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Voice over Internet Protocol (VoIP) applications (apps) provide convenient and low cost means for users to communicate and share information with each other in real-time. Day by day, the popularity of such apps is increasing, and people produce and share a huge amount of data, including their personal and sensitive information. This might lead to several privacy issues, such as revealing user contacts, private messages or personal photos. Therefore, having an up-to-date forensic understanding of these apps is necessary. This chapter presents analysis of forensically valuable remnants of three popular Mobile VoIP (mVoIP) apps on Google Play store, namely: Viber, Skype, and WhatsApp Messenger, in order to figure out to what extent these apps reveal forensically valuable information about the users activities. We performed a thorough investigative study of these three mVoIP apps on smartphone devices. Our experimental results show that several artefacts, such as messages, contact details, phone numbers, images, and video files, are recoverable from the smartphone device that is equipped with these mVoIP apps., Comment: 24 pages, 1 figure, 6 tables

Due to the flexibility, affordability and portability of cloud storage, individuals and companies envisage the cloud storage as one of the preferred storage media nowadays. This attracts the eyes of cyber criminals, since much valuable informa- tion such as user credentials, and private customer records are stored in the cloud. There are many ways for criminals to compromise cloud services; ranging from non-technical attack methods, such as social engineering, to deploying advanced malwares. Therefore, it is vital for cyber forensics examiners to be equipped and informed about best methods for investigation of different cloud platforms. In this chapter, using pCloud (an extensively used online cloud storage service) as a case study, and we elaborate on different kinds of artefacts retrievable during a forensics examination. We carried out our experiments on four different virtual machines running four popular operating systems: a 64 bit Windows 8, Ubuntu 14.04.1 LTS, Android 4.4.2, and iOS 8.1. Moreover, we examined cloud remnants of two different web browsers: Internet Explorer and Google Chrome on Windows. We believe that our study would promote awareness among digital forensic examiners on how to conduct cloud storage forensics examination., Comment: 30 pages, 19 figures, 2 tables

In recent years, mobile devices (e.g., smartphones and tablets) have met an increasing commercial success and have become a fundamental element of the everyday life for billions of people all around the world. Mobile devices are used not only for traditional communication activities (e.g., voice calls and messages) but also for more advanced tasks made possible by an enormous amount of multi-purpose applications (e.g., finance, gaming, and shopping). As a result, those devices generate a significant network traffic (a consistent part of the overall Internet traffic). For this reason, the research community has been investigating security and privacy issues that are related to the network traffic generated by mobile devices, which could be analyzed to obtain information useful for a variety of goals (ranging from device security and network optimization, to fine-grained user profiling). In this paper, we review the works that contributed to the state of the art of network traffic analysis targeting mobile devices. In particular, we present a systematic classification of the works in the literature according to three criteria: (i) the goal of the analysis; (ii) the point where the network traffic is captured; and (iii) the targeted mobile platforms. In this survey, we consider points of capturing such as Wi-Fi Access Points, software simulation, and inside real mobile devices or emulators. For the surveyed works, we review and compare analysis techniques, validation methods, and achieved results. We also discuss possible countermeasures, challenges and possible directions for future research on mobile traffic analysis and other emerging domains (e.g., Internet of Things). We believe our survey will be a reference work for researchers and practitioners in this research field., Comment: 55 pages

Tens of millions of wearable fitness trackers are shipped yearly to consumers who routinely collect information about their exercising patterns. Smartphones push this health-related data to vendors' cloud platforms, enabling users to analyze summary statistics on-line and adjust their habits. Third-parties including health insurance providers now offer discounts and financial rewards in exchange for such private information and evidence of healthy lifestyles. Given the associated monetary value, the authenticity and correctness of the activity data collected becomes imperative. In this paper, we provide an in-depth security analysis of the operation of fitness trackers commercialized by Fitbit, the wearables market leader. We reveal an intricate security through obscurity approach implemented by the user activity synchronization protocol running on the devices we analyze. Although non-trivial to interpret, we reverse engineer the message semantics, demonstrate how falsified user activity reports can be injected, and argue that based on our discoveries, such attacks can be performed at scale to obtain financial gains. We further document a hardware attack vector that enables circumvention of the end-to-end protocol encryption present in the latest Fitbit firmware, leading to the spoofing of valid encrypted fitness data. Finally, we give guidelines for avoiding similar vulnerabilities in future system designs.

