APK Auditor: Permission-based Android malware detection system

Android operating system has the highest market share in 2014; making it the most widely used mobile operating system in the world. This fact makes Android users the biggest target group for malware developers. Trend analyses show large increase in mobile malware targeting the Android platform. Android's security mechanism is based on an instrument that informs users about which permissions the application needs to be granted before installing them. This permission system provides an overview of the application and may help gain awareness about the risks. However, we do not have enough information to conclude that standard users read or digital investigators understand these permissions and their implications. Digital investigators need to be on the alert for the presence of malware when examining Android devices, and can benefit from supporting tools that help them understand the capabilities of such malicious code. This paper presents a permission-based Android malware detection system, APK Auditor that uses static analysis to characterize and classify Android applications as benign or malicious. APK Auditor consists of three components: (1) A signature database to store extracted information about applications and analysis results, (2) an Android client which is used by end-users to grant application analysis requests, and (3) a central server responsible for communicating with both signature database and smartphone client and managing whole analysis process. To test system performance, 8762 applications in total, 1853 benign applications from Google's Play Store and 6909 malicious applications from different sources were collected and analyzed by the system developed. The results show that APK Auditor is able to detect most well-known malwares and highlights the ones with a potential in approximately 88% accuracy with a 0.925 specificity.     

Remote Wiping and Secure Deletion on Mobile Devices: A Review

Mobile devices have become ubiquitous in almost every sector of both private and commercial endeavors. As a result of such widespread use in everyday life, many users knowingly and unknowingly save significant amounts of personal and/or commercial data on these mobile devices. Thus, loss of mobile devices through accident or theft can expose users—and their businesses—to significant personal and corporate cost. To mitigate this data leakage issue, remote wiping features have been introduced to modern mobile devices. Given the destructive nature of such a feature, however, it may be subject to criminal exploitation (e.g., a criminal exploiting one or more vulnerabilities to issue a remote wiping command to the victim's device). To obtain a better understanding of remote wiping, we survey the literature, focusing on existing approaches to secure flash storage deletion and provide a critical analysis and comparison of a variety of published research in this area. In support of our analysis, we further provide prototype experimental results for three Android devices, thus providing both a theoretical and applied focus to this article as well as providing directions for further research.     

Automated identification of installed malicious Android applications

Increasingly, Android smartphones are becoming more pervasive within the government and industry, despite the limited ways to detect malicious applications installed to these phones' operating systems. Although enterprise security mechanisms are being developed for use on Android devices, these methods cannot detect previously unknown malicious applications. As more sensitive enterprise information becomes available and accessible on these smartphones, the risk of data loss inherently increases. A malicious application's actions could potentially leave sensitive data exposed with little recourse. Without an effective corporate monitoring solution in place for these mobile devices, organizations will continue to lack the ability to determine when a compromise has occurred. This paper presents research that applies traditional digital forensic techniques to remotely monitor and audit Android smartphones. The smartphone sends changed file system data to a remote server, allowing for expensive forensic processing and the offline application of traditional tools and techniques rarely applied to the mobile environment. The research aims at ascertaining new ways of identifying malicious Android applications and ultimately attempts to improve the state of enterprise smartphone monitoring. An on-phone client, server, database, and analysis framework was developed and tested using real mobile malware. The results are promising that the developed detection techniques identify changes to important system partitions; recognize file system changes, including file deletions; and find persistence and triggering mechanisms in newly installed applications. It is believed that these detection techniques should be performed by enterprises to identify malicious applications affecting their phone infrastructure.     

Digital evidence from mobile telephone applications

In this paper we examine the legal aspects of the forensic investigation of mobile telephone applications. Mobile telephone applications might be involved with a variety of types of computer misuse including fraud, theft, money laundering, dissemination of copyrighted materials or indecent images, or instances where mobile telephone applications have been involved in the transmission of malware for malicious or criminal purposes. In this paper we examine the process of the forensic investigation of mobile telephone applications, and the issues relating to obtaining digital evidence from mobile telephone applications.     

