A mobile operating system created and developed by apple inc. exclusively for its hardware.

2 Mobile Operating Systems

An OS is a software interface that is responsible for managing and operating hardware units and assisting the user to use those units. For mobile phones, OSs have been developed to enable users to use phones in much the same way as personal computers were used 1 or 2 decades ago. The most well-known mobile OSs are Android, iOS, Windows phone OS, and Symbian. The market share ratios of those OSs are Android 47.51%, iOS 41.97%, Symbian 3.31%, and Windows phone OS 2.57%. There are some other mobile OSs that are less used (BlackBerry, Samsung, etc.) [46]. In the next section, we will briefly explain each of these OSs.

Mobile device management

While most devices that are not running a mobile operating system, that is, Windows, OS X, Linux, etc. have a well-established seat of tools and features that allow them to be centrally managed, this may not hold true with mobile devices. We generally want to be able to mandate patching and software upgrades, force changing of passwords at some interval, regulate and track installed software, adjust settings to a standard dictated by our policies, and a number of other similar functions. In order to enable these types of tasks, we generally turn to an external Mobile Device Management (MDM) solution such as those developed by Good Technologies, MobileIron, and a number of others.

The exact architecture of an MDM solution will vary from one vendor to another, but most utilize an agent on the mobile device that exists to enforce a certain configuration on the client. These agents typically regulate access to enterprise resources, such as e-mail, calendaring, or network resources, and can discontinue access by the client in the event that it becomes noncompliant in configuration, is stolen, or the user’s employment is terminated. Additionally, many MDM solutions enable the device to be remotely wiped, either completely or just corporate data, and/or disabled entirely.

As the distinction between mobile and nonmobile devices becomes narrower all the time, vendors of MDM solutions have begun to implement support of some devices that have been traditionally considered nonmobile. While this may seem like a considerable overlap with existing enterprise management tools, the ability to remotely manage both mobile and nonmobile devices using the same tools and techniques would result in less load on administrative resources, and would enable a greater uniformity across the set of devices in question.

Abstract

Since its launch in 2007, Google's open source mobile operating system Android has become the most prominent OS for smartphones. Availability of 3 million Android apps on official repository, Google Play Store, and a not too tightly controlled environment for app developers have added to the popularity of Android and growth of Android devices. This, however, has also provided an opportunity for malware writers to create inroads into Android devices through malicious apps on App stores including Google Play. These malicious apps may access and leak sensitive information such as details of calls, SMS, emails, pictures, contacts, location, password, etc. Loss of this personal data may lead to fraud, financial loss, threatening, etc. Various solutions based on static, dynamic, or hybrid analysis are proposed by state-of-the-art in the last decade. However, malware writers have also come up with ingenious ways of circumventing detection tools. Recent malware deploy threats like obfuscated and encrypted code, dynamic code loading, and reflection, etc. which fail static analysis approaches employing bytecode for analysis. Dynamic analysis is robust against these evasive methods because it executes the application in the controlled environment. In this chapter, we review dynamic analysis techniques for Android and evaluate these experimentally. We discuss various antidetection methods used by recent Android malware to circumvent even dynamic analysis. We compare the effectiveness of various state-of-the-art dynamic analysis techniques against antidetection techniques. With this chapter, we try to highlight issues and challenges concerned to Android malware analysis techniques that require the attention of research community to avoid loss of end user.

8.3 Network Threats

Avoid or limit the use of open, public 802.11 wireless networks. Additionally, ensure that you are using the later, stricter security protocols such as WPA2 and avoid the earlier flawed protocols such as WEP and WPA. Where possible, use a virtual private network (VPN) to connect to your organization’s secure network. However, a poor VPN technology that doesn’t use “pinning” or a similar technology to validate an encrypted authentication session may not do you any good if you’re using a fully insecure, crypto-less Wi-Fi network; as cyber adversaries may be able to access your username, password, or passphrase, as well as other private information by a MITM attack tracking your keystrokes. Mobile devices typically support cellular networks, as well as local wireless networks (Wi-Fi) and Bluetooth. Each of these types of networks can host different classes of threats:

Android

Which brings us to Android, the world’s most popular mobile operating system with an incredible 85 per cent of global market share as of late 2014, according to figures from analyst firm IDC. “Every day another million users power up their Android devices for the first time and start looking for apps, games, and other digital content,” as the software’s own home on the internet states.