Bitcoin is a popular cryptocurrency that records alltransactions in a distributed append-only public ledger calledblockchain. The security of Bitcoin heavily relies on the incentive-compatible proof-of-work (PoW) based distributed consensus pro-tocol, which is run by network nodes called miners. In exchangefor the incentive, the miners are expected to honestly maintainthe blockchain. Since its launch in 2009, Bitcoin economy hasgrown at an enormous rate, and it is now worth about 170 billions of dollars. This exponential growth in the market valueof Bitcoin motivates adversaries to exploit weaknesses for profit,and researchers to discover new vulnerabilities in the system,propose countermeasures, and predict upcoming trends.In this paper, we present a systematic survey that covers thesecurity and privacy aspects of Bitcoin. We start by presenting anoverview of the Bitcoin protocol and its major components alongwith their functionality and interactions within the system. Wereview the existing vulnerabilities in Bitcoin and its underlyingmajor technologies such as blockchain and PoW based consensusprotocol. These vulnerabilities lead to the execution of varioussecurity threats to the normal functionality of Bitcoin. Wethen discuss the feasibility and robustness of the state-of-the-art security solutions. Additionally, we present current privacyand anonymity considerations in Bitcoin and discuss the privacy-related threats to Bitcoin users along with the analysis of theexisting privacy-preserving solutions. Finally, we summarize thecritical open challenges and suggest directions for future researchtowards provisioning stringent security and privacy techniquesfor Bitcoin.

The apps installed on a smartphone can reveal much information about a user, such as their medical conditions, sexual orientation, or religious beliefs. Additionally, the presence or absence of particular apps on a smartphone can inform an adversary who is intent on attacking the device. In this paper, we show that a passive eavesdropper can feasibly identify smartphone apps by fingerprinting the network traffic that they send. Although SSL/TLS hides the payload of packets, side-channel data such as packet size and direction is still leaked from encrypted connections. We use machine learning techniques to identify smartphone apps from this side-channel data. In addition to merely fingerprinting and identifying smartphone apps, we investigate how app fingerprints change over time, across devices and across different versions of apps. Additionally, we introduce strategies that enable our app classification system to identify and mitigate the effect of ambiguous traffic, i.e., traffic in common among apps such as advertisement traffic. We fully implemented a framework to fingerprint apps and ran a thorough set of experiments to assess its performance. We fingerprinted 110 of the most popular apps in the Google Play Store and were able to identify them six months later with up to 96% accuracy. Additionally, we show that app fingerprints persist to varying extents across devices and app versions.

Legacy encryption systems depend on sharing a key (public or private) among the peers involved in exchanging an encrypted message. However, this approach poses privacy concerns. Especially with popular cloud services, the control over the privacy of the sensitive data is lost. Even when the keys are not shared, the encrypted material is shared with a third party that does not necessarily need to access the content. Moreover, untrusted servers, providers, and cloud operators can keep identifying elements of users long after users end the relationship with the services. Indeed, Homomorphic Encryption (HE), a special kind of encryption scheme, can address these concerns as it allows any third party to operate on the encrypted data without decrypting it in advance. Although this extremely useful feature of the HE scheme has been known for over 30 years, the first plausible and achievable Fully Homomorphic Encryption (FHE) scheme, which allows any computable function to perform on the encrypted data, was introduced by Craig Gentry in 2009. Even though this was a major achievement, different implementations so far demonstrated that FHE still needs to be improved significantly to be practical on every platform. First, we present the basics of HE and the details of the well-known Partially Homomorphic Encryption (PHE) and Somewhat Homomorphic Encryption (SWHE), which are important pillars of achieving FHE. Then, the main FHE families, which have become the base for the other follow-up FHE schemes are presented. Furthermore, the implementations and recent improvements in Gentry-type FHE schemes are also surveyed. Finally, further research directions are discussed. This survey is intended to give a clear knowledge and foundation to researchers and practitioners interested in knowing, applying, as well as extending the state of the art HE, PHE, SWHE, and FHE systems., Comment: - Updated. (October 6, 2017) - This paper is an early draft of the survey that is being submitted to ACM CSUR and has been uploaded to arXiv for feedback from stakeholders

