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A Study of data Store-based Home Automation 19

A Study of Data Store-based Home Automation

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9th acm conference on data and application security and privacy (CODASPY)

作者： Kafle, Kaushal Moran, Kevin Manandhar, Sunil Nadkarni, Adwait Poshyvanyk, Denys William & Mary Williamsburg VA 23185 USA

ISBN: (纸本)9781450360999

Home automation platforms provide a new level of convenience by enabling consumers to automate various aspects of physical objects in their homes. While the convenience is beneficial, security flaws in the platforms or integrated third-party products can have serious consequences for the integrity of a user's physical environment. In this paper we perform a systematic security evaluation of two popular smart home platforms, Google's Nest platform and Philips Hue, that implement home automation "routines" (i.e., trigger-action programs involving apps and devices) via manipulation of state variables in a centralized data store. Our semi-automated analysis examines, among other things, platform access control enforcement, the rigor of non-system enforcement procedures, and the potential for misuse of routines. This analysis results in ten key findings with serious security implications. For instance, we demonstrate the potential for the misuse of smart home routines in the Nest platform to perform a lateral privilege escalation, illustrate how Nest's product review system is ineffective at preventing multiple stages of this attack that it examines, and demonstrate how emerging platforms may fail to provide even bare-minimum security by allowing apps to arbitrarily add/remove other apps from the user's smart home. Our findings draw attention to the unique security challenges of platforms that execute routines via centralized data stores, and highlight the importance of enforcing security by design in emerging home automation platforms.

关键词： Smart Home Routines Lateral Privilege Escalation Overprivilege

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Adapting Financial Technology Standards to Blockchain Platforms 19

Adapting Financial Technology Standards to Blockchain Platfo...

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Annual acm Southeast conference (acmSE)

作者： Bello, Gabriel Perez, Alfredo J. Columbus State Univ Columbus GA 31907 USA

ISBN: (纸本)9781450362511

Traditional payment systems have standards designed to keep transaction data secure, but blockchain systems are not in scope for such security standards. We compare the Payment application data security Standard's (PA-DSS) applicability towards transaction-supported blockchain platforms to test the standard's applicability. By highlighting the differences in implementation on traditional and decentralized transaction platforms, we critique and adapt the standards to fit the decentralized model. In two case studies, we analyze the QTUM and Ethereum blockchain platforms' industry compliance, as their payment platforms support transactions equivalent to that of applications governed by the PA-DSS. We determine QTUM's and Ethereum's capabilities to properly ensure secure data handling with respect to current security standards. After adapting the PA-DSS and analyzing the QTUM and Ethereum platforms, we revise the new set of standards to create a set of best-practices for ensuring data security on both traditional and blockchain payment systems. We report the security gaps identified on each platform based on the final revision of the standards, presenting a conclusive perspective that neither platform is suitable for business adoption based on the PA-DSS standard's results. Finally, we discuss open research issues.

关键词： Blockchain Smart contracts privacy Compliance User data security frameworks application security Financial technology security standards Payment card industry

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Dynamic Groups and Attribute-Based Access Control for Next-Generation Smart Cars 19

Dynamic Groups and Attribute-Based Access Control for Next-G...

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9th acm conference on data and application security and privacy (CODASPY)

作者： Gupta, Maanak Benson, James Patwa, Farhan Sandhu, Ravi Univ Texas San Antonio Dept Comp Sci Inst Cyber Secur Ctr Secur & Privacy Enhanced Cloud Comp San Antonio TX 78249 USA

