Visualizations are finally in place and looking good enough to show others. Now all that is needed is to fill in the text and clean up the flow. One or two more sessions, and then I can sent out reading drafts.
This commit is contained in:
shelldweller
parent
310cb800f8
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1c4ef5bb15
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@ -303,18 +303,268 @@ Now we can compare the relative spread of each feature by calculating the coeffi
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```{r cv-results, echo=FALSE, fig.align="center"}
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# Summarize results in a table and a plot
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# Summarize results in a table
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knitr::kable(coeff_vars)
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plot(coeff_vars)
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# Scatterplot, not very interesting
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# plot(coeff_vars)
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```
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From this, it appears that `r selected_features[1]` has the widest range of variability, followed by `r selected_features[2]`. These are also the features that are most strongly skewed to the right, meaning that a few addresses have really high values for each of these features while the bulk of the data set has very low values for these numbers.
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Now do the following (after filling in methods, results, and conclusions, since those are done already):
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!) Break into groups somehow. Graph variables per group? Show how the variables are distributed for each ransomware group? Percent ransomware per each day of the week, for example. Is ransomware more prevalent on a particular day of the week? Break other numerical values into bins, and graph percentage per bin. Look for trends and correlations between groups/variables, and display them here. MORE OF THIS....
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Taking the feature with the highest variation `r selected_features[1]`, let us take a look at the distribution for individual ransomware families. Perhaps there is a similarity across families.
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```{r variation histograms, echo=FALSE, fig.show="hold", out.width='35%', warning=FALSE}
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# Density plots of the feature with highest variation
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selected_feature1 <- selected_features[1]
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ransomware_big_families <- ransomware %>%
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mutate(selected_feature1 = as.numeric(selected_feature1))
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# Note: Putting these graphs into a for loop breaks some of the formatting.
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# Low membership makes some of the graphs not very informative
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# Relatively boring graphs have been commented out to save time and space.
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# These can be uncommented if one wishes.
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# Label 1
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ransomware_big_families %>%
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filter(label==levels(ransomware_big_families$label)[1]) %>%
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select(income) %>%
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ggplot(aes(x=income, y = ..density..)) +
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geom_density(col = "green")+
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ggtitle(levels(ransomware_big_families$label)[1]) +
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scale_x_continuous(trans='log2')
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# Label 2
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#ransomware_big_families %>%
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# filter(label==levels(ransomware_big_families$label)[2]) %>%
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# select(income) %>%
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# ggplot(aes(x=income, y = ..density..)) +
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# geom_density(col = "green", size = .5)+
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# ggtitle(levels(ransomware_big_families$label)[2]) +
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# scale_x_continuous(trans='log2')
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# Label 3
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#ransomware_big_families %>%
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# filter(label==levels(ransomware_big_families$label)[3]) %>%
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# select(income) %>%
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# ggplot(aes(x=income, y = ..density..)) +
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# geom_density(col = "green")+
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# ggtitle(levels(ransomware_big_families$label)[3]) +
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# scale_x_continuous(trans='log2')
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# Label 4
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ransomware_big_families %>%
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filter(label==levels(ransomware_big_families$label)[4]) %>%
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select(income) %>%
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ggplot(aes(x=income, y = ..density..)) +
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geom_density(col = "green")+
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ggtitle(levels(ransomware_big_families$label)[4]) +
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scale_x_continuous(trans='log2')
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# Label 5
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ransomware_big_families %>%
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filter(label==levels(ransomware_big_families$label)[5]) %>%
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select(income) %>%
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ggplot(aes(x=income, y = ..density..)) +
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geom_density(col = "green")+
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ggtitle(levels(ransomware_big_families$label)[5]) +
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scale_x_continuous(trans='log2')
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# Label 6
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ransomware_big_families %>%
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filter(label==levels(ransomware_big_families$label)[6]) %>%
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select(income) %>%
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ggplot(aes(x=income, y = ..density..)) +
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geom_density(col = "green")+
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ggtitle(levels(ransomware_big_families$label)[6]) +
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scale_x_continuous(trans='log2')
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# Label 7
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ransomware_big_families %>%
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filter(label==levels(ransomware_big_families$label)[7]) %>%
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select(income) %>%
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ggplot(aes(x=income, y = ..density..)) +
