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N. Asokan

Publications and source records attributed to N. Asokan.

89 records · Page 5Linked to original sources

The Circle Game: Scalable Private Membership Test Using Trusted Hardware

Malware checking is changing from being a local service to a cloud-assisted one where users' devices query a cloud server, which hosts a dictionary of malware signatures, to check if particular applications are potentially malware. Whilst such an architecture gains all the benefits of cloud-based services, it opens up a major privacy concern since the cloud service can infer personal traits of the users based on the lists of applications queried by their devices. Private membership test (PMT) schemes can remove this privacy concern. However, known PMT schemes do not scale well to a large number of simultaneous users and high query arrival rates. We propose a simple PMT approach using a carousel: circling the entire dictionary through trusted hardware on the cloud server. Users communicate with the trusted hardware via secure channels. We show how the carousel approach, using different data structures to represent the dictionary, can be realized on two different commercial hardware security architectures (ARM TrustZone and Intel SGX). We highlight subtle aspects of securely implementing seemingly simple PMT schemes on these architectures. Through extensive experimental analysis, we show that for the malware checking scenario our carousel approach surprisingly outperforms Path ORAM on the same hardware by supporting a much higher query arrival rate while guaranteeing acceptable response latency for individual queries.

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OmniShare: Securely Accessing Encrypted Cloud Storage from Multiple Authorized Devices

Cloud storage services like Dropbox and Google Drive are widely used by individuals and businesses. Two attractive features of these services are 1) the automatic synchronization of files between multiple client devices and 2) the possibility to share files with other users. However, privacy of cloud data is a growing concern for both individuals and businesses. Encrypting data on the client-side before uploading it is an effective privacy safeguard, but it requires all client devices to have the decryption key. Current solutions derive these keys solely from user-chosen passwords, which have low entropy and are easily guessed. We present OmniShare, the first scheme to allow client-side encryption with high-entropy keys whilst providing an intuitive key distribution mechanism to enable access from multiple client devices. Instead of passwords, we use low bandwidth uni-directional out-of-band (OOB) channels, such as QR codes, to authenticate new devices. To complement these OOB channels, the cloud storage itself is used as a communication channel between devices in our protocols. We rely on a directory-based key hierarchy with individual file keys to limit the consequences of key compromise and allow efficient sharing of files without requiring re-encryption. OmniShare is open source software and currently available for Android and Windows with other platforms in development. We describe the design and implementation of OmniShare, and explain how we evaluated its security using formal methods, its performance via real-world benchmarks, and its usability through a cognitive walkthrough.

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IoT Sentinel: Automated Device-Type Identification for Security Enforcement in IoT

With the rapid growth of the Internet-of-Things (IoT), concerns about the security of IoT devices have become prominent. Several vendors are producing IP-connected devices for home and small office networks that often suffer from flawed security designs and implementations. They also tend to lack mechanisms for firmware updates or patches that can help eliminate security vulnerabilities. Securing networks where the presence of such vulnerable devices is given, requires a brownfield approach: applying necessary protection measures within the network so that potentially vulnerable devices can coexist without endangering the security of other devices in the same network. In this paper, we present IOT SENTINEL, a system capable of automatically identifying the types of devices being connected to an IoT network and enabling enforcement of rules for constraining the communications of vulnerable devices so as to minimize damage resulting from their compromise. We show that IOT SENTINEL is effective in identifying device types and has minimal performance overhead.

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Towards Fairness of Cryptocurrency Payments

Motivated by the great success and adoption of Bitcoin, a number of cryptocurrencies such as Litecoin, Dogecoin, and Ethereum are becoming increasingly popular. Although existing blockchain-based cryptocurrency schemes can ensure reasonable security for transactions, they do not consider any notion of fairness. Fair exchange allows two players to exchange digital "items", such as digital signatures, over insecure networks fairly, so that either each player gets the other's item, or neither player does. Given that blockchain participants typically do not trust each other, enabling fairness in existing cryptocurrencies is an essential but insufficiently explored problem. In this paper, we explore the solution space for enabling the fair exchange of a cryptocurrency payment for a receipt. We identify the timeliness of an exchange as an important property especially when one of the parties involved in the exchange is resource-constrained. We introduce the notion of strong timeliness for a fair exchange protocol and propose two fair payment-for-receipt protocol instantiations that leverage functionality of the blockchain to achieve strong timeliness. We implement both and compare their security and efficiency.

