Encryption has become an indispensable technology for preserving confidentiality. Unfortunately, cybercriminals have re-purposed this technology to deny users access to their data. This trend has sparked an onslaught of ransomware attacks, that resulted in several victims being extorted to pay ransoms in return for restoring their maliciously encrypted data. In response to these challenges, we propose a novel runtime solution that seamlessly defends against cryptographic ransomware. A key observation made by this work is that maliciously encrypted data is initially buffered in the OS's page cache before it is flushed to the underlying storage device. Based on this observation, we develop a solution that efficiently manages data synchronization between the memory and storage subsystems to prevent maliciously encrypted data from being permanently committed to the underlying storage. We extensively validate the robustness of this approach against more than one thousand ransomware samples and show that our design reliably restores all encrypted files. Furthermore, our solution is resilient to ransomware that employ techniques including master boot record infection and multi-threaded attacks. Finally, an evaluation of our proof-of-concept implementation shows minimal performance impact while running a mix of compute and I/O bound applications.

The unprecedented growth in mobile systems has transformed the way we approach everyday computing. Unfortunately, the emergence of a sophisticated type of malware known as ransomware poses a great threat to consumers of this technology. Traditional research on mobile malware detection has focused on approaches that rely on analyzing bytecode for uncovering malicious apps. However, cybercriminals can bypass such methods by embedding malware directly in native machine code, making traditional methods inadequate. Another challenge that detection solutions face is scalability. The sheer number of malware variants released every year makes it difficult for solutions to efficiently scale their coverage. To address these concerns, this work presents RansomShield, an energy-efficient solution that leverages CNNs to detect ransomware. We evaluate CNN architectures that have been known to perform well on computer vision tasks and examine their suitability for ransomware detection. We show that systematically converting native instructions from Android apps into images using space-filling curve visualization techniques enable CNNs to reliably detect ransomware with high accuracy. We characterize the robustness of this approach across ARM and x86 architectures and demonstrate the effectiveness of this solution across heterogeneous platforms including smartphones and chromebooks. We evaluate the suitability of different models for mobile systems by comparing their energy demands using different platforms. In addition, we present a CNN introspection framework that determines the important features that are needed for ransomware detection. Finally, we evaluate the robustness of this solution against adversarial machine learning (AML) attacks using state-of-the-art Android malware dataset.

The popularity of cryptocurrencies has garnered interest from cybercriminals, spurring an onslaught of cryptojacking campaigns that aim to hijack computational resources for the purpose of mining cryptocurrencies. In this paper, we present a cross-stack cryptojacking defense system that spans the hardware and OS layers. Unlike prior work that is confined to detecting cryptojacking behavior within web browsers, our solution is application agnostic. We show that tracking instructions that are frequently used in cryptographic hash functions serve as reliable signatures for fingerprinting cryptojacking activity. We demonstrate that our solution is resilient to multi-threaded and throttling evasion techniques that are commonly employed by cryptojacking malware. We characterize the robustness of our solution by extensively testing a diverse set of workloads that include real consumer applications. Finally, an evaluation of our proof-of-concept implementation shows minimal performance impact while running a mix of benchmark applications.