Given the huge impact that Online Social Networks (OSN) had in the way people get informed and form their opinion, they became an attractive playground for malicious entities that want to spread misinformation, and leverage their effect. In fact, misinformation easily spreads on OSN and is a huge threat for modern society, possibly influencing also the outcome of elections, or even putting people's life at risk (e.g., spreading "anti-vaccines" misinformation). Therefore, it is of paramount importance for our society to have some sort of "validation" on information spreading through OSN. The need for a wide-scale validation would greatly benefit from automatic tools. In this paper, we show that it is difficult to carry out an automatic classification of misinformation considering only structural properties of content propagation cascades. We focus on structural properties, because they would be inherently difficult to be manipulated, with the the aim of circumventing classification systems. To support our claim, we carry out an extensive evaluation on Facebook posts belonging to conspiracy theories (as representative of misinformation), and scientific news (representative of fact-checked content). Our findings show that conspiracy content actually reverberates in a way which is hard to distinguish from the one scientific content does: for the classification mechanisms we investigated, classification F1-score never exceeds 0.65 during content propagation stages, and is still less than 0.7 even after propagation is complete., Comment: 10 pages, 5 figures

Both authentication and deauthentication are instrumental for preventing unauthorized access to computer and data assets. While there are obvious motivating factors for using strong authentication mechanisms, convincing users to deauthenticate is not straight-forward, since deauthentication is not considered mandatory. A user who leaves a logged-in workstation unattended (especially for a short time) is typically not inconvenienced in any way; in fact, the other way around: no annoying reauthentication is needed upon return. However, an unattended workstation is trivially susceptible to the well-known "lunchtime attack" by any nearby adversary who simply takes over the departed user's log-in session. At the same time, since deathentication does not intrinsically require user secrets, it can, in principle, be made unobtrusive. To this end, this paper designs the first automatic user deauthentication system, FADEWICH, that does not rely on biometric- or behavior-based techniques (e.g., keystroke dynamics) and does not require users to carry any devices. It uses physical properties of wireless signals and the effect of human bodies on their propagation. To assess FADEWICH's feasibility and performance, extensive experiments were conducted with its prototype. Results show that it suffices to have nine inexpensive wireless sensors deployed in a shared office setting to correctly deauthenticate all users within six seconds (90% within four seconds) after they leave their workstation's vicinity. We considered two realistic scenarios where the adversary attempts to subvert FADEWICH and showed that lunchtime attacks fail., Comment: conference

Mobile devices have become ubiquitous due to centralization of private user information, contacts, messages and multiple sensors. Google Android, an open-source mobile Operating System (OS), is currently the market leader. Android popularity has motivated the malware authors to employ set of cyber attacks leveraging code obfuscation techniques. Obfuscation is an action that modifies an application (app) code, preserving the original semantics and functionality to evade anti-malware. Code obfuscation is a contentious issue. Theoretical code analysis techniques indicate that, attaining a verifiable and secure obfuscation is impossible. However, obfuscation tools and techniques are popular both among malware developers (to evade anti-malware) and commercial software developers (protect intellectual rights). We conducted a survey to uncover answers to concrete and relevant questions concerning Android code obfuscation and protection techniques. The purpose of this paper is to review code obfuscation and code protection practices, and evaluate efficacy of existing code de-obfuscation tools. In particular, we discuss Android code obfuscation methods, custom app protection techniques, and various de-obfuscation methods. Furthermore, we review and analyse the obfuscation techniques used by malware authors to evade analysis efforts. We believe that, there is a need to investigate efficiency of the defense techniques used for code protection. This survey would be beneficial to the researchers and practitioners, to understand obfuscation and de-obfuscation techniques to propose novel solutions on Android.

With the digital breakthrough, smart phones have become very essential component. Mobile devices are very attractive attack surface for cyber thieves as they hold personal details (accounts, locations, contacts, photos) and have potential capabilities for eavesdropping (with cameras/microphone, wireless connections). Android, being the most popular, is the target of malicious hackers who are trying to use Android app as a tool to break into and control device. Android malware authors use many anti-analysis techniques to hide from analysis tools. Academic researchers and commercial anti-malware companies are putting great effort to detect such malicious apps. They are making use of the combinations of static, dynamic and behavior based analysis techniques. Despite of all the security mechanisms provided by Android, apps can carry out malicious actions through collusion. In collusion malicious functionality is divided across multiple apps. Each participating app accomplish its part and communicate information to another app through Inter Component Communication (ICC). ICC do not require any special permissions. Also, there is no compulsion to inform user about the communication. Each participating app needs to request a minimal set of privileges, which may make it appear benign to current state-of-the-art techniques that analyze one app at a time. There are many surveys on app analysis techniques in Android; however they focus on single-app analysis. This survey augments this through focusing only on collusion among multiple-apps. In this paper, we present Android vulnerabilities that may be exploited for a possible collusion attack. We cover the existing threat analysis, scenarios, and a detailed comparison of tools for intra and inter-app analysis. To the best of our knowledge this is the first survey on app collusion and state-of-the-art detection tools in Android., Comment: 83 pages, 4 figures, This is a survey paper

Attribute-Based Encryption (ABE) could be an effective cryptographic tool for the secure management of Internet-of-Things (IoT) devices, but its feasibility in the IoT has been under-investigated thus far. This article explores such feasibility for well-known IoT platforms, namely, Intel Galileo Gen 2, Intel Edison, Raspberry Pi 1 Model B, and Raspberry Pi Zero, and concludes that adopting ABE in the IoT is indeed feasible.