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geom_density(col = "green")+
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ggtitle(levels(ransomware_big_families$label)[7]) +
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scale_x_continuous(trans='log2')
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# Label 8
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ransomware_big_families %>%
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filter(label==levels(ransomware_big_families$label)[8]) %>%
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select(income) %>%
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ggplot(aes(x=income, y = ..density..)) +
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geom_density(col = "green")+
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ggtitle(levels(ransomware_big_families$label)[8]) +
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scale_x_continuous(trans='log2')
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# Label 9
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#ransomware_big_families %>%
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# filter(label==levels(ransomware_big_families$label)[9]) %>%
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# select(income) %>%
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# ggplot(aes(x=income, y = ..density..)) +
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# geom_density(col = "green")+
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# ggtitle(levels(ransomware_big_families$label)[9]) +
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# scale_x_continuous(trans='log2')
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# Label 10
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ransomware_big_families %>%
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filter(label==levels(ransomware_big_families$label)[10]) %>%
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select(income) %>%
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ggplot(aes(x=income, y = ..density..)) +
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geom_density(col = "green")+
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ggtitle(levels(ransomware_big_families$label)[10]) +
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scale_x_continuous(trans='log2')
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# Label 11
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ransomware_big_families %>%
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filter(label==levels(ransomware_big_families$label)[11]) %>%
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select(income) %>%
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ggplot(aes(x=income, y = ..density..)) +
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geom_density(col = "green")+
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ggtitle(levels(ransomware_big_families$label)[11]) +
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scale_x_continuous(trans='log2')
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# Label 12
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ransomware_big_families %>%
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filter(label==levels(ransomware_big_families$label)[12]) %>%
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select(income) %>%
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ggplot(aes(x=income, y = ..density..)) +
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geom_density(col = "green")+
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ggtitle(levels(ransomware_big_families$label)[12]) +
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scale_x_continuous(trans='log2')
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# Label 13
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ransomware_big_families %>%
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filter(label==levels(ransomware_big_families$label)[13]) %>%
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select(income) %>%
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ggplot(aes(x=income, y = ..density..)) +
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geom_density(col = "green")+
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ggtitle(levels(ransomware_big_families$label)[13]) +
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scale_x_continuous(trans='log2')
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# Label 14
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ransomware_big_families %>%
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filter(label==levels(ransomware_big_families$label)[14]) %>%
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select(income) %>%
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ggplot(aes(x=income, y = ..density..)) +
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geom_density(col = "green")+
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ggtitle(levels(ransomware_big_families$label)[14]) +
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scale_x_continuous(trans='log2')
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# Label 15
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ransomware_big_families %>%
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filter(label==levels(ransomware_big_families$label)[15]) %>%
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select(income) %>%
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ggplot(aes(x=income, y = ..density..)) +
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geom_density(col = "green")+
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ggtitle(levels(ransomware_big_families$label)[15]) +
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scale_x_continuous(trans='log2')
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# Label 16
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ransomware_big_families %>%
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filter(label==levels(ransomware_big_families$label)[16]) %>%
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select(income) %>%
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ggplot(aes(x=income, y = ..density..)) +
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geom_density(col = "green")+
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ggtitle(levels(ransomware_big_families$label)[16]) +
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scale_x_continuous(trans='log2')
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# Label 17
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#ransomware_big_families %>%
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# filter(label==levels(ransomware_big_families$label)[17]) %>%
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# select(income) %>%
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# ggplot(aes(x=income, y = ..density..)) +
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# geom_density(col = "green", size = .5)+
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# ggtitle(levels(ransomware_big_families$label)[17]) +
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# scale_x_continuous(trans='log2')
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# Label 18
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ransomware_big_families %>%
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filter(label==levels(ransomware_big_families$label)[18]) %>%
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select(income) %>%
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ggplot(aes(x=income, y = ..density..)) +
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geom_density(col = "green")+
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ggtitle(levels(ransomware_big_families$label)[18]) +
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scale_x_continuous(trans='log2')
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# Label 19
