Ransomware: Emergence of the cyber-extortion menace

Ransomware is increasingly posing a threat to the security of information resources. Millions of dollars of monetary loss have been afflicted on end-users and corporations alike through unlawful deployment of ransomware. Through malware injection into end-user devices and subsequent extortion of their system or data, ransomware has emerged as a threat requiring immediate attention and containment by the cyber-security community. We conduct a detailed analysis of the steps of execution involved in ransomware deployment to facilitate readiness of the cyber-security community in containing the rapid proliferation of ransomware. This paper examines the evolution of malware over a period of 26 years and the emergence of ransomware in the cyber-threat landscape. Key findings on the evolution of ransomware and its use of emerging technologies are presented

Ransomware 2.0: An emerging threat to national security

The global Covid-19 pandemic has seen the rapid evolution of our traditional working environment; more people are working from home and the number of online meetings has increased. This trend has also affected the security sector. Consequently, the evolution of ransomware to what is now being described as ‘Ransomware 2.0’ has governments, businesses and individuals alike rushing to secure their data

Hypervisor-Level Ransomware Detection in Cloud Using Machine Learning

Ransomware attack incidences have been on the rise for a few years. The attacks have evolved over the years. The severity of these attacks has only increased in the cloud era. This article discusses the evolution of ransomware attacks targeting cloud storage and explores existing ransomware detection solutions. It also presents a methodology for generating a dataset for detecting ransomware in the cloud and discusses the results, including feature selection and normalization. The article proposes a system for detecting attacks in virtualized environments using machine learning models and evaluates the performance of different classification models. The proposed system is shown to have high accuracy of 96.6% in detecting ransomware attacks in virtualized environments at the hypervisor level

The Evolution of Embedding Metadata in Blockchain Transactions

The use of blockchains is growing every day, and their utility has greatly expanded from sending and receiving crypto-coins to smart-contracts and decentralized autonomous organizations. Modern blockchains underpin a variety of applications: from designing a global identity to improving satellite connectivity. In our research we look at the ability of blockchains to store metadata in an increasing volume of transactions and with evolving focus of utilization. We further show that basic approaches to improving blockchain privacy also rely on embedding metadata. This paper identifies and classifies real-life blockchain transactions embedding metadata of a number of major protocols running essentially over the bitcoin blockchain. The empirical analysis here presents the evolution of metadata utilization in the recent years, and the discussion suggests steps towards preventing criminal use. Metadata are relevant to any blockchain, and our analysis considers primarily bitcoin as a case study. The paper concludes that simultaneously with both expanding legitimate utilization of embedded metadata and expanding blockchain functionality, the applied research on improving anonymity and security must also attempt to protect against blockchain abuse.Comment: 9 pages, 6 figures, 1 table, 2018 International Joint Conference on Neural Network

Ransomware and reputation

open access articleRansomware is a particular form of cyber-attack in which a victim loses access to either his electronic device or files unless he pays a ransom to criminals. A criminal’s ability to make money from ransomware critically depends on victims believing that the criminal will honour ransom payments. In this paper we explore the extent to which a criminal can build trust through reputation. We demonstrate that there are situations in which it is optimal for the criminal to always return the files and situations in which it is not. We argue that the ability to build reputation will depend on how victims distinguish between different ransomware strands. If ransomware is to survive as a long term revenue source for criminals then they need to find ways of building a good reputation

Ransomware in High-Risk Environments

In today’s modern world, cybercrime is skyrocketing globally, which impacts a variety of organizations and endpoint users. Hackers are using a multitude of approaches and tools, including ransomware threats, to take over targeted systems. These acts of cybercrime lead to huge damages in areas of business, healthcare systems, industry sectors, and other fields. Ransomware is considered as a high risk threat, which is designed to hijack the data. This paper is demonstrating the ransomware types, and how they are evolved from the malware and trojan codes, which is used to attack previous incidents, and explains the most common encryption algorithms such as AES, and RSA, ransomware uses them during infection process in order to produce complex threats. The practical approach for data encryption uses python programming language to show the efficiency of those algorithms in real attacks by executing this section on Ubuntu virtual machine. Furthermore, this paper analyzes programming languages, which is used to build ransomware. An example of ransomware code is being demonstrated in this paper, which is written specifically in C sharp language, and it has been tested out on windows operating system using MS visual studio. So, it is very important to recognize the system vulnerability, which can be very useful to prevent the ransomware. In contrast, this threat might sneak into the system easily, allowing for a ransom to be demanded. Therefore, understanding ransomware anatomy can help us to find a better solution in different situations. Consequently, this paper shows a number of outstanding removal techniques to get rid from ransomware attacks in the system

Ransomware in High-Risk Environments

In today’s modern world, cybercrime is skyrocketing globally, which impacts a variety of organizations and endpoint users. Hackers are using a multitude of approaches and tools, including ransomware threats, to take over targeted systems. These acts of cybercrime lead to huge damages in areas of business, healthcare systems, industry sectors, and other fields. Ransomware is considered as a high risk threat, which is designed to hijack the data. This paper is demonstrating the ransomware types, and how they are evolved from the malware and trojan codes, which is used to attack previous incidents, and explains the most common encryption algorithms such as AES, and RSA, ransomware uses them during infection process in order to produce complex threats. The practical approach for data encryption uses python programming language to show the efficiency of those algorithms in real attacks by executing this section on Ubuntu virtual machine. Furthermore, this paper analyzes programming languages, which is used to build ransomware. An example of ransomware code is being demonstrated in this paper, which is written specifically in C sharp language, and it has been tested out on windows operating system using MS visual studio. So, it is very important to recognize the system vulnerability, which can be very useful to prevent the ransomware. In contrast, this threat might sneak into the system easily, allowing for a ransom to be demanded. Therefore, understanding ransomware anatomy can help us to find a better solution in different situations. Consequently, this paper shows a number of outstanding removal techniques to get rid from ransomware attacks in the system

Malware detection techniques for mobile devices

Mobile devices have become very popular nowadays, due to its portability and high performance, a mobile device became a must device for persons using information and communication technologies. In addition to hardware rapid evolution, mobile applications are also increasing in their complexity and performance to cover most needs of their users. Both software and hardware design focused on increasing performance and the working hours of a mobile device. Different mobile operating systems are being used today with different platforms and different market shares. Like all information systems, mobile systems are prone to malware attacks. Due to the personality feature of mobile devices, malware detection is very important and is a must tool in each device to protect private data and mitigate attacks. In this paper, analysis of different malware detection techniques used for mobile operating systems is provides. The focus of the analysis will be on the to two competing mobile operating systems - Android and iOS. Finally, an assessment of each technique and a summary of its advantages and disadvantages is provided. The aim of the work is to establish a basis for developing a mobile malware detection tool based on user profiling.Comment: 11 pages, 6 figure