Enterprise Vulnerabilities
From DHS/US-CERT's National Vulnerability Database
CVE-2021-21866PUBLISHED: 2021-08-02
A unsafe deserialization vulnerability exists in the ObjectManager.plugin ProfileInformation.ProfileData functionality of CODESYS GmbH CODESYS Development System 3.5.16 and 3.5.17. A specially crafted file can lead to arbitrary command execution. An attacker can provide a malicious file to trigger t...
CVE-2021-27499PUBLISHED: 2021-08-02
Ypsomed mylife Cloud, mylife Mobile Application, Ypsomed mylife Cloud: All versions prior to 1.7.2, Ypsomed mylife App: All versions prior to 1.7.5,The application layer encryption of the communication protocol between the Ypsomed mylife App and mylife Cloud uses non-random IVs, which allows man-in-...
CVE-2021-27503PUBLISHED: 2021-08-02
Ypsomed mylife Cloud, mylife Mobile Application, Ypsomed mylife Cloud: All versions prior to 1.7.2, Ypsomed mylife App: All versions prior to 1.7.5,The application encrypts on the application layer of the communication protocol between the Ypsomed mylife App and mylife Cloud credentials based on har...
CVE-2021-27943PUBLISHED: 2021-08-02
The pairing procedure used by the Vizio P65-F1 6.0.31.4-2 and E50x-E1 10.0.31.4-2 Smart TVs and mobile application is vulnerable to a brute-force attack (against only 10000 possibilities), allowing a threat actor to forcefully pair the device, leading to remote control of the TV settings and configu...
CVE-2021-29979PUBLISHED: 2021-08-02Hubs Cloud allows users to download shared content, specifically HTML and JS, which could allow javascript execution in the Hub Cloud instance’s primary hosting domain.*. This vulnerability affects Hubs Cloud < mozillareality/reticulum/1.0.1/20210618012634.
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User Rank: Apprentice
11/19/2015 | 6:42:56 PM
As the author notes:
"Any ML system must attempt to separate and differentiate activity based either on pre-defined (i.e. trained learning) or self-learned classifications"
Thus, for a cybersecurity machine learning system to be effective, it must have some principled and structured method to differentiate appropriate and inappropriate access. And, moreover, the system must have the correct context to make such a decision.
The author makes the statement that ML systems struggle to do this:
"Unfortunately, ML systems are not good at describing why a particular activity is anomalous, and how it is related to others. So when the ML system delivers an alert, you still have to do the hard work of understanding whether it is a false positive or not, before trying to understand how the anomaly is related to other activity in the system."
I would point the author to a new line of machine learning algorithms for access auditing called Explanation-Based Auditing.
A detailed peer-reviewed publication can be found at vldb.org/pvldb/vol5/p001_danielfabbri_vldb2012.pdf.
The general idea is to learn why accesses to data occur (e.g., the doctor accessed a record because of an appointment with the patient). This can be modeled as a graph search between the person accessing the data and the data accessed. When such an "explanation" is found, the system can determine the reason for access, filtering it away from manual review.
Thus, as the previous comment states, such as system can remove a tremendous amount of false positives, allowing the privacy or security officer to focus on the unexplained and suspicious.