Abstract Ransomware constitutes a significant threat to the Android operating system. It can either lock or encrypt the target devices, and victims are forced to pay ransoms to restore their data. Hence, the prompt detection of such attacks has a priority in comparison to other malicious threats. Previous works on Android malware detection mainly focused on Machine Learning-oriented approaches that were tailored to identifying malware families, without a clear focus on ransomware. More specifically, such approaches resorted to complex information types such as permissions, user-implemented API calls, and native calls. However, this led to significant drawbacks concerning complexity, resilience against obfuscation, and explainability. To overcome these issues, in this paper, we propose and discuss learning-based detection strategies that rely on System API information. These techniques leverage the fact that ransomware attacks heavily resort to System API to perform their actions, and allow distinguishing between generic malware, ransomware and goodware. We tested three different ways of employing System API information, i.e., through packages, classes, and methods, and we compared their performances to other, more complex state-of-the-art approaches. The attained results showed that systems based on System API could detect ransomware and generic malware with very good accuracy, comparable to systems that employed more complex information. Moreover, the proposed systems could accurately detect novel samples in the wild and showed resilience against static obfuscation attempts. Finally, to guarantee early on-device detection, we developed and released on the Android platform a complete ransomware and malware detector (R-PackDroid) that employed one of the methodologies proposed in this paper.
proposed a method that aims to discriminate between ransomware and goodware using process monitoring @cite_6 . In particular, they considered system-related features representing the I O rate, as well as the CPU and memory usage. The system has been evaluated with only one ransomware sample developed by the authors, and no implementation is publicly available.
@cite_6
2367504390 2124900567 2461373307 2296579688
Due to recent indiscriminate attacks of ransomware, damage cases including encryption of users’ important files are constantly increasing. The existing vaccine systems are vulnerable to attacks of new pattern ransomware because they can only detect the ransomware of existing patterns. More effective technique is required to prevent modified ransomware. In this paper, an effective method is proposed to prevent the attacks of modified ransomware on Android platform. The proposed technique specifies and intensively monitors processes and specific file directories using statistical methods based on Processor usage, Memory usage, and I O rates so that the process with abnormal behaviors can be detected. If the process running a suspicious ransomware is detected, the proposed system will stop the process and take steps to confirm the deletion of programs associated with the process from users. The information of suspected and exceptional processes confirmed by users is stored in a database. The proposed technique can detect ransomware even if you do not save its patterns. Its speed of detection is very fast because it can be implemented in Android source code instead of mobile application. In addition, it can effectively determine modified patterns of ransomware and provide protection with minimum damage. A novel approach to malware detection by recognizing known inter-process and intra-process malicious functionalities in software behavior is proposed. It encompasses two essential tasks: the specification of a functionality that may involve a joint activity of several apparently independent processes, and efficient recognition of the specified functionality in the process behavior. The robustness of the proposed technology is achieved by the generalization of the specification domain that is separated from the detection domain. The functionalities of interest are defined in the abstract system domain through activity diagrams, thus resulting in formal specifications that are rather generic and less prone to false negatives. To facilitate the detection, we developed a procedure that automatically generates a Colored Petri Net recognizing the specified functionality in the system call domain. The separation of specification and recognition domains results in signature expressiveness and recognition efficiency. The approach is illustrated by the analysis, specification and consequent recognition of several common malicious functionalities including self-replication engines and popular payloads. A prototype IDS implementing the proposed approach has been developed and successfully tested on a set of real malware. Although the concept of ransomware is not new (i.e., such attacks date back at least as far as the 1980s), this type of malware has recently experienced a resurgence in popularity. In fact, in the last few years, a number of high-profile ransomware attacks were reported, such as the large-scale attack against Sony that prompted the company to delay the release of the film "The Interview." Ransomware typically operates by locking the desktop of the victim to render the system inaccessible to the user, or by encrypting, overwriting, or deleting the user's files. However, while many generic malware detection systems have been proposed, none of these systems have attempted to specifically address the ransomware detection problem. In this paper, we present a novel dynamic analysis system called UNVEIL that is specifically designed to detect ransomware. The key insight of the analysis is that in order to mount a successful attack, ransomware must tamper with a user's files or desktop. UNVEIL automatically generates an artificial user environment, and detects when ransomware interacts with user data. In parallel, the approach tracks changes to the system's desktop that indicate ransomware-like behavior. Our evaluation shows that UNVEIL significantly improves the state of the art, and is able to identify previously unknown evasive ransomware that was not detected by the antimalware industry. In ransomware attacks, the actual target is the human, as opposed to the classic attacks that abuse the infected devices e.g., botnet renting, information stealing. Mobile devices are by no means immune to ransomware attacks. However, there is little research work on this matter and only traditional protections are available. Even state-of-the-art mobile malware detection approaches are ineffective against ransomware apps because of the subtle attack scheme. As a consequence, the ample attack surface formed by the billion mobile devices is left unprotected. First, in this work we summarize the results of our analysis of the existing mobile ransomware families, describing their common characteristics. Second, we present HelDroid, a fast, efficient and fully automated approach that recognizes known and unknown scareware and ransomware samples from goodware. Our approach is based on detecting the "building blocks" that are typically needed to implement a mobile ransomware application. Specifically, HelDroid detects, in a generic way, if an app is attempting to lock or encrypt the device without the user's consent, and if ransom requests are displayed on the screen. Our technique works without requiring that a sample of a certain family is available beforehand. We implemented HelDroid and tested it on real-world Android ransomware samples. On a large dataset comprising hundreds of thousands of APKs including goodware, malware, scareware, and ransomware, HelDroid exhibited nearly zero false positives and the capability of recognizing unknown ransomware samples.