The rise of ransomware: Forensic analysis for windows based ransomware attacks. (15th March 2022)
- Record Type:
- Journal Article
- Title:
- The rise of ransomware: Forensic analysis for windows based ransomware attacks. (15th March 2022)
- Main Title:
- The rise of ransomware: Forensic analysis for windows based ransomware attacks
- Authors:
- Kara, Ilker
Aydos, Murat - Abstract:
- Highlights: Compherensive review of techniques used in detection and analysis of ransomware. A novel method for ransomware detection and analysis. A real case study to show the applicability of the proposed method. A unique and appropriate dataset for ransomware analysis. Abstract: While information technologies grow and propagate worldwide, malwares have modified and risen their efficiency towards information system. Recently, the attackers have started to use ransom software (ransomware) as an effective method of cyberattack because of their profitability. Ransomware infiltrate victim systems in various ways, usually encrypt files in the system, and demand a ransom to allow user access to the encrypted files again. Although security mechanisms such as firewalls, anti-virus programs, and automated analysis programs have been developed to combat this threat, these mechanisms have little success and fail to protect the valuable assets stored in local or cloud storage resources. In this study, an effective detection and analysis method against ransomware was proposed, and the proposed method was discussed in detail with a case study. As a result of the study, potential information about the attacker were found to be accessible through characteristic behavior analysis of the onion ransomware, which was analyzed in accordance with the proposed method. This paper also presents an insight to the ransomware threat and provides a basic review of the methods and techniques used inHighlights: Compherensive review of techniques used in detection and analysis of ransomware. A novel method for ransomware detection and analysis. A real case study to show the applicability of the proposed method. A unique and appropriate dataset for ransomware analysis. Abstract: While information technologies grow and propagate worldwide, malwares have modified and risen their efficiency towards information system. Recently, the attackers have started to use ransom software (ransomware) as an effective method of cyberattack because of their profitability. Ransomware infiltrate victim systems in various ways, usually encrypt files in the system, and demand a ransom to allow user access to the encrypted files again. Although security mechanisms such as firewalls, anti-virus programs, and automated analysis programs have been developed to combat this threat, these mechanisms have little success and fail to protect the valuable assets stored in local or cloud storage resources. In this study, an effective detection and analysis method against ransomware was proposed, and the proposed method was discussed in detail with a case study. As a result of the study, potential information about the attacker were found to be accessible through characteristic behavior analysis of the onion ransomware, which was analyzed in accordance with the proposed method. This paper also presents an insight to the ransomware threat and provides a basic review of the methods and techniques used in the detection and analysis of ransomware attacks. … (more)
- Is Part Of:
- Expert systems with applications. Volume 190(2022)
- Journal:
- Expert systems with applications
- Issue:
- Volume 190(2022)
- Issue Display:
- Volume 190, Issue 2022 (2022)
- Year:
- 2022
- Volume:
- 190
- Issue:
- 2022
- Issue Sort Value:
- 2022-0190-2022-0000
- Page Start:
- Page End:
- Publication Date:
- 2022-03-15
- Subjects:
- Cybersecurity -- Digital forensic -- Malware attacks -- Ransomware detection -- Onion ransomware -- Analysis techniques
Expert systems (Computer science) -- Periodicals
Systèmes experts (Informatique) -- Périodiques
Electronic journals
006.33 - Journal URLs:
- http://www.sciencedirect.com/science/journal/09574174 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.eswa.2021.116198 ↗
- Languages:
- English
- ISSNs:
- 0957-4174
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3842.004220
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 20053.xml