - - Important 6.5 5.7
Windows Bluetooth Driver Remote Code Execution Vulnerability
CVE-2023-24947 No No - - Important 8.8 7.7
Windows Driver Revocation List Security Feature Bypass Vulnerability
CVE-2023-28251 No No - - Important 5.5 4.8
Windows Graphics Component Elevation of Privilege Vulnerability
CVE-2023-24899 No No - - Important 7.0 6.1
Windows Installer Elevation of Privilege Vulnerability
CVE-2023-24904 No No - - Important 7.1 6.2
Windows Kernel Elevation of Privilege Vulnerability
CVE-2023-24949 No No - - Important 7.8 6.8
Windows Lightweight Directory Access Protocol (LDAP) Remote Code Execution Vulnerability
CVE-2023-28283 No No - - Critical 8.1 7.1
Windows MSHTML Platform Security Feature Bypass Vulnerability
CVE-2023-29324 No No - - Important 6.5 5.7
Windows NFS Portmapper Information Disclosure Vulnerability
CVE-2023-24901 No No - - Important 7.5 6.5
Windows NTLM Security Support Provider Information Disclosure Vulnerability
CVE-2023-24900 No No - - Important 5.9 5.2
Windows Network File System Remote Code Execution Vulnerability
CVE-2023-24941 No No - - Critical 9.8 8.5
Windows OLE Remote Code Execution Vulnerability
CVE-2023-29325 Yes No - - Critical 8.1 7.3
Windows Pragmatic General Multicast (PGM) Denial of Service Vulnerability
CVE-2023-24940 No No - - Important 7.5 6.5
Windows Pragmatic General Multicast (PGM) Remote Code Execution Vulnerability
CVE-2023-24943 No No - - Critical 9.8 8.5
Windows SMB Denial of Service Vulnerability
CVE-2023-24898 No No - - Important 7.5 6.5
Windows Secure Socket Tunneling Protocol (SSTP) Remote Code Execution Vulnerability
CVE-2023-24903 No No - - Critical 8.1 7.1
Windows iSCSI Target Service Information Disclosure Vulnerability
CVE-2023-24945 No No - - Important 5.5 4.8

Renato Marinho
Morphus Labs| LinkedIn|Twitter

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ISC Stormcast For Tuesday, May 9th, 2023 https://isc.sans.edu/podcastdetail.html?id=8488


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