Related papers: RAPPER: Ransomware Prevention via Performance Coun…

Randomized Aggregatable Privacy-Preserving Ordinal Response, or RAPPOR, is a technology for crowdsourcing statistics from end-user client software, anonymously, with strong privacy guarantees. In short, RAPPORs allow the forest of client…

This study introduces ROFBS$\alpha$, a new defense architecture that addresses delays in detection in ransomware detectors based on machine learning. It builds on our earlier Real Time Open File Backup System, ROFBS, by adopting an…

This study provides a comprehensive synthesis of Artificial Intelligence (AI), especially Machine Learning (ML) and Deep Learning (DL), in ransomware defense. Using a "review of reviews" methodology based on PRISMA, this paper gathers…

Providing security for information is highly critical in the current era with devices enabled with smart technology, where assuming a day without the internet is highly impossible. Fast internet at a cheaper price, not only made…

Ransomware has grown to become one of the most damaging types of cybercrime, affecting private and public organizations in any sector. While early types of ransomware targeted many victims via automated attacks, ransomware groups have…

With the rapid technological advancement, security has become a major issue due to the increase in malware activity that poses a serious threat to the security and safety of both computer systems and stakeholders. To maintain stakeholders,…

Recommender systems are an integral part of online platforms that recommend new content to users with similar interests. However, they demand a considerable amount of user activity data where, if the data is not adequately protected,…

Adversarial attacks on Neural Network weights, such as the progressive bit-flip attack (PBFA), can cause a catastrophic degradation in accuracy by flipping a very small number of bits. Furthermore, PBFA can be conducted at run time on the…

It was found when reviewing the ransomware detection research literature that almost no proposal provided enough detail on how the test data set was created, or sufficient description of its actual content, to allow it to be recreated by…

Computers are widely used today by most people. Internet based applications, like ecommerce or ebanking attracts criminals, who using sophisticated techniques, tries to introduce malware on the victim computer. But not only computer users…

Network analysis and machine learning techniques have been widely applied for building malware detection systems. Though these systems attain impressive results, they often are $(i)$ not extensible, being monolithic, well tuned for the…

Protecting state-of-the-art AI-based cybersecurity defense systems from cyber attacks is crucial. Attackers create adversarial examples by adding small changes (i.e., perturbations) to the attack features to evade or fool the deep learning…

We present in this work an economic analysis of ransomware, with relevant data from Cryptolocker, CryptoWall, TeslaCrypt and other major strands. We include a detailed study of the impact that different price discrimination strategies can…

Fileless malware and cryptojacking attacks have appeared independently as the new alarming threats in 2017. After 2020, fileless attacks have been devastating for victim organizations with low-observable characteristics. Also, the amount of…

Memory was captured from a system infected by ransomware and its contents was examined using live forensic tools, with the intent of identifying the symmetric encryption keys being used. NotPetya, Bad Rabbit and Phobos hybrid ransomware…

This study focuses on the creation and implementation of ransomware for educational purposes that leverages Python's native cryptographic APIs in a controlled environment. Additionally, an Android version of the framework is implemented…

Research shows that over the last decade, malware has been growing exponentially, causing substantial financial losses to various organizations. Different anti-malware companies have been proposing solutions to defend attacks from these…

Money laundering is a global phenomenon with wide-reaching social and economic consequences. Cryptocurrencies are particularly susceptible due to the lack of control by authorities and their anonymity. Thus, it is important to develop new…

In response to the increasing ransomware threat, this study presents a novel detection system that integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. By leveraging Sysmon logs, the system enables…

Detecting malware, especially ransomware, is essential to securing today's interconnected ecosystems, including cloud storage, enterprise file-sharing, and database services. Training high-performing artificial intelligence (AI) detectors…