Location privacy: The challenges of mobile service devices

Adding to the current debate, this article focuses on the personal data and privacy challenges posed by private industry's use of smart mobile devices that provide location-based services to users and consumers. Directly relevant to personal data protection are valid concerns about the collection, retention, use and accessibility of this kind of personal data, in relation to which a key issue is whether valid consent is ever obtained from users. While it is indisputable that geo-location technologies serve important functions, their potential use for surveillance and invasion of privacy should not be overlooked. Thus, in this study we address the question of how a legal regime can ensure the proper functionality of geo-location technologies while preventing their misuse. In doing so, we examine whether information gathered from geo-location technologies is a form of personal data, how it is related to privacy and whether current legal protection mechanisms are adequate. We argue that geo-location data are indeed a type of personal data. Not only is this kind of data related to an identified or identifiable person, it can reveal also core biographical personal data. What is needed is the strengthening of the existing law that protects personal data (including location data), and a flexible legal response that can incorporate the ever-evolving and unknown advances in technology.     

Forensic Investigation of Cooperative Storage Cloud Service: Symform as a Case Study

Researchers envisioned Storage as a Service (StaaS) as an effective solution to the distributed management of digital data. Cooperative storage cloud forensic is relatively new and is an under‐explored area of research. Using Symform as a case study, we seek to determine the data remnants from the use of cooperative cloud storage services. In particular, we consider both mobile devices and personal computers running various popular operating systems, namely Windows 8.1, Mac OS X Mavericks 10.9.5, Ubuntu 14.04.1 LTS, iOS 7.1.2, and Android KitKat 4.4.4. Potential artefacts recovered during the research include data relating to the installation and uninstallation of the cloud applications, log‐in to and log‐out from Symform account using the client application, file synchronization as well as their time stamp information. This research contributes to an in‐depth understanding of the types of terrestrial artifacts that are likely to remain after the use of cooperative storage cloud on client devices.     

Malware categorization using dynamic mnemonic frequency analysis with redundancy filtering

The battle between malware developers and security analysts continues, and the number of malware and malware variants keeps increasing every year. Automated malware generation tools and various detection evasion techniques are also developed every year. To catch up with the advance of malware development technologies, malware analysis techniques need to be advanced to help security analysts. In this paper, we propose a malware analysis method to categorize malware using dynamic mnemonic frequencies. We also proposed a redundancy filtering technique to alleviate drawbacks of dynamic analysis. Experimental results show that our proposed method can categorize malware and can reduce storage overheads of dynamic analysis.     

Forensic acquisition and analysis of palm webOS on mobile devices

The emergence of webOS on Palm devices has created new challenges and opportunities for digital investigators. With the purchase of Palm by Hewlett Packard, there are plans to use webOS on an increasing number and variety of computer systems. These devices can store substantial amounts of information relevant to an investigation, including digital photographs, videos, call logs, SMS/MMS messages, e-mail, remnants of Web browsing and much more. Although some files can be obtained from such devices with relative ease, the majority of information of forensic interest is stored in databases on a system partition that many mobile forensic tools do not acquire. This paper provides a methodology for acquiring and examining forensic duplicates of user and system partitions from a device running webOS. The primary sources of digital evidence on these devices are covered with illustrative examples. In addition, the recovery of deleted items from various areas on webOS devices is discussed.     

Scalable code clone search for malware analysis

Reverse engineering is the primary step to analyze a piece of malware. After having disassembled a malware binary, a reverse engineer needs to spend extensive effort analyzing the resulting assembly code, and then documenting it through comments in the assembly code for future references. In this paper, we have developed an assembly code clone search system called ScalClone based on our previous work on assembly code clone detection systems. The objective of the system is to identify the code clones of a target malware from a collection of previously analyzed malware binaries. Our new contributions are summarized as follows: First, we introduce two assembly code clone search methods for malware analysis with a high recall rate. Second, our methods allow malware analysts to discover both exact and inexact clones at different token normalization levels. Third, we present a scalable system with a database model to support large-scale assembly code search. Finally, experimental results on real-life malware binaries suggest that our proposed methods can effectively identify assembly code clones with the consideration of different scenarios of code mutations.     

