NoCry: No More Secure Encryption Keys for Cryptographic Ransomware

Since the appearance of ransomware in the cyber crime scene, researchers and anti-malware companies have been offering solutions to mitigate the threat. Anti-malware solutions differ on the specific strategy they implement, and all have pros and cons. However, three requirements concern them all: their implementation must be secure, be effective, and be efficient. Recently, Genç et al. proposed to stop a specific class of ransomware, the cryptographically strong one, by blocking unauthorized calls to cryptographically secure pseudo-random number generators, which are required to build strong encryption keys. Here, in adherence to the requirements, we discuss an implementation of that solution that is more secure (with components that are not vulnerable to known attacks), more effective (with less false negatives in the class of ransomware addressed) and more efficient (with minimal false positive rate and negligible overhead) than the original, bringing its security and technological readiness to a higher level

Why Current Statistical Approaches to Ransomware Detection Fail

The frequent use of basic statistical techniques to detect ransomware is a popular and intuitive strategy; statistical tests can be used to identify randomness, which in turn can indicate the presence of encryption and, by extension, a ransomware attack. However, common file formats such as images and compressed data can look random from the perspective of some of these tests. In this work, we investigate the current frequent use of statistical tests in the context of ransomware detection, primarily focusing on false positive rates. The main aim of our work is to show that the current over-dependence on simple statistical tests within anti-ransomware tools can cause serious issues with the reliability and consistency of ransomware detection in the form of frequent false classifications. We determined thresholds for five key statistics frequently used in detecting randomness, namely Shannon entropy, chi-square, arithmetic mean, Monte Carlo estimation for Pi and serial correlation coefficient. We obtained a large data set of 84,327 files comprising of images, compressed data and encrypted data. We then tested these thresholds (taken from a variety of previous publications in the literature where possible) against our dataset, showing that the rate of false positives is far beyond what could be considered acceptable. False positive rates were often above 50% and even above 90% on several occasions. False negative rates were also generally between 5% and 20%, numbers which are also far too high. As a direct result of these experiments, we determine that relying on these simple statistical approaches is not good enough to detect ransomware attacks consistently. We instead recommend the exploration of higher-order statistics such as skewness and kurtosis for future ransomware detection techniques

On Deception-Based Protection Against Cryptographic Ransomware

In order to detect malicious file system activity, some commercial and academic anti-ransomware solutions implement deception-based techniques, specifically by placing decoy files among user files. While this approach raises the bar against current ransomware, as any access to a decoy file is a sign of malicious activity, the robustness of decoy strategies has not been formally analyzed and fully tested. In this paper, we analyze existing decoy strategies and discuss how they are effective in countering current ransomware by defining a set of metrics to measure their robustness. To demonstrate how ransomware can identify existing deception-based detection strategies, we have implemented a proof-of-concept anti-decoy ransomware that successfully bypasses decoys by using a decision engine with few rules. Finally, we discuss existing issues in decoy-based strategies and propose practical solutions to mitigate them

A Roadmap for Improving the Impact of Anti-Ransomware Research

Ransomware is a type of malware which restricts access to a victim’s computing resources and demands a ransom in order to restore access. This is a continually growing and costly threat across the globe, therefore efforts have been made both in academia and industry to develop techniques that can help to detect and recover from ransomware attacks. This paper aims to provide an overview of the current landscape of Windows-based anti-ransomware tools and techniques, using a clear, simple and consistent terminology in terms of Data Sources, Processing and Actions. We extensively analysed relevant literature so that, to the best of our knowledge, we had at the time covered all approaches taken to detect and recover from ransomware attacks. We grouped these techniques according to their main features as a way to understand the landscape. We then selected 15 existing anti-ransomware tools both to examine how they fit into this landscape and to compare them by aggregating their accuracy and overhead – two of the most important selection criteria of these tools – as reported by the tools’ respective authors. We were able to determine popular solutions and unexplored gaps that could lead to promising areas of anti-ransomware development. From there, we propose two novel detection techniques, namely serial byte correlation and edit distance. This paper serves as a much needed roadmap of knowledge and ideas to systematise the current landscape of anti-ransomware tools

Next Generation Cryptographic Ransomware

We are assisting at an evolution in the ecosystem of cryptoware - the malware that encrypts files and makes them unavailable unless the victim pays up. New variants are taking the place once dominated by older versions; incident reports suggest that forthcoming ransomware will be more sophisticated, disruptive, and targeted. Can we anticipate how such future generations of ransomware will work in order to start planning on how to stop them? We argue that among them there will be some which will try to defeat current anti-ransomware; thus, we can speculate over their working principle by studying the weak points in the strategies that seven of the most advanced anti-ransomware are currently implementing. We support our speculations with experiments, proving at the same time that those weak points are in fact vulnerabilities and that the future ransomware that we have imagined can be effective

Ransomware Network Traffic Analysis for Pre-Encryption Alert

International audienceCyber Security researchers are in an ongoing battle against ransomware attacks. Some exploits begin with social engineering methods to install payloads on victims' computers , followed by a communication with command and control servers for data exchange. To scale down these attacks, scientists should shed light on the danger of those rising intrusions to prevent permanent data loss. To join this arm race against malware, we propose in this paper an analysis of various ransomware families based on the collected system and network logs from a computer. We delve into malicious network traffic generated by these samples to perform a packet level detection. Our goal is to reconstruct ransomware's full activity to check if its network communication is distinguishable from benign traffic. Then, we examine if the first packet sent occurs before data's encryption to alert the administrators or afterwards. We aim to define the first occurrence of the alert raised by malicious network traffic and where it takes place in a ransomware workflow. Logs collected are available at http://serveur2.seres.rennes.telecom-bretagne.eu/data/RansomwareData/