Showing Vulnerability to a Machine: Automated Prioritization of Software Vulnerabilities


If a software vulnerability can be detected and remedied, then a
potential intrusion is prevented. While not all software
vulnerabilities are known, 86
percent of vulnerabilities leading to a data breach were
, though there is some
risk of inadvertent damage when applying software patches. When new
vulnerabilities are identified they are published in the Common
Vulnerabilities and Exposures (CVE) dictionary by vulnerability
, such as the National Vulnerability Database (NVD).

The Common Vulnerabilities Scoring System (CVSS) provides a metric
for prioritization that is meant to capture the potential severity of
a vulnerability. However, it has been criticized
for a lack of timeliness, vulnerable population representation,
normalization, rescoring and broader expert consensus that can lead to
For example, some of the
worst exploits
have been assigned low CVSS scores. Additionally,
CVSS does not measure the vulnerable population size, which many
practitioners have stated they expect it to score. The design of the
current CVSS system leads to too
severe vulnerabilities, which causes user fatigue. ­

To provide a more timely and broad approach, we use machine learning
to analyze users’ opinions about the severity of vulnerabilities by
examining relevant tweets. The model predicts whether users believe a
vulnerability is likely to affect a large number of people, or if the
vulnerability is less dangerous and unlikely to be exploited. The
predictions from our model are then used to score vulnerabilities
faster than traditional approaches, like CVSS, while providing a
different method for measuring severity, which better reflects
real-world impact.

Our work uses nowcasting to address this important gap
of prioritizing early-stage CVEs to know if they are urgent or not.
Nowcasting is the economic discipline of determining a trend or a
trend reversal objectively in real time. In this case, we are
recognizing the value of linking social media responses to the release
of a CVE after it is released, but before it is scored by CVSS. Scores
of CVEs should ideally be available as soon as possible after the CVE
is released, while the current process often hampers prioritization of
triage events and ultimately slows response to severe vulnerabilities.
This crowdsourced approach reflects numerous practitioner observations
about the size and widespread nature of the vulnerable population, as
shown in Figure 1. For example, in the Mirai
botnet incident in 2017
a massive number of vulnerable IoT
devices were compromised leading to the largest Denial of Service
(DoS) attack on the internet at the time.

Figure 1: Tweet showing social commentary
on a vulnerability that reflects severity

Model Overview

Figure 2 illustrates the overall process that starts with analyzing
the content of a tweet and concludes with two forecasting evaluations.
First, we run Named Entity Recognition (NER) on tweet contents to
extract named entities. Second, we use two classifiers to test the
relevancy and severity towards the pre-identified entities. Finally,
we match the relevant and severe tweets to the corresponding CVE.

Figure 2: Process overview of the steps
in our CVE score forecasting

Each tweet is associated to CVEs by inspecting URLs or the contents
hosted at a URL. Specifically, we link a CVE to a tweet if it contains
a CVE number in the message body, or if the URL content contains a
CVE. Each tweet must be associated with a single CVE and must be
classified as relevant to security-related topics to be scored. The
first forecasting task considers how well our model can predict the
CVSS rankings ahead of time. The second task is predicting future
exploitation of the vulnerability for a CVE based on Symantec
Antivirus Signatures and Exploit DB. The rationale is that eventual
presence in these lists indicates not just that exploits can exist or
that they do exist, but that they also are publicly available.

Modeling Approach

Predicting the CVSS scores and exploitability from Twitter data
involves multiple steps. First, we need to find appropriate
representations (or features) for our natural language to be processed
by machine learning models. In this work, we use two natural language
processing methods in natural language processing for extracting
features from text: (1) N-grams features, and (2) Word embeddings.
Second, we use these features to predict if the tweet is relevant to
the cyber security field using a classification model. Third, we use
these features to predict if the relevant tweets are making strong
statements indicative of severity. Finally, we match the severe and
relevant tweets up to the corresponding CVE.

N-grams are word sequences, such as word pairs for 2-gram or word
triples for 3-grams. In other words, they are contiguous sequence of n
words from a text. After we extract these n-grams, we can represent
original text as a bag-of-ngrams. Consider the sentence:

A criticial vulnerability was found in Linux.

