View source for 18047891. SYSTEM AND METHOD FOR DETECTING A MALICIOUS COMMAND AND CONTROL CHANNEL USING A SIMPLE MAIL TRANSFER PROTOCOL simplified abstract (Saudi Arabian Oil Company)

=SYSTEM AND METHOD FOR DETECTING A MALICIOUS COMMAND AND CONTROL CHANNEL USING A SIMPLE MAIL TRANSFER PROTOCOL=


==Organization Name==

[[:Category:Saudi Arabian Oil Company|Saudi Arabian Oil Company]]


[[Category:Saudi Arabian Oil Company]]

==Inventor(s)==

[[:Category:Ammar Abdulateef Almulhim of Dammam (SA)|Ammar Abdulateef Almulhim of Dammam (SA)]][[Category:Ammar Abdulateef Almulhim of Dammam (SA)]]

[[:Category:Ghadah Hatem Alshehri of Dammam (SA)|Ghadah Hatem Alshehri of Dammam (SA)]][[Category:Ghadah Hatem Alshehri of Dammam (SA)]]

==SYSTEM AND METHOD FOR DETECTING A MALICIOUS COMMAND AND CONTROL CHANNEL USING A SIMPLE MAIL TRANSFER PROTOCOL - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18047891 titled 'SYSTEM AND METHOD FOR DETECTING A MALICIOUS COMMAND AND CONTROL CHANNEL USING A SIMPLE MAIL TRANSFER PROTOCOL

==Simplified Explanation==
The patent application describes a method to extract and analyze Simple Mail Transfer Protocol (SMTP) traffic from a network to detect malicious SMTP sessions using a machine learning model.

==Key Features and Innovation==
* Extraction of SMTP traffic from network data.
* Utilization of a bot detector with a machine learning model to identify malicious SMTP sessions.
* Provision of alerts upon detection of malicious SMTP sessions.

==Potential Applications==
This technology can be applied in cybersecurity systems to enhance email security by detecting and preventing malicious SMTP sessions.

==Problems Solved==
* Detection of malicious SMTP sessions in network traffic.
* Improving email security by identifying potential threats.

==Benefits==
* Enhanced email security.
* Early detection of malicious activities.
* Prevention of cyber threats through proactive measures.

==Commercial Applications==
===Email Security Enhancement Technology for Cybersecurity Systems===
This technology can be utilized by cybersecurity companies to offer advanced email security solutions to businesses and organizations, ensuring protection against cyber threats targeting email communications.

==Prior Art==
Information on prior art related to this technology is not provided in the abstract.

==Frequently Updated Research==
There is no information on frequently updated research relevant to this technology.

==Questions about SMTP Traffic Analysis==
===Question 1===
How does the machine learning model differentiate between normal and malicious SMTP sessions?

The machine learning model is trained on a dataset of known malicious SMTP sessions to learn patterns and behaviors associated with malicious activities, allowing it to distinguish between normal and malicious SMTP traffic.

===Question 2===
What are the potential implications of false positives in detecting malicious SMTP sessions?

False positives in detecting malicious SMTP sessions could lead to unnecessary alerts and potential disruptions in email communications. It is essential to fine-tune the machine learning model to minimize false positives and ensure accurate detection of threats.


==Original Abstract Submitted==

In an example, simple mail traffic protocol (SMTP) traffic can be extracted from network traffic of a network. The SMTP traffic can be processed using a bot detector employing a machine learning model trained to determine whether the SMTP traffic contains a malicious SMTP session. Alert data can be provided in response to detecting the malicious SMTP session.



[[Category:H04L9/40]]
[[Category:CPC_H04L63/1416]]