Ransomware Detection and Prevention Using Honeypot
Abstract
The significance of network security explores the details of ransomware attacks, highlighting the crucial parameters essential to fortifying defences defenses against this pernicious cyber threat. Network security involves safeguarding computer networks against unauthorized access, data breaches, and cyberattacks. Ransomware attack, a specific type of cyberattack, entails malicious software encrypting a computer system, making them unavailable to use; in return, the attacker asks for ransom in the form of cryptocurrency like Bitcoin or Ethereum. These attacks can result in significant data loss and financial harm to individuals and organizations alike. In response to this vulnerability and to prevent data loss caused by such attacks, This this specialized tool is meticulously designed to swiftly identify and mitigate ransomware threats in real-time. It conducts thorough ransomware analysis by examining ransom notes, file extensions, and ransomware-specific codes, allowing for accurate identification of ransomware variants and facilitating data recovery. Furthermore, the tool aids in classifying various ransomware families. With the help of Honeypot, this tool will lure attackers away from real systems. This tool aligns with network security principles, including Confidentiality, Integrity, and Availability.
Keyword: Ransomware, cybersecurity, honeypot, network security, cryptocurrency
Refrences:
- Il Bae S, Bin Lee G, Im EG. Ransomware detection using machine learning algorithms. Concurr
Comput: Practice and Experience (CCPE). 2019 Jun; 32(18): e5422. doi: 10.1002/cpe.5422. - Mohurle S, Patil M. A Brief Study of WannaCry Threat: Ransomware Attack 2017. Int J Adv Res
Comput Sci (IJARCS). 2017; 8(5): 1938–1940. - Spitzner L. Honeypots: Tracking Hackers. Boston, MA, USA: Addison Wesley; 2002.
- Abu Elkhail, Nada Lachtar, Duha Lbdh, et al. Seamlessly safeguarding data against Ransomware
attacks. IEEE Trans Dependable Secure Comput. 2023 Jan; 20(1): 1–16. - Mairh A, Barik D, Verma K, Jena D. Honeypot in network security: a survey. Proceedings of the
2011 International Conference on Communication, Computing & Security (ICCCS ’11); 2011 Feb 12-14; Rourkela, Odisha, India. New York (NY): Association for Computing Machinery; 2011. p.
600-605. DOI: 10.1145/1947940.1948065. - Shubham Sharma, Satwinder Singh. Ransomware Threat, Attack, Prevention and Cure on
Window Platform. Int J Innov Technol Explor Eng. 2020 Feb; 9(4): 2721–2732. - Chamupathi Gigara Hettige, Torin Wirasingha. A Review on Ransomware Detection Systems.
TechRxiv. 2023 Jun. - Herrera-Silva Juan A, Myriam Hernandez Alvarez. Dynamic Feature Dataset for Ransomware
Detection Using Machine Learning Algorithms. Sensors. 2023 Jan; 23(3): 1053 - Ebu Yusuf Guven, Ali Aydin M. Analysis of Malicious Files Gathering via Honeypot Trap
System and Benchmark of Anti-Virus Software. Research Square. 2023 Dec. DOI:
10.21203/rs.3.rs-3733937/v1. - Sibi Chakkaravarthy S, Sangeetha D, Meenalosini Vimal Cruz, Vaidehi V, Balasubramanian
Raman. Design of Intrusion Detection Honeypot Using Social Leopard Algorithm to Detect IoT
Ransomware Attacks. IEEE Access. 2020 Sep 23; 8: 169944–169956. - Chakkaravarthy S, Sangeetha D, Cruz MV, Vaidehi V, Raman B. Design of intrusion detection
honeypot using social leopard algorithm to detect IoT Ransomware attacks. IEEE Access.
2020;8:169944-169956. DOI: 10.1109/ACCESS.2020.3023764. - Mathur Aditya P, Nwokedi Idika. Survey of malware detection techniques. Department of
Computer Science. West Lafayette: Purdue University; 2007 Feb. - Sakhawat Hossain GM, Kaushik Deb, Helge Janicke, Sarker Iqbal H. PDF Malware Detection:
Towards Machine Learning Modeling with Explainability Analysis. IEEE Access. 2024 Jan; 12:
13833–13859.