A Survey and Evaluation of Android-Based Malware Evasion Techniques and Detection Frameworks

Android platform security is an active area of research where malware detection techniques continuously evolve to identify novel malware and improve the timely and accurate detection of existing malware. Adversaries are constantly in charge of employing innovative techniques to avoid or prolong malware detection effectively. Past studies have shown that malware detection systems are susceptible to evasion attacks where adversaries can successfully bypass the existing security defenses and deliver the malware to the target system without being detected. The evolution of escape-resistant systems is an open research problem. This paper presents a detailed taxonomy and evaluation of Android-based malware evasion techniques deployed to circumvent malware detection. The study characterizes such evasion techniques into two broad categories, polymorphism and metamorphism, and analyses techniques used for stealth malware detection based on the malware’s unique characteristics. Furthermore, the article also presents a qualitative and systematic comparison of evasion detection frameworks and their detection methodologies for Android-based malware. Finally, the survey discusses open-ended questions and potential future directions for continued research in mobile malware detection

Acceleration of Statistical Detection of Zero-day Malware in the Memory Dump Using CUDA-enabled GPU Hardware

This paper focuses on the anticipatory enhancement of methods of detecting stealth software. Cyber security detection tools are insufficiently powerful to reveal the most recent cyber-attacks which use malware. In this paper, we will present first an idea of the highest stealth malware, as this is the most complicated scenario for detection because it combines both existing anti-forensic techniques together with their potential improvements. Second, we present new detection methods, which are resilient to this hidden prototype. To help solve this detection challenge, we have analyzed Windows memory content using a new method of Shannon Entropy calculation; methods of digital photogrammetry; the Zipf Mandelbrot law, as well as by disassembling the memory content and analyzing the output. Finally, we present an idea and architecture of the software tool, which uses CUDA enabled GPU hardware to speed-up memory forensics. All three ideas are currently a work in progress

Detecting Malware by Analyzing App Permissions on Android Platform: A Systematic Literature Review

Smartphone adaptation in society has been progressing at a very high speed. Having the ability to run on a vast variety of devices, much of the user base possesses an Android phone. Its popularity and flexibility have played a major role in making it a target of different attacks via malware, causing loss to users, both financially and from a privacy perspective. Different malware and their variants are emerging every day, making it a huge challenge to come up with detection and preventive methodologies and tools. Research has spawned in various directions to yield effective malware detection mechanisms. Since malware can adopt different ways to attack and hide, accurate analysis is the key to detecting them. Like any usual mobile app, malware requires permission to take action and use device resources. There are 235 total permissions that the Android app can request on a device. Malware takes advantage of this to request unnecessary permissions, which would enable those to take malicious actions. Since permissions are critical, it is important and challenging to identify if an app is exploiting permissions and causing damage. The focus of this article is to analyze the identified studies that have been conducted with a focus on permission analysis for malware detection. With this perspective, a systematic literature review (SLR) has been produced. Several papers have been retrieved and selected for detailed analysis. Current challenges and different analyses were presented using the identified articles. 2022 by the authors.This research was funded by the Molde University College-Specialized University in Logistics, Norway, with the support of the Open Access fund.Scopus2-s2.0-8514085354

Acceleration of Statistical Detection of Zero-day Malware in the Memory Dump Using CUDA-enabled GPU Hardware

This paper focuses on the anticipatory enhancement of methods of detecting stealth software. Cyber security detection tools are insufficiently powerful to reveal the most recent cyber-attacks which use malware. In this paper, we will present first an idea of the highest stealth malware, as this is the most complicated scenario for detection because it combines both existing anti-forensic techniques together with their potential improvements. Second, we will present new detection methods which are resilient to this hidden prototype. To help solve this detection challenge, we have analyzed Windows’ memory content using a new method of Shannon Entropy calculation; methods of digital photogrammetry; the Zipf–Mandelbrot law, as well as by disassembling the memory content and analyzing the output. Finally, we present an idea and architecture of the software tool, which uses CUDA-enabled GPU hardware, to speed-up memory forensics. All three ideas are currently a work in progress. Keywords: rootkit detection, anti-forensics, memory analysis, scattered fragments, anticipatory enhancement, CUDA