ISBN: (纸本)9781450360999

Smart cars are among the essential components and major drivers of future cities and connected world. The interaction among connected entities in this vehicular internet of things (IoT) domain, which also involves smart traffic infrastructure, restaurant beacons, emergency vehicles, etc., offer several real-time applications and provide safer and pleasant driving experience to consumers. With more than 100 million lines of code and hundreds of sensors, these connected vehicles (CVs) expose a large attack surface, which can be remotely compromised and exploited by malicious attackers. security and privacy are big concerns that deter the adoption of smart cars, which if not properly addressed will have grave implications with risk to human life and limb. In this paper, we present a formalized dynamic groups and attribute-based access control (ABAC) model (referred as CV-ABAC(G)) for smart cars ecosystem, where the model not only considers system wide attributes-based security policies but also takes into account the individual user privacy preferences for allowing or denying service notifications, alerts and operations to on-board resources. Further, we introduce a novel notion of groups in vehicular IoT, which are dynamically assigned to moving entities like connected cars, based on their current GPS coordinates, speed or other attributes, to ensure relevance of location and time sensitive notification services, to provide administrative benefits to manage large numbers of entities, and to enable attributes inheritance for fine-grained authorization policies. We present proof of concept implementation of our model in AWS cloud platform demonstrating real-world uses cases along with performance metrics.

关键词： Access Control Smart Cars Connected Vehicles Internet of Things Authorization Attribute-Based Access Control Amazon Web Services (AWS) Autonomous Cars security privacy Cloud Computing

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Result-Based Detection of Insider Threats to Relational databases 19

Result-Based Detection of Insider Threats to Relational Data...

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9th acm conference on data and application security and privacy (CODASPY)

作者： Sallam, Asmaa Bertino, Elisa Purdue Univ Dept Comp Sci W Lafayette IN 47906 USA

ISBN: (纸本)9781450360999

Insiders misuse of resources is a real threat to organizations. According to recent security reports, data has been the most vulnerable to attacks by insiders, especially data located in databases and corporate file servers. Although anomaly detection is an effective technique for flagging early signs of insider attacks, modern techniques for the detection of anomalies in database access are not able to detect several sophisticated data misuse scenarios such as attempts to track data updates and the aggregation of data by an insider that exceeds his/her need to perform job functions. In such scenarios, if the insider does not have prior knowledge of the distribution of the target data, many of his/her queries may extract no data or small amounts of data. Therefore, monitoring the total size of data retrieved by each user and comparing it to normal levels will either result in low anomaly detection accuracy or long time to anomaly detection. In this paper, we propose anomaly detection techniques designed to detect data aggregation and attempts to track data updates. Our techniques infer the normal rates of tables references and tuples retrievals from past database access logs. User queries are then analyzed to detect queries that lead to exceeding the normal data access rates. We evaluated the proposed techniques on the query logs of a real database. The results of the evaluation indicate that when the system configuration parameters are adequately selected and sufficient data is available for training, our techniques have low false alarm generation rate and high anomaly detection accuracy.

关键词： Insider Threats data Analytics for security Relational databases Anomaly Detection Temporal Attacks

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MagneticSpy: Exploiting Magnetometer in Mobile Devices for Website and application Fingerprinting 18

MagneticSpy: Exploiting Magnetometer in Mobile Devices for W...

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2019 Workshop on privacy in the Electronic Society

作者： Matyunin, Nikolay Wang, Yujue Arul, Tolga Kullmann, Kristian Szefer, Jakub Katzenbeisser, Stefan Tech Univ Darmstadt Darmstadt Germany CYSEC Darmstadt Germany Yale Univ New Haven CT 06520 USA Univ Passau Chair Comp Engn Passau Germany

ISBN: (纸本)9781450368308

Recent studies have shown that aggregate CPU usage and power consumption traces on smartphones can leak information about applications running on the system or websites visited. In response, access to such data has been blocked for mobile applications starting from Android 8. In this work, we explore a new source of side-channel leakage for this class of attacks. Our method is based on the fact that electromagnetic activity caused by mobile processors leads to noticeable disturbances in magnetic sensor measurements on mobile devices, with the amplitude being proportional to the CPU workload. Therefore, recorded sensor data can be analyzed to reveal information about ongoing activities. The attack works on a number of devices: we evaluated 80 models of modern smartphones and tablets and observed the reaction of the magnetometer to the CPU activity on 56 of them. On selected devices we were able to successfully identify which application has been opened (with up to 90% accuracy) or which web page has been loaded (up to 91% accuracy). The presented side channel poses a significant risk to end users' privacy, as the sensor data can be recorded from native apps or even from web pages without user permissions. Finally, we discuss possible countermeasures to prevent the presented information leakage.