If we consider all 2-gram features, then the bag-of-ngrams
representation contains “A critical”, “critical vulnerability”, etc.

Word embeddings are a way to learn the meaning of a word by how it
was used in previous contexts, and then represent that meaning in a
vector space. Word embeddings know the meaning of a word by the
company it keeps, more formally known as the distribution
. These
word embedding representations
are machine friendly, and similar
words are often assigned similar representations. Word embeddings are
domain specific. In our work, we additionally train terminology
specific to cyber security topics, such as related words to
threats are defenses, cyberrisk,
cybersecurity, threat, and iot-based. The
embedding would allow a classifier to implicitly combine the knowledge
of similar words and the meaning of how concepts differ. Conceptually,
word embeddings may help a classifier use these embeddings to
implicitly associate relationships such as:

device + infected = zombie

where an entity called device has a mechanism applied called
infected (malicious software infecting it) then it becomes a zombie.

To address issues where social media tweets differ linguistically
from natural language, we leverage previous research and
from the Natural Language Processing (NLP) community.
This addresses specific nuances like less consistent capitalization,
and stemming to account for a variety of special characters like ‘@’
and ‘#’.

Figure 3: Tweet demonstrating value of
identifying named entities in tweets in order to gauge severity

Named Entity Recognition (NER) identifies the words that construct
nouns based on their context within a sentence, and benefits from our
embeddings incorporating cyber security words. Correctly identifying
the nouns using NER is important to how we parse a sentence. In Figure
3, for instance, NER facilitates Windows 10 to be understood as
an entity while October 2018 is treated as elements of a date.
Without this ability, the text in Figure 3 may be confused with the
physical notion of windows in a building.

Once NER tokens are identified, they are used to test if a
vulnerability affects them. In the Windows 10 example,
Windows 10 is the entity and the classifier will predict
whether the user believes there is a serious vulnerability affecting
Windows 10. One prediction is made per entity, even if a
tweet contains multiple entities. Filtering tweets that do not contain
named entities reduces tweets to only those relevant to expressing
observations on a software vulnerability.

From these normalized tweets, we can gain insight into how strongly
users are emphasizing the importance of the vulnerability by observing
their choice of words. The choice of adjective is instrumental in the
classifier capturing the strong opinions. Twitter users often use
strong adjectives and superlatives to convey magnitude in a tweet or
when stressing the importance of something related to a vulnerability
like in Figure 4. This magnitude often indicates to the model when a
vulnerability’s exploitation is widespread. Table 1 shows our analysis
of important adjectives that tend to indicate a more severe vulnerability.

Figure 4: Tweet showing strong adjective use

Table 1: Log-odds ratios for words
correlated with highly-severe CVEs

Finally, the processed features are evaluated with two different
classifiers to output scores to predict relevancy and severity. When a
named entity is identified all words comprising it are replaced with a
single token to prevent the model from biasing toward that entity. The
first model uses an n-gram approach where sequences of two, three, and
four tokens are input into a logistic regression model. The second
approach uses a one-dimensional Convolutional Neural Network (CNN),
comprised of an embedding layer, a dropout layer then a fully
connected layer, to extract features from the tweets.

Evaluating Data

To evaluate the performance of our approach, we curated a dataset of
6,000 tweets containing the keywords vulnerability or
ddos from Dec 2017 to July 2018. Workers on Amazon’s Mechanical
Turk platform were asked to judge whether a user believed a
vulnerability they were discussing was severe. For all labeling,
multiple users must independently agree on a label, and multiple
statistical and expert-oriented techniques are used to eliminate
spurious annotations. Five annotators were used for the labels in the
relevancy classifier and ten annotators were used for the severity
annotation task. Heuristics were used to remove unserious respondents;
for example, when users did not agree with other annotators for a
majority of the tweets. A subset of tweets were expert-annotated and
used to measure the quality of the remaining annotations.

