Timed Automata for Mobile Ransomware Detection

Considering the plethora of private and sensitive information stored in smartphone and tablets, it is easy to understand the reason why attackers develop everyday more and more aggressive malicious payloads with the aim to exfiltrate our data. One of the last trend in mobile malware landascape is represented by the so-called ransomware, a threat capable to lock the user interface and to cipher the data of the mobile device under attack. In this paper we propose an approach to model an Android application in terms of timed automaton by considering system call traces i.e., performing a dynamic analysis. We obtain encouraging results in the experimental analysis we performed exploiting real-world  (ransomware and legitimate) Android applications

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

The Paradox of Choice: Investigating Selection Strategies for Android Malware Datasets Using a Machine-learning Approach

The increase in the number of mobile devices that use the Android operating system has attracted the attention of cybercriminals who want to disrupt or gain unauthorized access to them through malware infections. To prevent such malware, cybersecurity experts and researchers require datasets of malware samples that most available antivirus software programs cannot detect. However, researchers have infrequently discussed how to identify evolving Android malware characteristics from different sources. In this paper, we analyze a wide variety of Android malware datasets to determine more discriminative features such as permissions and intents. We then apply machine-learning techniques on collected samples of different datasets based on the acquired features’ similarity. We perform random sampling on each cluster of collected datasets to check the antivirus software’s capability to detect the sample. We also discuss some common pitfalls in selecting datasets. Our findings benefit firms by acting as an exhaustive source of information about leading Android malware datasets

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

Android Application Security Scanning Process

This chapter presents the security scanning process for Android applications. The aim is to guide researchers and developers to the core phases/steps required to analyze Android applications, check their trustworthiness, and protect Android users and their devices from being victims to different malware attacks. The scanning process is comprehensive, explaining the main phases and how they are conducted including (a) the download of the apps themselves; (b) Android application package (APK) reverse engineering; (c) app feature extraction, considering both static and dynamic analysis; (d) dataset creation and/or utilization; and (e) data analysis and data mining that result in producing detection systems, classification systems, and ranking systems. Furthermore, this chapter highlights the app features, evaluation metrics, mechanisms and tools, and datasets that are frequently used during the app’s security scanning process

Android Malware Detection System using Genetic Programming

Nowadays, smartphones and other mobile devices are playing a significant role in the way people engage in entertainment, communicate, network, work, and bank and shop online. As the number of mobile phones sold has increased dramatically worldwide, so have the security risks faced by the users, to a degree most do not realise. One of the risks is the threat from mobile malware. In this research, we investigate how supervised learning with evolutionary computation can be used to synthesise a system to detect Android mobile phone attacks. The attacks include malware, ransomware and mobile botnets. The datasets used in this research are publicly downloadable, available for use with appropriate acknowledgement. The primary source is Drebin. We also used ransomware and mobile botnet datasets from other Android mobile phone researchers. The research in this thesis uses Genetic Programming (GP) to evolve programs to distinguish malicious and non-malicious applications in Android mobile datasets. It also demonstrates the use of GP and Multi-Objective Evolutionary Algorithms (MOEAs) together to explore functional (detection rate) and non-functional (execution time and power consumption) trade-offs. Our results show that malicious and non-malicious applications can be distinguished effectively using only the permissions held by applications recorded in the application's Android Package (APK). Such a minimalist source of features can serve as the basis for highly efficient Android malware detection. Non-functional tradeoffs are also highlight

On the Effectiveness of System API-Related Information for Android Ransomware Detection

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

A cyber-kill-chain based taxonomy of crypto-ransomware features

In spite of being just a few years old, ransomware is quickly becoming a serious threat to our digital infrastructures, data and services. Majority of ransomware families are requesting for a ransom payment to restore a custodian access or decrypt data which were encrypted by the ransomware earlier. Although the ransomware attack strategy seems to be simple, security specialists ranked ransomware as a sophisticated attack vector with many variations and families. Wide range of features which are available in different families and versions of ransomware further complicates their detection and analysis. Though the existing body of research provides significant discussions about ransomware details and capabilities, the all research body is fragmented. Therefore, a ransomware feature taxonomy would advance cyber defenders’ understanding of associated risks of ransomware. In this paper we provide, to the best of our knowledge, the first scientific taxonomy of ransomware features, aligned with Lockheed Martin Cyber Kill Chain (CKC) model. CKC is a well-established model in industry that describes stages of cyber intrusion attempts. To ease the challenge of applying our taxonomy in real world, we also provide the corresponding ransomware defence taxonomy aligned with Courses of Action matrix (an intelligence-driven defence model). We believe that this research study is of high value for the cyber security research community, as it provides the researchers with a means of assessing the vulnerabilities and attack vectors towards the intended victims

A proposed adaptive pre-encryption crypto-ransomware early detection model

Crypto-ransomware is a malware that uses the system's cryptography functions to encrypt user data. The irreversible effect of crypto-ransomware makes it challenging to survive the attack compared to other malware categories. When a crypto-ransomware attack encrypts user files, it becomes difficult to access these files without having the decryption key. Due to the availability of ransomware development tool kits like Ransomware as a Service (RaaS), many ransomware variants are being developed. This contributes to the rise of ransomware attacks witnessed nowadays. However, the conventional approaches employed by malware detection solutions are not suitable to detect ransomware. This is because ransomware needs to be detected as early as before the encryption takes place. These attacks can effectively be handled only if detected during the pre-encryption phase. Early detection of ransomware attacks is challenging due to the limited amount of data available before encryption. An adaptive pre-encryption model is proposed in this paper which is expected to deal with the population concept drift of crypto-ransomware given the limited amount of data collected during the pre-encryption phase of the attack lifecycle. With such adaptability, the model can maintain up-to-date knowledge about the attack behavior and identify the polymorphic ransomware that continuously changes its behavior