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C-FLAT: Control-FLow ATtestation for Embedded Systems Software

Remote attestation is a crucial security service particularly relevant to increasingly popular IoT (and other embedded) devices. It allows a trusted party (verifier) to learn the state of a remote, and potentially malware-infected, device (prover). Most existing approaches are static in nature and only check whether benign software is initially loaded on the prover. However, they are vulnerable to run-time attacks that hijack the application's control or data flow, e.g., via return-oriented programming or data-oriented exploits. As a concrete step towards more comprehensive run-time remote attestation, we present the design and implementation of Control- FLow ATtestation (C-FLAT) that enables remote attestation of an application's control-flow path, without requiring the source code. We describe a full prototype implementation of C-FLAT on Raspberry Pi using its ARM TrustZone hardware security extensions. We evaluate C-FLAT's performance using a real-world embedded (cyber-physical) application, and demonstrate its efficacy against control-flow hijacking attacks.

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Know Your Phish: Novel Techniques for Detecting Phishing Sites and their Targets

Phishing is a major problem on the Web. Despite the significant attention it has received over the years, there has been no definitive solution. While the state-of-the-art solutions have reasonably good performance, they require a large amount of training data and are not adept at detecting phishing attacks against new targets. In this paper, we begin with two core observations: (a) although phishers try to make a phishing webpage look similar to its target, they do not have unlimited freedom in structuring the phishing webpage; and (b) a webpage can be characterized by a small set of key terms; how these key terms are used in different parts of a webpage is different in the case of legitimate and phishing webpages. Based on these observations, we develop a phishing detection system with several notable properties: it is language-independent, can be implemented entirely on client-side, has excellent classification performance and is fast. In addition, we developed a target identification component that can identify the target website that a phishing webpage is attempting to mimic. The target detection component is faster than previously reported systems and can help minimize false positives in our phishing detection system.

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Pitfalls in Designing Zero-Effort Deauthentication: Opportunistic Human Observation Attacks

Deauthentication is an important component of any authentication system. The widespread use of computing devices in daily life has underscored the need for zero-effort deauthentication schemes. However, the quest for eliminating user effort may lead to hidden security flaws in the authentication schemes. As a case in point, we investigate a prominent zero-effort deauthentication scheme, called ZEBRA, which provides an interesting and a useful solution to a difficult problem as demonstrated in the original paper. We identify a subtle incorrect assumption in its adversary model that leads to a fundamental design flaw. We exploit this to break the scheme with a class of attacks that are much easier for a human to perform in a realistic adversary model, compared to the naïve attacks studied in the ZEBRA paper. For example, one of our main attacks, where the human attacker has to opportunistically mimic only the victim's keyboard typing activity at a nearby terminal, is significantly more successful compared to the naïve attack that requires mimicking keyboard and mouse activities as well as keyboard-mouse movements. Further, by understanding the design flaws in ZEBRA as cases of tainted input, we show that we can draw on well-understood design principles to improve ZEBRA's security.

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Characterizing SEAndroid Policies in the Wild

Starting from the 5.0 Lollipop release all Android processes must be run inside confined SEAndroid access control domains. As a result, Android device manufacturers were compelled to develop SEAndroid expertise in order to create policies for their device-specific components. In this paper we analyse SEAndroid policies from a number of 5.0 Lollipop devices on the market, and identify patterns of common problems we found. We also suggest some practical tools that can improve policy design and analysis. We implemented the first of such tools, SEAL.