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#ransomware_big_families %>%
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# filter(label==levels(ransomware_big_families$label)[19]) %>%
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# select(income) %>%
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# ggplot(aes(x=income, y = ..density..)) +
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# geom_density(col = "green")+
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# ggtitle(levels(ransomware_big_families$label)[19]) +
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# scale_x_continuous(trans='log2')
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# Label 20
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ransomware_big_families %>%
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filter(label==levels(ransomware_big_families$label)[20]) %>%
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select(income) %>%
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ggplot(aes(x=income, y = ..density..)) +
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geom_density(col = "green")+
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ggtitle(levels(ransomware_big_families$label)[20]) +
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scale_x_continuous(trans='log2')
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# Label 21
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#ransomware_big_families %>%
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# filter(label==levels(ransomware_big_families$label)[21]) %>%
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# select(income) %>%
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# ggplot(aes(x=income, y = ..density..)) +
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# geom_density(col = "green", size = .5)+
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# ggtitle(levels(ransomware_big_families$label)[21]) +
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# scale_x_continuous(trans='log2')
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# Label 22
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ransomware_big_families %>%
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filter(label==levels(ransomware_big_families$label)[22]) %>%
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select(income) %>%
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ggplot(aes(x=income, y = ..density..)) +
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geom_density(col = "green")+
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ggtitle(levels(ransomware_big_families$label)[22]) +
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scale_x_continuous(trans='log2')
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# Label 23
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ransomware_big_families %>%
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filter(label==levels(ransomware_big_families$label)[23]) %>%
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select(income) %>%
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ggplot(aes(x=income, y = ..density..)) +
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geom_density(col = "green")+
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ggtitle(levels(ransomware_big_families$label)[23]) +
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scale_x_continuous(trans='log2')
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# Label 24
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ransomware_big_families %>%
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filter(label==levels(ransomware_big_families$label)[24]) %>%
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select(income) %>%
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ggplot(aes(x=income, y = ..density..)) +
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geom_density(col = "green")+
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ggtitle(levels(ransomware_big_families$label)[24]) +
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scale_x_continuous(trans='log2')
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# Label 25
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#ransomware_big_families %>%
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# filter(label==levels(ransomware_big_families$label)[25]) %>%
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# select(income) %>%
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# ggplot(aes(x=income, y = ..density..)) +
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# geom_density(col = "green", size = .5)+
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# ggtitle(levels(ransomware_big_families$label)[25]) +
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# scale_x_continuous(trans='log2')
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# Label 26
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#ransomware_big_families %>%
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# filter(label==levels(ransomware_big_families$label)[26]) %>%
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# select(income) %>%
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# ggplot(aes(x=income, y = ..density..)) +
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# geom_density(col = "green")+
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# ggtitle(levels(ransomware_big_families$label)[26]) +
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# scale_x_continuous(trans='log2')
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# Label 27
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ransomware_big_families %>%
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filter(label==levels(ransomware_big_families$label)[27]) %>%
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select(income) %>%
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ggplot(aes(x=income, y = ..density..)) +
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geom_density(col = "green")+
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ggtitle(levels(ransomware_big_families$label)[27]) +
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scale_x_continuous(trans='log2')
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# Label 28
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ransomware_big_families %>%
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filter(label==levels(ransomware_big_families$label)[28]) %>%
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select(income) %>%
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ggplot(aes(x=income, y = ..density..)) +
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geom_density(col = "green")+
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ggtitle(levels(ransomware_big_families$label)[28]) +
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scale_x_continuous(trans='log2')
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# Label 29
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ransomware_big_families %>%
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filter(label==levels(ransomware_big_families$label)[29]) %>%
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select(income) %>%
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ggplot(aes(x=income, y = ..density..)) +
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geom_density(col = "green")+
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ggtitle(levels(ransomware_big_families$label)[29]) +
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scale_x_continuous(trans='log2')
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```
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```{r shrimp-percentage, echo=FALSE, include=FALSE}
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@ -681,55 +931,55 @@ cm_labels$byClass %>% knitr::kable()
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Here are some graphs, tell a bit more about them.
