Asset Details
Do you wish to reserve the book?
Detecting Malware C&C Communication Traffic Using Artificial Intelligence Techniques
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Detecting Malware C&C Communication Traffic Using Artificial Intelligence Techniques
Do you wish to request the book?
Detecting Malware C&C Communication Traffic Using Artificial Intelligence Techniques
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Detecting Malware C&C Communication Traffic Using Artificial Intelligence Techniques
2025
Overview
Banking malware poses a significant threat to users by infecting their computers and then attempting to perform malicious activities such as surreptitiously stealing confidential information from them. Banking malware variants are also continuing to evolve and have been increasing in numbers for many years. Amongst these, the banking malware Zeus and its variants are the most prevalent and widespread banking malware variants discovered. This prevalence was expedited by the fact that the Zeus source code was inadvertently released to the public in 2004, allowing malware developers to reproduce the Zeus banking malware and develop variants of this malware. Examples of these include Ramnit, Citadel, and Zeus Panda. Tools such as anti-malware programs do exist and are able to detect banking malware variants, however, they have limitations. Their reliance on regular updates to incorporate new malware signatures or patterns means that they can only identify known banking malware variants. This constraint inherently restricts their capability to detect novel, previously unseen malware variants. Adding to this challenge is the growing ingenuity of malicious actors who craft malware specifically developed to bypass signature-based anti-malware systems. This paper presents an overview of the Zeus, Zeus Panda, and Ramnit banking malware variants and discusses their communication architecture. Subsequently, a methodology is proposed for detecting banking malware C&C communication traffic, and this methodology is tested using several feature selection algorithms to determine which feature selection algorithm performs the best. These feature selection algorithms are also compared with a manual feature selection approach to determine whether a manual, automated, or hybrid feature selection approach would be more suitable for this type of problem.
Publisher
MDPI AG
Subject