One of Android’s features is its ability to backup users’ data and settings. Thus, when they move to a new Android device – which many consumers do at least annually – Google is able to configure the new device very much like the old one, seamlessly via the cloud (which to be fair, other device manufacturers also offer). What most consumers won’t be aware of, however, is that the feature also means that Google knows just about every Wi-Fi password in the world, given Android’s install-base. So most data it can’t get by driving past in a Street View Car can be accessed anyway thanks to Android.

Abstract

The Android operating system is one of the most popular open-source platforms in the mobile operating system market. It had a worldwide smart-phone market share of 68% at the second quarter of 2012. However, there has been little research on test coverage and test cost-effectiveness in this platform. The goal of this case study reported in this paper is to assess test coverage, fault detection effectiveness, test cost-effectiveness, and defect density in code-base of version 2.1 of the Android platform. We raise and address five research questions (RQs) in this study. Among our results are: (1) in contrary to what one would expect, for packages with larger coverage values (meaning more rigorous testing), it is not necessarily true that less defects have been reported by the users after release. Also, it is not necessarily true that components with low coverage have more defects; (2) we re-confirm (replicate) the existence of correlation between code coverage and mutation score, similar to existing studies; and (3) the package with the highest defect density (DD) in the Android code-base is Music (DD = 0.19 per 1 KLOC) and the package with the lowest DD value is ContactsProvider (DD = 0.0003). Results of our study will help us and other researchers to get a better view on test coverage, fault detection effectiveness, test cost-effectiveness, and defect density in Android code-base.

3.1 The principle of least privilege

Humans are inherently social, and our notion of trust is innate. In fact, trust has always been closely associated to survival. We routinely limit the amount of information that we share with others on the basis of what we feel they needs to know. Information, if one were to apply a digital analogy, is shared on a “policy of least privilege.”

Although we can understand this instinctively, one of the inherent challenges in cyber security is accommodating the fact that humans also expect their computer systems to have the same ability, to switch between trust domains, and decide what information should be shared, how it should be shared, and what level of access somebody should have to it. We see no issue with using the same mobile device to chat via Twitter, for example, whereas moments later, check our personal bank balances. Phishing attacks continue to grow in popularity, and the consequences of an uninformed user clicking what looks to be a legitimate link in an e-mail, only to see their action invite malware that attacks vulnerability in an operating system, are all too familiar.

The challenge security teams face is both to protect their networks and simultaneously allow their employees to leverage the productivity benefits afforded by, for example, social media and cloud-based applications.

This reality is further complicated by the very business model the “free” Internet has been built around. Online advertising companies and search engines benefit from compromised security. For example, many sites require personal information from users, and make money by selling that information to marketing firms and vendors. A user may be persuaded that a site will respect the user’s right to privacy, even when the implicit exchange is free service for the right to sell your data.

That instinctive ability to determine the level of privilege somebody should have in a social relationship is dependent upon “granularity.” Unfortunately, today’s operating systems (OSes) and applications (e.g., web browsers) are incapable of providing either a similar degree of granularity, or effective embodiment of trust domains, or confinement to apply the concept of least privilege. Critical OS design concepts come from a pre-internet age, where designers did not have to take into account targeted attacks that exploit unpatched weaknesses within the operating system or software, or deliberate monitoring systems that jeopardize individual privacy.

Although all operating systems utilize some kind of software isolation (e.g., sandboxing), access controls, and hardware defense (e.g., user and kernel modes) to segment applications, OS services and data, with the objective of applying least privilege, they cannot manage their inherent, latent vulnerability.

Operating systems offer hackers an enormous attack surface (e.g., the Windows operating system and Android mobile operating systems have approximately 50,000,000 and 10,000,000 lines of code respectively2). Mobile device market differentiation boils down to a constantly growing feature list, but it is exactly those features that expose the consumers mobile device to vulnerabilities – approximately 1 significant defect/KLOC that can allow an attacker to increase execution rights and compromise the computer to get into both local and remote resources.3

Consumers are also susceptible to the existence of applications that allow websites and search engines to monitor their behavior and betray privacy. Often these applications (e.g., Google Chrome) come from companies whose very aim is to profit from their monitoring of consumers, while apparently offering value (functionality, or claims of security) within their applications. Although privacy is a sophisticated subject that requires an extensive attention on its own, it likewise utilizes a solid implementation of least privilege. Both security and privacy necessitate that our computers are trustworthy.

