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

DECEPTION BASED TECHNIQUES AGAINST RANSOMWARES: A SYSTEMATIC REVIEW

Ransomware is the most prevalent emerging business risk nowadays. It seriously affects business continuity and operations. According to Deloitte Cyber Security Landscape 2022, up to 4000 ransomware attacks occur daily, while the average number of days an organization takes to identify a breach is 191. Sophisticated cyber-attacks such as ransomware typically must go through multiple consecutive phases (initial foothold, network propagation, and action on objectives) before accomplishing its final objective. This study analyzed decoy-based solutions as an approach (detection, prevention, or mitigation) to overcome ransomware. A systematic literature review was conducted, in which the result has shown that deception-based techniques have given effective and significant performance against ransomware with minimal resources. It is also identified that contrary to general belief, deception techniques mainly involved in passive approaches (i.e., prevention, detection) possess other active capabilities such as ransomware traceback and obstruction (thwarting), file decryption, and decryption key recovery. Based on the literature review, several evaluation methods are also analyzed to measure the effectiveness of these deception-based techniques during the implementation process

Ransomware: Current Trend, Challenges, and Research Directions

Ransomware attacks have become a global incidence, with the primary aim of making monetary gains through illicit means. The attack started through e-mails and has expanded through spamming and phishing. Ransomware encrypts targets’ files and display notifications, requesting for payment before the data can be unlocked. Ransom demand is usually in form of virtual currency, bitcoin, because it is difficult to track. In this paper, we give a brief overview of the current trend, challenges, and research progress in the bid to finding lasting solutions to the menace of ransomware that currently challenge computer and network security, and data privacy

GUIDE FOR THE COLLECTION OF INSTRUSION DATA FOR MALWARE ANALYSIS AND DETECTION IN THE BUILD AND DEPLOYMENT PHASE

During the COVID-19 pandemic, when most businesses were not equipped for remote work and cloud computing, we saw a significant surge in ransomware attacks. This study aims to utilize machine learning and artificial intelligence to prevent known and unknown malware threats from being exploited by threat actors when developers build and deploy applications to the cloud. This study demonstrated an experimental quantitative research design using Aqua. The experiment\u27s sample is a Docker image. Aqua checked the Docker image for malware, sensitive data, Critical/High vulnerabilities, misconfiguration, and OSS license. The data collection approach is experimental. Our analysis of the experiment demonstrated how unapproved images were prevented from running anywhere in our environment based on known vulnerabilities, embedded secrets, OSS licensing, dynamic threat analysis, and secure image configuration. In addition to the experiment, the forensic data collected in the build and deployment phase are exploitable vulnerability, Critical/High Vulnerability Score, Misconfiguration, Sensitive Data, and Root User (Super User). Since Aqua generates a detailed audit record for every event during risk assessment and runtime, we viewed two events on the Audit page for our experiment. One of the events caused an alert due to two failed controls (Vulnerability Score, Super User), and the other was a successful event meaning that the image is secure to deploy in the production environment. The primary finding for our study is the forensic data associated with the two events on the Audit page in Aqua. In addition, Aqua validated our security controls and runtime policies based on the forensic data with both events on the Audit page. Finally, the study’s conclusions will mitigate the likelihood that organizations will fall victim to ransomware by mitigating and preventing the total damage caused by a malware attack

Detecting crypto-ransomware in IoT networks based on energy consumption footprint

An Internet of Things (IoT) architecture generally consists of a wide range of Internet-connected devices or things such as Android devices, and devices that have more computational capabilities (e.g., storage capacities) are likely to be targeted by ransomware authors. In this paper, we present a machine learning based approach to detect ransomware attacks by monitoring power consumption of Android devices. Specifically, our proposed method monitors the energy consumption patterns of different processes to classify ransomware from non-malicious applications. We then demonstrate that our proposed approach out-performs K-Nearest Neighbors, Neural Networks, Support Vector Machine and Random Forest, in terms of accuracy rate, recall rate, precision rate and F-measure