关键词： information leakage smartphone sensors hardware side channels application fingerprinting website fingerprinting mobile security magnetometer

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Inspect What Your Location History Reveals About You Raising user awareness on privacy threats associated with disclosing his location data 28

Inspect What Your Location History Reveals About You Raising...

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28th acm International conference on Information and Knowledge Management (CIKM)

作者： Boutet, Antoine Gambs, Sebastien Univ Lyon INSA Lyon INRIA CITI Lyon France Univ Quebec Montreal Montreal PQ Canada

ISBN: (纸本)9781450369763

Location is one of the most extensively collected personal data on mobile by applications and third-party services. However, how the location of users is actually processed in practice by the actors of targeted advertising ecosystem remains unclear. Nonetheless, these providers have a strong incentive to create very detailed profile of users to better monetize the collected data. End users are usually not aware about the strength and wide range of inference that can be performed from their mobility traces. In this demonstration, users interact with a web-based application to inspect their location history and to discover the inferential power of this kind of data. Moreover to better understand the possible countermeasures, users can apply a sanitization to protect their data and visualize the impact on both the mobility traces and the associated inferred information. The objective of this demonstration is to raise the user awareness on the profiling capabilities and the privacy threats associated with disclosing his location data as well as how sanitization mechanisms can be efficient to mitigate these privacy risks. In addition, by collecting users feedbacks on the personal information revealed and the usage of a geosanitization mechanism, we hope that this demonstration will also be useful to constitute a new and valuable dataset on users perceptions on these questions.

关键词： Location privacy Awareness User Tracking Mobile applications

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Limitless HTTP in an HTTPS World: Inferring the Semantics of the HTTPS Protocol without Decryption 19

Limitless HTTP in an HTTPS World: Inferring the Semantics of...

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9th acm conference on data and application security and privacy (CODASPY)

作者： Anderson, Blake Chi, Andrew Dunlop, Scott McGrew, David Cisco San Jose CA 95134 USA Univ N Carolina Chapel Hill NC 27515 USA

ISBN: (纸本)9781450360999

We present new analytic techniques for inferring HTTP semantics from passive observations of HTTPS that can infer the value of important fields including the status-code, Content-Type, and Server, and the presence or absence of several additional HTTP header fields, e.g., Cookie and Referer. Our goals are to improve the understanding of the confidentiality limitations of HTTPS, and to explore benign uses of traffic analysis that could replace HTTPS interception and static private keys in some scenarios. We found that our techniques increase the efficacy of malware detection, but they do not enable more powerful website fingerprinting attacks against Tor. Our broader set of results raises concerns about the confidentiality goals of TLS relative to a user's expectation of privacy, warranting future research. We apply our methods to the semantics of both HTTP/1.1 and HTTP/2 on data collected from automated runs of Firefox 58.0, Chrome 63.0, and Tor Browser 7.0.11 in a lab setting, and from applications running in a malware sandbox. We obtain ground truth plaintext for a diverse set of applications from the malware sandbox by extracting the key material needed for decryption from RAM post-execution. We developed an iterative approach to simultaneously solve several multi-class (field values) and binary (field presence) classification problems, and we show that our inference algorithm achieves an unweighted F-1 score greater than 0.900 for most HTTP fields examined.

关键词： Transport Layer security Network Traffic Analysis Machine Learning Malware Detection Website Fingerprinting

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SKA-CaNPT: Secure Key Agreement using Cancelable and Noninvertible Biometrics based on Periodic Transformation 19

SKA-CaNPT: Secure Key Agreement using Cancelable and Noninve...