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```{r binary som graphs, echo=FALSE, fig.align="center"}
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```{r binary som graphs, echo=FALSE, fig.show="hold", out.width='35%'}
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# Be careful with these, some are really large and take a long time to produce.
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# Visualize neural network mapping
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plot(som_model2, type = 'mapping', pch = 19, palette.name = topo.colors)
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cat(" \n")
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#cat(" \n")
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# Distance map
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plot(som_model2, type = 'quality', pch = 19, palette.name = topo.colors)
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cat(" \n")
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#cat(" \n")
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# Visualize counts
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plot(som_model2, type = 'counts', pch = 19, palette.name = topo.colors)
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cat(" \n")
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#cat(" \n")
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# Visualize fan diagram
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plot(som_model2, type = 'codes', pch = 19, palette.name = topo.colors)
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cat(" \n")
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#cat(" \n")
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# Visualize heatmap for variable 1
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plot(som_model2, type = 'property', property = som_model2$codes[[1]][,1],
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main=colnames(train_num)[1], pch = 19, palette.name = topo.colors)
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cat(" \n")
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#cat(" \n")
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# Visualize heatmap for variable 2
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plot(som_model2, type = 'property', property = som_model2$codes[[1]][,2],
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main=colnames(train_num)[2], pch = 19, palette.name = topo.colors)
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cat(" \n")
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#cat(" \n")
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# Visualize heatmap for variable 3
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plot(som_model2, type = 'property', property = som_model2$codes[[1]][,3],
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main=colnames(train_num)[3], pch = 19, palette.name = topo.colors)
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cat(" \n")
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#cat(" \n")
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# Visualize heatmap for variable 4
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plot(som_model2, type = 'property', property = som_model2$codes[[1]][,4],
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main=colnames(train_num)[4], pch = 19, palette.name = topo.colors)
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cat(" \n")
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#cat(" \n")
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# Visualize heatmap for variable 5
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plot(som_model2, type = 'property', property = som_model2$codes[[1]][,5],
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main=colnames(train_num)[5], pch = 19, palette.name = topo.colors)
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cat(" \n")
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#cat(" \n")
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# Visualize heatmap for variable 6
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plot(som_model2, type = 'property', property = som_model2$codes[[1]][,6],
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main=colnames(train_num)[6], pch = 19, palette.name = topo.colors)
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cat(" \n")
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#cat(" \n")
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```
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Binary file not shown.
Binary file not shown.
Binary file not shown.
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@ -1,8 +1,24 @@
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for(i in 1:30){
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print(
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ransomware %>% filter(label==levels(ransomware$label)[i]) %>%
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#i <- 1
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ransomware_big_families <- ransomware %>%
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group_by(label) %>% filter(n() > 1) %>%
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ungroup()
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ransomware_big_families <- ransomware_big_families %>% mutate(income = as.numeric(income))
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for(i in 1:length(levels(ransomware_big_families$label))){
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ggp <-
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ransomware_big_families %>% filter(label==levels(ransomware_big_families$label)[i]) %>%
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select(income) %>%
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ggplot(aes(x=income, y = ..density..)) + geom_histogram(bins="30") +
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geom_density(col = "green", size = .5)
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)
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}
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ggplot(aes(x=income, y = ..density..)) +
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# geom_histogram(bins="30") +
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# geom_bar() +
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geom_density(col = "green", size = .5)+
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ggtitle(levels(ransomware_big_families$label)[i]) +
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scale_x_continuous(trans='log2')
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print(ggp)
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print(i)
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print(levels(ransomware_big_families$label)[i])
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}
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