Online Social Networks (OSNs), such as Facebook, provide users with tools to share information along with a set of privacy controls preferences to regulate the spread of information. Current privacy controls are efficient to protect content data. However, the complexity of tuning them undermine their efficiency when protecting contextual information (such as the social network structure) that many users believe being kept private. In this paper, we demonstrate the extent of the problem of information leakage in Facebook. In particular, we show the possibility of inferring, from the network "surrounding" a victim user, some information that the victim set as hidden. We developed a system, named OSSINT (Open Source Social Network INTelligence), on top of our previous tool SocialSpy, that is able to infer hidden information of a victim profile and retrieve private information from public one. OSSINT retrieves the friendship network of a victim and shows how it is possible to infer additional private information (e.g., user personal preferences and hobbies). Our proposed system OSSINT goes extra mile about the network topology information, i.e., predicting new friendships using the victim's friends of friends network (2-hop of distance from the victim profile), and hence possibly deduce private information of the full Facebook network. OSSINT correctly improved the previous results of SocialSpy predicting an average of 11 additional friendships with peaks of 20 new friends. Moreover, OSSINT, for the considered victim profiles demonstrated how it is possible to infer real life information such as current city, hometown, university, supposed being private.

The large spread of sensors and smart devices in urban infrastructures are motivating research in the area of Internet of Thing (IoT), to develop new services and improve citizens' quality of life. Sensors and smart devices generate large amount of measurement data from sensing the environment, which is used to enable services, such as control power consumption or traffic density. To deal with such a large amount of information, and provide accurate measurements, service providers can adopt information fusion, which, given the decentralized nature of urban deployments, can be performed by means of consensus algorithms. These algorithms allow distributed agents to (iteratively) compute linear functions on the exchanged data, and take decisions based on the outcome, without the need for the support of a central entity. However, the use of consensus algorithms raises several security concerns, especially when private or security critical information are involved in the computation. This paper proposes ODIN, a novel algorithm that allows information fusion over encrypted data. ODIN is a privacy-preserving extension of the popular consensus gossip algorithm, that prevents distributed agents have direct access to the data while they iteratively reach consensus; agents cannot access even the final consensus value, but can only retrieve partial information, e.g., a binary decision. ODIN uses efficient additive obfuscation and proxy re-encryption during the update steps, and Garbled Circuits to take final decisions on the obfuscated consensus. We discuss the security of our proposal, and show its practicability and efficiency on real-world resource constrained devices, developing a prototype implementation for Raspberry Pi devices.

The Internet Protocol (IP) is the lifeblood of the modern Internet. Its simplicity and universality have fueled the unprecedented and lasting global success of the current Internet. Nonetheless, some limitations of IP have been emerging in recent years. Its original design envisaged supporting perhaps tens of thousands of static hosts operating in a friendly academic-like setting, mainly in order to facilitate email communication and remote access to scarce computing resources. At present IP interconnects billions of static and mobile devices (ranging from supercomputers to IoT gadgets) with a large and dynamic set of popular applications. Starting in mid-1990s, the advent of mobility, wirelessness and the web substantially shifted Internet usage and communication paradigms. This accentuated long-term concerns about the current Internet architecture and prompted interest in alternative designs. The U.S. National Science Foundation (NSF) has been one of the key supporters of efforts to design a set of candidate next-generation Internet architectures. As a prominent design requirement, NSF emphasized "security and privacy by design" in order to avoid the long and unhappy history of incremental patching and retrofitting that characterizes the current Internet architecture. To this end, as a result of a competitive process, four prominent research projects were funded by the NSF in 2010: Nebula, Named-Data Networking (NDN), MobilityFirst (MF), and Expressive Internet Architecture (XIA). This paper provides a comprehensive and neutral analysis of salient security and privacy features (and issues) in these NSF-funded Future Internet Architectures. It also compares the four candidate designs with the current IP-based architecture and discusses similarities, differences, and possible improvements.