Blockchain-Enabled Security Framework against Ransomware Attacks Using Machine Learning
Vinod S. Wadne1 , vinods1111@gmail.com
Professor, Dept. Of IT, JSPM BSIOTR, Pune, India
Bachelor students, Prince Yadav2, Harsh Patel3 , Meraj Shaikh4, Arpit Dahat5
Dept. Of IT, JSPM BSIOTR, Pune, India
Abstract— In this paper, we first describe and analyze the background of a certain discipline by reviewing the recent scientific literature on this subject and summarizing it about issues of the new research. Ransomware through reasoning can also be considered the last significant stage for cyber extortion blocking access to the resources of the target organization until submission by the latter to certain coercion or payment. There is a separate class of malware known as ransomware. When a computer or some other device suffers a ransomware malware attack, such device is either locked/held hostage or the data within the device is encrypted. Ransom demands (usually a small sum) are placed on the victims for providing the data order for the data translation in the form of a decryption key. Due to these attacks, surveillance means and protective programs are recommended given the prevention of ransomware epidemic outbreaks. The most vulnerable targets are probably those classed as organizations, such as financial institutes and healthcare sectors. Blockchain technology prevents tempering which makes it much more effective than the traditional centralized approach of data storage. Such aspects of blockchain technology can enhance the security perimeter for the detection and prevention of ransomware attacks even more. The objectives of the research are to demonstrate the extent of the problem and to show how problems are identifiable within datasets, which is through the application of machine learning. In this paper, we propose a new security framework that applies machine learning to prevent ransomware attacks and is based on the principles of blockchain technology.
Keywords : Blockchain technology, Ransomware attacks, Cybersecurity, Machine learning, Data integrity, Network traffic analysis.