Porting to Other Mobile Operating Systems

It is an interesting question as to whether presented techniques for Windows Mobile can be used for other mobile operating systems as well. Unfortunately, the answer to this is “generally, no.” The system architectures are very different from Windows Mobile. Our approach is based on the fact that it is very easy for untrusted software to run as a kernel-mode process. Other operating systems are more restricted, so the support of the operating system manufacturer would be required to get a sufficient trust level for the sandbox program.

Examples of the more restricted operating systems are Symbian OS and the iPhone operating system. Symbian OS, especially, implements very restricted access to almost anything, beginning with system version 9. If software wants to access system directories or manipulate other processes, it needs special Symbian OS capabilities that are not easy to obtain.

The upcoming Linux phones promise to be more accessible because of the open-source nature of their operating system. Examples are the Open Handset Alliance (Android), the LiMo foundation, and Openmoko. But the future still must determine which of these platforms will really be used and gain wide acceptance.

35.2.1 Programming language and platform

Most computer-assisted intervention systems require interaction with the physical world and thus require some level of real-time performance. This often leads to the choice of a compiled language, such as C++, due to its faster execution times. On the other hand, compiled languages are not ideal for interactive debugging because it is necessary to recompile the program every time a change is made. In this case, an interpreted language such as Python may be more attractive. It is not unusual for systems to be constructed with a mix of languages to attempt to reap the benefits of each language. One common design is to use C++ for performance-critical modules, with Python scripts to “glue” these modules together. An example of this design is provided by 3D Slicer [6], which consists of Python scripts that utilize a large collection of C++ modules.

The platform choice refers not only to the operating system, but possibly also to an environment or framework. Two popular operating systems are Windows and Linux, though OS X is also used, and mobile operating systems, such as iOS and Android, are becoming more prevalent. Development environments often encapsulate the underlying operating system and thus can provide some amount of portability. Examples of these environments include Matlab,1 Qt, and 3D Slicer. In other cases, such as Robot Operating System (ROS) [8], the platform may require a specific operating system (e.g., ROS is best supported on Linux).

Typically, decisions about programming language are based on developer familiarity, availability of existing software packages, and performance considerations. The situation is similar for choosing the platform, possibly with an additional constraint regarding the availability of drivers for specific hardware components.

What’s Different about Mobile?

Managing and controlling data-at-rest on a legacy PC is difficult. The operating system provides very little in terms of isolating corporate data from personal. And in most cases all applications have access to all data on the PC. If you have access to the PC, you’re considered a trusted user. This provides a huge threat surface leading to data loss, malware attacks, and breaches (Figure 1.1).

Mobile operating systems are different from their PC counterparts in that they employ operating system sandboxing. This sandbox approach separates each app and its data from other apps and their data. This also includes isolation from the operating system as well. But there are features in the mobile operating systems that provide ways in which data can be shared and are typically user-driven. A user can receive an email with an attachment in the email app, open that attachment in a secondary app that allows for it to be edited, and then open the document in a third app to print it over-the-air to a printer, and furthermore upload it to a cloud service. Additionally, features like copy/paste, screenshot, email forwarding, and more exist as well. But what’s important is that much of this is user-driven or user-defined rather than allowing an app to natively perform these functions.

Another important aspect of the mobile era is that the traditional network edge has now become blurred. Mobile devices are very ubiquitous and access enterprise data over the network in a variety of ways. Whether its cloud services, web 2.0, data backup services, multiple network services (cellular, Wi-Fi, NFC, etc.); all make management of this data far more challenging. No longer can we look at the network as a single entry point, the network edge has disappeared, now data lives everywhere.

Last, but certainly not least, is the emergence of BYOD (Bring your own device). In the PC world, IT provided the computer preconfigured with security controls. But in the mobile world, people show up with their personal devices looking to connect them to their enterprise network or cloud. And even those organizations with Corporate issued devices, inevitably find that the user will use it for personal use. In either circumstance, the user has a plethora of features by which they can share, forward, or upload data to and from the network. This has also made the end-user the low hanging fruit for attack. Since these mobile devices are always connected, this provides a much larger window of compromise for attack and exfiltration of data.