Author
Listed:
- Atef Ibrahim
(Computer Engineering Department, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia
Electrical and Computer Engineering Department, University of Victoria, Victoria, BC V8P 5C2, Canada)
- Usman Tariq
(Management Information System Department, College of Business Administration, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia)
- Tariq Ahamed Ahanger
(Management Information System Department, College of Business Administration, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia)
- Bilal Tariq
(Department of Management Sciences, COMSATS University Islamabad, Vehari Campus, Vehari 61010, Pakistan)
- Fayez Gebali
(Electrical and Computer Engineering Department, University of Victoria, Victoria, BC V8P 5C2, Canada)
Abstract
Ransomware is malicious software that encrypts data before demanding payment to unlock them. The majority of ransomware variants use nearly identical command and control (C&C) servers but with minor upgrades. There are numerous variations of ransomware, each of which can encrypt either the entire computer system or specific files. Malicious software needs to infiltrate a system before it can do any real damage. Manually inspecting all potentially malicious file types is a time-consuming and resource-intensive requirement of conventional security software. Using established metrics, this research delves into the complex issues of identifying and preventing ransomware. On the basis of real-world malware samples, we created a parameterized categorization strategy for functional classes and suggestive features. We also furnished a set of criteria that highlights the most commonly featured criteria and investigated both behavior and insights. We used a distinct operating system and specific cloud platform to facilitate remote access and collaboration on files throughout the entire operational experimental infrastructure. With the help of our proposed ransomware detection mechanism, we were able to effectively recognize and prevent both state-of-art and modified ransomware anomalies. Aggregated log revealed a consistent but satisfactory detection rate at 89%. To the best of our knowledge, no research exists that has investigated the ransomware detection and impact of ransomware for PureOS, which offers a unique platform for PC, mobile phones, and resource intensive IoT (Internet of Things) devices.
Suggested Citation
Atef Ibrahim & Usman Tariq & Tariq Ahamed Ahanger & Bilal Tariq & Fayez Gebali, 2023.
"Retaliation against Ransomware in Cloud-Enabled PureOS System,"
Mathematics, MDPI, vol. 11(1), pages 1-19, January.
Handle:
RePEc:gam:jmathe:v:11:y:2023:i:1:p:249-:d:1023842
Download full text from publisher
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2023:i:1:p:249-:d:1023842. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.