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On Making Emerging Trusted Execution Environments Accessible to Developers

New types of Trusted Execution Environment (TEE) architectures like TrustLite and Intel Software Guard Extensions (SGX) are emerging. They bring new features that can lead to innovative security and privacy solutions. But each new TEE environment comes with its own set of interfaces and programming paradigms, thus raising the barrier for entry for developers who want to make use of these TEEs. In this paper, we motivate the need for realizing standard TEE interfaces on such emerging TEE architectures and show that this exercise is not straightforward. We report on our on-going work in mapping GlobalPlatform standard interfaces to TrustLite and SGX.

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Open-TEE - An Open Virtual Trusted Execution Environment

Hardware-based Trusted Execution Environments (TEEs) are widely deployed in mobile devices. Yet their use has been limited primarily to applications developed by the device vendors. Recent standardization of TEE interfaces by GlobalPlatform (GP) promises to partially address this problem by enabling GP-compliant trusted applications to run on TEEs from different vendors. Nevertheless ordinary developers wishing to develop trusted applications face significant challenges. Access to hardware TEE interfaces are difficult to obtain without support from vendors. Tools and software needed to develop and debug trusted applications may be expensive or non-existent. In this paper, we describe Open-TEE, a virtual, hardware-independent TEE implemented in software. Open-TEE conforms to GP specifications. It allows developers to develop and debug trusted applications with the same tools they use for developing software in general. Once a trusted application is fully debugged, it can be compiled for any actual hardware TEE. Through performance measurements and a user study we demonstrate that Open-TEE is efficient and easy to use. We have made Open- TEE freely available as open source.

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How Far Removed Are You? Scalable Privacy-Preserving Estimation of Social Path Length with Social PaL

Social relationships are a natural basis on which humans make trust decisions. Online Social Networks (OSNs) are increasingly often used to let users base trust decisions on the existence and the strength of social relationships. While most OSNs allow users to discover the length of the social path to other users, they do so in a centralized way, thus requiring them to rely on the service provider and reveal their interest in each other. This paper presents Social PaL, a system supporting the privacy-preserving discovery of arbitrary-length social paths between any two social network users. We overcome the bootstrapping problem encountered in all related prior work, demonstrating that Social PaL allows its users to find all paths of length two and to discover a significant fraction of longer paths, even when only a small fraction of OSN users is in the Social PaL system - e.g., discovering 70% of all paths with only 40% of the users. We implement Social PaL using a scalable server-side architecture and a modular Android client library, allowing developers to seamlessly integrate it into their apps.

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LookAhead: Augmenting Crowdsourced Website Reputation Systems With Predictive Modeling

Unsafe websites consist of malicious as well as inappropriate sites, such as those hosting questionable or offensive content. Website reputation systems are intended to help ordinary users steer away from these unsafe sites. However, the process of assigning safety ratings for websites typically involves humans. Consequently it is time consuming, costly and not scalable. This has resulted in two major problems: (i) a significant proportion of the web space remains unrated and (ii) there is an unacceptable time lag before new websites are rated. In this paper, we show that by leveraging structural and content-based properties of websites, it is possible to reliably and efficiently predict their safety ratings, thereby mitigating both problems. We demonstrate the effectiveness of our approach using four datasets of up to 90,000 websites. We use ratings from Web of Trust (WOT), a popular crowdsourced web reputation system, as ground truth. We propose a novel ensemble classification technique that makes opportunistic use of available structural and content properties of webpages to predict their eventual ratings in two dimensions used by WOT: trustworthiness and child safety. Ours is the first classification system to predict such subjective ratings and the same approach works equally well in identifying malicious websites. Across all datasets, our classification performs well with average F$_1$-score in the 74--90\% range.