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9th acm conference on data and application security and privacy (CODASPY)

作者： Eskandarian, Laleh Akdogan, Dilara Karaoglan Altop, Duygu Levi, Albert Sabanci Univ Fac Engn & Nat Sci Istanbul Turkey

ISBN: (纸本)9781450360999

Nowadays, many of the security-providing applications use biometrics-based authentication. However, since each person's biometrics is unique and non-replaceable, once it is compromised, it will be compromised forever. Therefore, it is hard for the users to trust biometrics. To overcome this problem, in this paper, we propose a novel secure key agreement protocol SKA-CaNPT. Here, we use a periodic transformation function to make biometrics cancelable and noninvertible. At the very end of our SKA-CaNPT protocol, the user and the server make an agreement on a symmetric shared key that is based on the feature points of the user's biometrics. Therefore, if the transformed data is compromised, then just by changing one of the inputs of the transformation function, we can renew the cryptographic key. As a proof of concept, we apply our SKA-CaNPT protocol on fingerprints. Besides, we apply different security analyses on our protocol. We use Shannon's entropy and Hamming distance metrics to analyze the randomness and the distinctiveness of the agreed keys. Moreover, according to the low IKGR (Incorrect Key Generation Rate), high CKGR (Correct Key Generation Rate) and high attack complexity possessed by our SKA-CaNPT protocol, we can conclude that our scheme is secure against brute-force, replay and impersonation attacks.

关键词： Biometrics bio-cryptography cancelable biometrics noninvertible biometrics periodic transformation fingerprints key agreement security analysis

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Understanding the Responsiveness of Mobile App Developers to Software Library Updates 19

Understanding the Responsiveness of Mobile App Developers to...

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9th acm conference on data and application security and privacy (CODASPY)

作者： Yasumatsu, Tatsuhiko Watanabe, Takuya Kanei, Fumihiro Shioji, Eitaro Akiyama, Mitsuaki Mori, Tatsuya Waseda Univ Tokyo Japan NTT Secure Platform Labs Tokyo Japan RIKEN AIP Tokyo Japan

ISBN: (纸本)9781450360999

This paper reports a longitudinal measurement study aiming to understand how mobile app developers are responsive to updates of software libraries over time. To quantify their responsiveness to library updates, we collected 21,046 Android apps, which equated 142,611 unique application package kit (APK) files, each corresponding to a different version of an app. The release dates of these APK files spanned across 9 years. The key findings we derived from our analysis are as follows. (1) We observed an undesirable level of responsiveness of app developers;50% of library update adoptions by app developers were performed for more than 3 months after the release date of the library, and 50% of outdated libraries used in apps were retained for over 10 months. (2) Deploying a security fix campaign in the app distribution market effectively reduced the number of apps with unfixed vulnerabilities;however, CVE-numbered vulnerabilities (without a campaign) were prone to remain unfixed. (3) The responsiveness of app developers varied and depended on multiple factors, for example, popular apps with a high number of installations had a better response to library updates and, while it took 77 days on average for app developers to adopt version updates for advertising libraries, it took 237 days for updates of utility libraries to be adopted. We discuss practical ways to eliminate libraries with vulnerabilities and to improve the responsiveness of app developers to library updates.

关键词： Android security Mobile Apps Measurement Software Library Mobile App Developers

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CCSW 2019 - proceedings of the 2019 acm SIGSAC conference on Cloud Computing security Workshop

CCSW 2019 - Proceedings of the 2019 ACM SIGSAC Conference on...

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10th acm SIGSAC conference on Cloud Computing security Workshop, CCSW 2019, co-located with the 26th acm conference on Computer and Communications security, CCS 2019

ISBN: (纸本)9781450368261

The proceedings contain 17 papers. The topics discussed include: verifiable computation using smart contracts;secure delegation of isogeny computations and cryptographic applications;ABSTRACT: cloud-based image classification service is not robust to affine transformation: a forgotten battlefield;MalPro: a learning-based malware propagation and containment modeling;PrivFL: practical privacy-preserving federated regressions on high-dimensional data over mobile networks;AppMine: behavioral analytics for web application vulnerability detection;efficient multi-party private set intersection against malicious adversaries;secure data exchange: a marketplace in the cloud;quantifying information leakage of deterministic encryption;and simple forward and backward private searchable symmetric encryption schemes with constant number of roundtrips.

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