Using the features extracted from tweet contents, including word
embeddings and n-grams, we built a model using the annotated data from
Amazon Mechanical Turk as labels. First, our model learns if tweets
are relevant to a security threat using the annotated data as ground
truth. This would remove a statement like “here is how you can
#exploit tax loopholes” from being confused with a cyber
security-related discussion about a user exploiting a software
vulnerability as a malicious tool. Second, a forecasting model scores
the vulnerability based on whether annotators perceived the threat to
be severe.

CVSS Forecasting Results

Both the relevancy classifier and the severity classifier were
applied to various datasets. Data was collected from December 2017 to
July 2018. Most notably 1,000 tweets were held-out from the original
6,000 to be used for the relevancy classifier and 466 tweets were
held-out for the severity classifier. To measure the performance, we
use the Area
Under the precision-recall Curve
(AUC), which is a correctness
score that summarizes the tradeoffs of minimizing the two types of
errors (false positive vs false negative), with scores near 1
indicating better performance.

  • The relevancy classifier
    scored 0.85
  • The severity classifier using the CNN scored
  • The severity classifier using a Logistic Regression
    model, without embeddings, scored 0.54

Next, we evaluate how well this approach can be used to forecast
CVSS ratings. In this evaluation, all tweets must occur a minimum of
five days ahead of CVSS scores. The severity forecast score for a CVE
is defined as the maximum severity score among the tweets which are
relevant and associated with the CVE. Table 1 shows the results of
three models: randomly guessing the severity, modeling based on the
volume of tweets covering a CVE, and the ML-based approach described
earlier in the post. The scoring metric in Table 2 is precision at top
K using our logistic regression model. For example, where K=100, this
is a way for us to identify what percent of the 100 most severe
vulnerabilities were correctly predicted. The random model would
predicted 59, while our model predicted 78 of the top 100 and all ten
of the most severe vulnerabilities.

Table 2: Comparison of random simulated
predictions, a model based just on quantitative features like
“likes”, and the results of our model

Exploit Forecasting Results

We also measured the practical ability of our model to identify the
exploitability of a CVE in the wild, since this is one of the
motivating factors for tracking. To do this, we collected severe
vulnerabilities that have known exploits by their presence in the
following data sources:

  • Symantec Antivirus
  • Symantec Intrusion Prevention System
  • ExploitDB catalog

The dataset for exploit forecasting was comprised of 377,468 tweets
gathered from January 2016 to November 2017. Of the 1,409 CVEs used in
our forecasting evaluation, 134 publicly weaponized vulnerabilities
were found across all three data sources.

Using CVEs from the aforementioned sources as ground truth, we find
our CVE classification model is more predictive of detecting
operationalized exploits
from the vulnerabilities than CVSS.
Table 3 shows precision scores illustrating seven of the top ten most
severe CVEs and 21 of the top 100 vulnerabilities were found to have
been exploited in the wild. Compare that to one of the top ten and 16
of the top 100 from using the CVSS score itself. The recall scores
show the percentage of our 134 weaponized vulnerabilities found in our
K examples. In our top ten vulnerabilities, seven were found to be in
the 134 (5.2%), while the CVSS scoring’s top ten included only one
(0.7%) CVE being exploited.

Table 3: Precision and recall scores for
the top 10, 50 and 100 vulnerabilities when comparing CVSS scoring,
our simplistic volume model and our NLP model


Preventing vulnerabilities is critical to an organization’s
information security posture, as it effectively mitigates some cyber
security breaches. In our work, we found that social media content
that pre-dates CVE scoring releases can be effectively used by machine
learning models to forecast vulnerability scores and prioritize
vulnerabilities days before they are made available. Our approach
incorporates a novel social sentiment component, which CVE scores do
not, and it allows scores to better predict real-world exploitation of
vulnerabilities. Finally, our approach allows for a more practical
prioritization of software vulnerabilities effectively indicating the
few that are likely to be weaponized by attackers. NIST has acknowledged
that the current CVSS methodology is insufficient. The current process
of scoring CVSS is expected to be replaced by ML-based solutions by
October 2019, with limited human involvement. However, there is no
indication of utilizing a social component in the scoring effort.

This work was led by researchers at Ohio State under the IARPA
program, with support from Leidos and FireEye. This work was
originally presented at NAACL in
June 2019, our
describes this work in more detail and was also covered by

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