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Citizen Electronic Identities using TPM 2.0

Electronic Identification (eID) is becoming commonplace in several European countries. eID is typically used to authenticate to government e-services, but is also used for other services, such as public transit, e-banking, and physical security access control. Typical eID tokens take the form of physical smart cards, but successes in merging eID into phone operator SIM cards show that eID tokens integrated into a personal device can offer better usability compared to standalone tokens. At the same time, trusted hardware that enables secure storage and isolated processing of sensitive data have become commonplace both on PC platforms as well as mobile devices. Some time ago, the Trusted Computing Group (TCG) released the version 2.0 of the Trusted Platform Module (TPM) specification. We propose an eID architecture based on the new, rich authorization model introduced in the TCGs TPM 2.0. The goal of the design is to improve the overall security and usability compared to traditional smart card-based solutions. We also provide, to the best our knowledge, the first accessible description of the TPM 2.0 authorization model.

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Security of OS-level virtualization technologies: Technical report

The need for flexible, low-overhead virtualization is evident on many fronts ranging from high-density cloud servers to mobile devices. During the past decade OS-level virtualization has emerged as a new, efficient approach for virtualization, with implementations in multiple different Unix-based systems. Despite its popularity, there has been no systematic study of OS-level virtualization from the point of view of security. In this report, we conduct a comparative study of several OS-level virtualization systems, discuss their security and identify some gaps in current solutions.

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ConXsense - Automated Context Classification for Context-Aware Access Control

We present ConXsense, the first framework for context-aware access control on mobile devices based on context classification. Previous context-aware access control systems often require users to laboriously specify detailed policies or they rely on pre-defined policies not adequately reflecting the true preferences of users. We present the design and implementation of a context-aware framework that uses a probabilistic approach to overcome these deficiencies. The framework utilizes context sensing and machine learning to automatically classify contexts according to their security and privacy-related properties. We apply the framework to two important smartphone-related use cases: protection against device misuse using a dynamic device lock and protection against sensory malware. We ground our analysis on a sociological survey examining the perceptions and concerns of users related to contextual smartphone security and analyze the effectiveness of our approach with real-world context data. We also demonstrate the integration of our framework with the FlaskDroid architecture for fine-grained access control enforcement on the Android platform.

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The Company You Keep: Mobile Malware Infection Rates and Inexpensive Risk Indicators

There is little information from independent sources in the public domain about mobile malware infection rates. The only previous independent estimate (0.0009%) [12], was based on indirect measurements obtained from domain name resolution traces. In this paper, we present the first independent study of malware infection rates and associated risk factors using data collected directly from over 55,000 Android devices. We find that the malware infection rates in Android devices estimated using two malware datasets (0.28% and 0.26%), though small, are significantly higher than the previous independent estimate. Using our datasets, we investigate how indicators extracted inexpensively from the devices correlate with malware infection. Based on the hypothesis that some application stores have a greater density of malicious applications and that advertising within applications and cross-promotional deals may act as infection vectors, we investigate whether the set of applications used on a device can serve as an indicator for infection of that device. Our analysis indicates that this alone is not an accurate indicator for pinpointing infection. However, it is a very inexpensive but surprisingly useful way for significantly narrowing down the pool of devices on which expensive monitoring and analysis mechanisms must be deployed. Using our two malware datasets we show that this indicator performs 4.8 and 4.6 times (respectively) better at identifying infected devices than the baseline of random checks. Such indicators can be used, for example, in the search for new or previously undetected malware. It is therefore a technique that can complement standard malware scanning by anti-malware tools. Our analysis also demonstrates a marginally significant difference in battery use between infected and clean devices.

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PeerShare: A System Secure Distribution of Sensitive Data Among Social Contacts

We present the design and implementation of the PeerShare, a system that can be used by applications to securely distribute sensitive data to social contacts of a user. PeerShare incorporates a generic framework that allows different applications to distribute data with different security requirements. By using interfaces available from existing popular social networks. PeerShare is designed to be easy to use for both end users as well as developers of applications. PeerShare can be used to distribute shared keys, public keys and any other data that need to be distributed with authenticity and confidentiality guarantees to an authorized set of recipients, specified in terms of social relationships. We have used \peershare already in three different applications and plan to make it available for developers.

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