Techniques for the reverse engineering of banking malware

Malware attacks are a significant and frequently reported problem, adversely affecting the productivity of organisations and governments worldwide. The well-documented consequences of malware attacks include financial loss, data loss, reputation damage, infrastructure damage, theft of intellectual property, compromise of commercial negotiations, and national security risks. Mitiga-tion activities involve a significant amount of manual analysis. Therefore, there is a need for automated techniques for malware analysis to identify malicious behaviours. Research into automated techniques for malware analysis covers a wide range of activities. This thesis consists of a series of studies: an anal-ysis of banking malware families and their common behaviours, an emulated command and control environment for dynamic malware analysis, a technique to identify similar malware functions, and a technique for the detection of ransomware. An analysis of the nature of banking malware, its major malware families, behaviours, variants, and inter-relationships are provided in this thesis. In doing this, this research takes a broad view of malware analysis, starting with the implementation of the malicious behaviours through to detailed analysis using machine learning. The broad approach taken in this thesis differs from some other studies that approach malware research in a more abstract sense. A disadvantage of approaching malware research without domain knowledge, is that important methodology questions may not be considered. Large datasets of historical malware samples are available for countermea-sures research. However, due to the age of these samples, the original malware infrastructure is no longer available, often restricting malware operations to initialisation functions only. To address this absence, an emulated command and control environment is provided. This emulated environment provides full control of the malware, enabling the capabilities of the original in-the-wild operation, while enabling feature extraction for research purposes. A major focus of this thesis has been the development of a machine learn-ing function similarity method with a novel feature encoding that increases feature strength. This research develops techniques to demonstrate that the machine learning model trained on similarity features from one program can find similar functions in another, unrelated program. This finding can lead to the development of generic similar function classifiers that can be packaged and distributed in reverse engineering tools such as IDA Pro and Ghidra. Further, this research examines the use of API call features for the identi-fication of ransomware and shows that a failure to consider malware analysis domain knowledge can lead to weaknesses in experimental design. In this case, we show that existing research has difficulty in discriminating between ransomware and benign cryptographic software. This thesis by publication, has developed techniques to advance the disci-pline of malware reverse engineering, in order to minimize harm due to cyber-attacks on critical infrastructure, government institutions, and industry.Doctor of Philosoph

Resilient and Scalable Android Malware Fingerprinting and Detection

Malicious software (Malware) proliferation reaches hundreds of thousands daily. The manual analysis of such a large volume of malware is daunting and time-consuming. The diversity of targeted systems in terms of architecture and platforms compounds the challenges of Android malware detection and malware in general. This highlights the need to design and implement new scalable and robust methods, techniques, and tools to detect Android malware. In this thesis, we develop a malware fingerprinting framework to cover accurate Android malware detection and family attribution. In this context, we emphasize the following: (i) the scalability over a large malware corpus; (ii) the resiliency to common obfuscation techniques; (iii) the portability over different platforms and architectures. In the context of bulk and offline detection on the laboratory/vendor level: First, we propose an approximate fingerprinting technique for Android packaging that captures the underlying static structure of the Android apps. We also propose a malware clustering framework on top of this fingerprinting technique to perform unsupervised malware detection and grouping by building and partitioning a similarity network of malicious apps. Second, we propose an approximate fingerprinting technique for Android malware's behavior reports generated using dynamic analyses leveraging natural language processing techniques. Based on this fingerprinting technique, we propose a portable malware detection and family threat attribution framework employing supervised machine learning techniques. Third, we design an automatic framework to produce intelligence about the underlying malicious cyber-infrastructures of Android malware. We leverage graph analysis techniques to generate relevant, actionable, and granular intelligence that can be used to identify the threat effects induced by malicious Internet activity associated to Android malicious apps. In the context of the single app and online detection on the mobile device level, we further propose the following: Fourth, we design a portable and effective Android malware detection system that is suitable for deployment on mobile and resource constrained devices, using machine learning classification on raw method call sequences. Fifth, we elaborate a framework for Android malware detection that is resilient to common code obfuscation techniques and adaptive to operating systems and malware change overtime, using natural language processing and deep learning techniques. We also evaluate the portability of the proposed techniques and methods beyond Android platform malware, as follows: Sixth, we leverage the previously elaborated techniques to build a framework for cross-platform ransomware fingerprinting relying on raw hybrid features in conjunction with advanced deep learning techniques