OBA2: An Onion approach to Binary code Authorship Attribution

A critical aspect of malware forensics is authorship analysis. The successful outcome of such analysis is usually determined by the reverse engineer's skills and by the volume and complexity of the code under analysis. To assist reverse engineers in such a tedious and error-prone task, it is desirable to develop reliable and automated tools for supporting the practice of malware authorship attribution. In a recent work, machine learning was used to rank and select syntax-based features such as n-grams and flow graphs. The experimental results showed that the top ranked features were unique for each author, which was regarded as an evidence that those features capture the author's programming styles. In this paper, however, we show that the uniqueness of features does not necessarily correspond to authorship. Specifically, our analysis demonstrates that many “unique” features selected using this method are clearly unrelated to the authors' programming styles, for example, unique IDs or random but unique function names generated by the compiler; furthermore, the overall accuracy is generally unsatisfactory. Motivated by this discovery, we propose a layered Onion Approach for Binary Authorship Attribution called OBA2. The novelty of our approach lies in the three complementary layers: preprocessing, syntax-based attribution, and semantic-based attribution. Experiments show that our method produces results that not only are more accurate but have a meaningful connection to the authors' styles.     

Forensic data recovery and examination of magnetic swipe card cloning devices

Magnetic swipe card technology is used for many purposes including credit, debit, store loyalty, mobile phone top-up and security identification cards. These types of cards and the details contained on them are often relied upon as a form of identification and personal authentication. As such reliance is placed upon them it is surprising that they do not incorporate more stringent security features, and because of this lack of features it is not surprising that they attract the attention of people who wish to exploit them for illegal gain. The paper introduces the type of technology, and range of devices available for manipulating magnetic swipe card data. It proposes the use of Digital Evidence Bags as a suitable format for the evidential storage of information obtained from them, thus further illustrating the flexibility of the format and demonstrating the diverse range of devices that have to be handled within the digital investigation and law enforcement community.     

Forensic carving of network packets and associated data structures

Using validated carving techniques, we show that popular operating systems (e.g. Windows, Linux, and OSX) frequently have residual IP packets, Ethernet frames, and associated data structures present in system memory from long-terminated network traffic. Such information is useful for many forensic purposes including establishment of prior connection activity and services used; identification of other systems present on the system’s LAN or WLAN; geolocation of the host computer system; and cross-drive analysis. We show that network structures can also be recovered from memory that is persisted onto a mass storage medium during the course of system swapping or hibernation. We present our network carving techniques, algorithms and tools, and validate these against both purpose-built memory images and a readily available forensic corpora. These techniques are valuable to both forensics tasks, particularly in analyzing mobile devices, and to cyber-security objectives such as malware analysis.     

A methodology for the security evaluation within third-party Android Marketplaces

This paper aims to evaluate possible threats with unofficial Android marketplaces, and geo-localize the malware distribution over three main regions: China; Europe; and Russia. It provides a comprehensive review of existing academic literature about security in Android focusing especially on malware detection systems and existing malware databases. Through the implementation of a methodology for identification of malicious applications it has been collected data revealing a 5% of them as malicious in an overall analysis. Furthermore, the analysis shown that Russia and Europe have a preponderance of generic detections and adware, while China is found to be targeted mainly by riskware and malware.     

Investigating the uses of mobile phone evidence in China criminal proceedings

Data from mobile phones are regularly used in the investigation of crime and court proceedings. Previously published research has primarily addressed technical issues or provided operational manuals for using forensic science evidence, rather than analysing human factors and the implementation of forensic tools in investigation settings. Moreover, previous research has focused almost entirely on western countries, and there is a dearth of research into the uses of forensic evidence in China. In this study, a review was carried out of court sentencing documents referring to mobile phone evidence in China over the period 2013–2018. Automated content analysis was used to identify the specific evidence types utilised and the sentencing outcome for each case. Results show that mobile phone evidence was used in 3.3% of criminal proceedings. Among various data types mentioned in criminal proceedings, call records sustained as the most frequently used type of data. After which, instant messaging tools (e.g. WeChat) are an increasing proportion of all mobile phone evidence, from 1% in 2015 to 25% in 2018. For cases that utilised mobile phone data, the analysis of instant messaging and online transaction tools is routine, with little variation in the use of each application (WeChat, Alipay, QQ) for investigations of different types of crime. However, in the majority of criminal cases, mobile phone data function as subsidiary evidence and posed limited impacts on verdict reached. The current findings indicate that a large amount of mobile phone evidence was transformed into other evidence formats or filtered out directly before court proceedings.     

