Statistics


This OCS section has been realized to provide statistics on data stored in our database.


Databases

Our database

The Cybersecurity Observatory hosts different databases each with specific data related to the various services offered by the website. These data are updated daily and they grow as time passes. The pie chart on the right shows the percentage of space occupied by each data type. The chart is interactive, e.g., it is possible to click on the "TWEET" label in legenda to hide tweet data and see only the other data.

Tweet

Tweet Analysis

We began collecting tweets in June 2018. Our target is to understand how every-day talks involve Cybersecurity and, in the meanwhile, gain from these discussion the widespread of new malwares orthe discovery of a new vulnerability. Up to now we've collected 135.000 tweets from around the world, all related to cyber-security.



CVE

Vulnerability report

One of our most important database hosts the list of Common Vulnerabilities and Exposures (CVE) from 1998. Every time a new vulnerability is found, an authority gets notified and it will then assign a unique code to it. Generally CVE publication date is not the same of the discovery date, this because it is required some time to fix the bug.After some time, the CVE is public even if it is still not fixed. Our observatory links CVE-related informations with other data in order to provide additional informations and provide knowledge to end users.

CVE danger level

The authority also ranks the discovered bug, assigning to the CVE a 3-level score low, medium, high. This helps end users to understand how much a bug is dangerous. This score is called Common Vulnerabilities Scoring System (CVSS). The chart highlights the percentage of each score in our database.

Exploit Analysis

By "Exploit" we define the code that effectively exploits a vulnerability. The charts shows that there are 4 types of exploit: remote, webapps, local, dos. They identify the different ways in which is possible to exploit a vulnerability. As you can see webapps and dos Exploits have become more common with the widespread of Internet.

Exploit


Number of discovered Exploits

The chart shows the trend of the number of exploit from 1998 This chart is similar to the previous one highlighting the total number of exploits

Spam E-Mail detection

Our database hosts spam e-mails coming from Untroubled. We carried many studies on this data-set in order to understand which are most commonspam types, from which part of the world they come and which are the main characteristics. From an in-depth analysis it would be possible also to attribute a spam set to a specific spammer.

Spam


Spam types

We have identified 5 different spam types: Advertisment, Phishing, Malware, Portal and Confidential. Advertisement are the ones sent just for advertising products; Phishing are the ones that try to lead the recipient to giveprivate informations impersonating a common website; Malware are the ones containing malicious attachments; Portal are the ones containing many links to webpages asking the user to register in order to gain private informations from the recipient; Confidential are the ones in which the sender tries to impersonate a person leading the recipient to send money with false promises.