Mitigating security implications of bringing your own device in an enterprise environment

A thesis submitted in partial fulfilment of the requirements for the Degree of Master of Science in Information Systems Security (MSc.ISS) at Strathmore UniversityThe rapid growth in the bring your own device (BYOD) phenomenon, has resulted in the introduction of personal mobile devices in the Enterprise environment. The benefit derived from embracing BYOD in organisations is enhanced mobility of employees and the reduced equipment cost to Enterprises. An effective BYOD management is required to protect company data as diverse mobile devices are finding their way into the enterprise. Available mobile device statistics revealed that 52% of these devices are either lost or stolen worldwide, this becomes a major security concern amid risk of exposure of sensitive and important corporate data. The highlighted risks to the enterprises requires a solution to safeguard, reduce and attempt to mitigate security breaches. This research seeks to answer the following how intrusion detection is leading to increase in cybercrime? Rational look at security challenges for BYOD and how secure is BYOD? The rapid application development (RAD) methodology was applied in this research to prototype a scanning and detection technique to prevent or mitigate threats from BYOD to the enterprise environment. The developed application is a scanner and firewall that will be able to scan, monitor and mitigate malicious attacks on BYOD and present results of scanned devices, ports and blocked devices with a 95% accuracy

Towards a Network-based Approach for Smartphone Security

Smartphones have become an important utility that affects many aspects of our daily life. Due to their large dissemination and the tasks that are performed with them, they have also become a valuable target for criminals. Their specific capabilities and the way they are used introduce new threats in terms of information security. The research field of smartphone security has gained a lot of momentum in the past eight years. Approaches that have been presented so far focus on investigating design flaws of smartphone operating systems as well as their potential misuse by an adversary. Countermeasures are often realized based upon extensions made to the operating system itself, following a host-based design approach. However, there is a lack of network-based mechanisms that allow a secure integration of smartphones into existing IT infrastructures. This topic is especially relevant for companies whose employees use smartphones for business tasks. This thesis presents a novel, network-based approach for smartphone security called CADS: Context-related Signature and Anomaly Detection for Smartphones. It allows to determine the security status of smartphones by analyzing three aspects: (1) their current configuration in terms of installed software and available hardware, (2) their behavior and (3) the context they are currently used in. Depending on the determined security status, enforcement actions can be defined in order to allow or to deny access to services provided by the respective IT infrastructure. The approach is based upon the distributed collection and central analysis of data about smartphones. In contrast to other approaches, it explicitly supports to leverage existing security services both for analysis and enforcement purposes. A proof of concept is implemented based upon the IF-MAP protocol for network security and the Google Android platform. An evaluation verifies (1) that the CADS approach is able to detect so-called sensor sniffing attacks and (2) that reactions can be triggered based on detection results to counter ongoing attacks. Furthermore, it is demonstrated that the functionality of an existing, host-based approach that relies on modifications of the Android smartphone platform can be mimicked by the CADS approach. The advantage of CADS is that it does not need any modifications of the Android platform itself

Cyber Security and Critical Infrastructures

This book contains the manuscripts that were accepted for publication in the MDPI Special Topic "Cyber Security and Critical Infrastructure" after a rigorous peer-review process. Authors from academia, government and industry contributed their innovative solutions, consistent with the interdisciplinary nature of cybersecurity. The book contains 16 articles: an editorial explaining current challenges, innovative solutions, real-world experiences including critical infrastructure, 15 original papers that present state-of-the-art innovative solutions to attacks on critical systems, and a review of cloud, edge computing, and fog's security and privacy issues