移动设备电子搜查的制度挑战与程序规制——以英美法为比较对象

评估执法的正当需求和手机用户的合法权益成为移动互联网时代的程序规制命题。移动设备搜查对警察权力的重要性和公民权利保护的重要性日益凸显,应当将移动设备视为人们具有合理信赖的一个信息隐私空间。英国、美国的手机搜查规范上的域内不统一导致搜查实践的多样态、公民权利的保护缺陷。但其应对证据的移动电子化的基本思路——根据信息种类或其他变量进行立法,区别对待搜查条件——对中国相关搜查问题的解释和解决具有镜鉴意义。我国需要考虑确立获得搜查批准的原则,通过类型化规则给执法提供明确指导,并且明确搜查后的系列权利保障,进行电子搜查规则的适时调整与动态平衡。     

IntroLib: Efficient and transparent library call introspection for malware forensics

Dynamic malware analysis aims at revealing malware's runtime behavior. To evade analysis, advanced malware is able to detect the underlying analysis tool (e.g. one based on emulation.) On the other hand, existing malware-transparent analysis tools incur significant performance overhead, making them unsuitable for live malware monitoring and forensics. In this paper, we present IntroLib, a practical tool that traces user-level library calls made by malware with low overhead and high transparency. IntroLib is based on hardware virtualization and resides outside of the guest virtual machine where the malware runs. Our evaluation of an IntroLib prototype with 93 real-world malware samples shows that IntroLib is immune to emulation and API hooking detection by malware, uncovers more semantic information about malware behavior than system call tracing, and incurs low overhead (<15% in all-but-one test case) in performance benchmark testing.     

Leveraging virtual machine introspection with memory forensics to detect and characterize unknown malware using machine learning techniques at hypervisor

The Virtual Machine Introspection (VMI) has emerged as a fine-grained, out-of-VM security solution that detects malware by introspecting and reconstructing the volatile memory state of the live guest Operating System (OS). Specifically, it functions by the Virtual Machine Monitor (VMM), or hypervisor. The reconstructed semantic details obtained by the VMI are available in a combination of benign and malicious states at the hypervisor. In order to distinguish between these two states, the existing out-of-VM security solutions require extensive manual analysis. In this paper, we propose an advanced VMM-based, guest-assisted Automated Internal-and-External (A-IntExt) introspection system by leveraging VMI, Memory Forensics Analysis (MFA), and machine learning techniques at the hypervisor. Further, we use the VMI-based technique to introspect digital artifacts of the live guest OS to obtain a semantic view of the processes details. We implemented an Intelligent Cross View Analyzer (ICVA) and implanted it into our proposed A-IntExt system, which examines the data supplied by the VMI to detect hidden, dead, and dubious processes, while also predicting early symptoms of malware execution on the introspected guest OS in a timely manner. Machine learning techniques are used to analyze the executables that are mined and extracted using MFA-based techniques and ascertain the malicious executables. The practicality of the A-IntExt system is evaluated by executing large real-world malware and benign executables onto the live guest OSs. The evaluation results achieved 99.55% accuracy and 0.004 False Positive Rate (FPR) on the 10-fold cross-validation to detect unknown malware on the generated dataset. Additionally, the proposed system was validated against other benchmarked malware datasets and the A-IntExt system outperforms the detection of real-world malware at the VMM with performance exceeding 6.3%.     

Are Internet protocol addresses personal data? The fight against online copyright infringement

Internet Protocol addresses [IP addresses] are central for Internet electronic communications. They individualize computers and their users to make the delivery of data packets possible. IP addresses are also often used to identify websurfers for litigation purposes. In particular, they constitute a key in the fight against online copyright infringement to identify infringers. However, it is a matter of dispute to know if IP addresses are personal data. In a review of relevant case law, the present paper seeks to identify when IP addresses are - or should be - considered as personal data. It suggests a contextual approach to the concept of personal data.     

Tackling the U3 trend with computer forensics

A new technology has emerged, allowing applications to be stored and run on portable devices, such as flash drives and iPods. Sandisk's U3™ smart technology appears to be becoming the standard in this new realm of portability. With the advent of this technology, questions are arising as to the effects it will have on computer forensic investigations. Probably hundreds of thousands of people have purchased devices with U3 or similar technologies already. The fear is that these people will be able to plug their devices into computers, do their misdeeds and then simply unplug those devices, removing any trace. This article will illustrate that this is not the case and will discuss different artifacts that a device such as this will leave behind. For the purposes of this illustration we have investigated the use of some of the most common applications used on U3 drives. This information will serve as a guide to investigating computer crimes perpetrated via U3 or similar technologies. Investigators must keep in mind during their investigations the possibility that their suspects have used such technology, particularly when their investigations seem to lead to a dead end.