Side channel attacks on smart home systems: A short overview

This paper provides an overview on side-channel attacks with emphasis on vulnerabilities in the smart home. Smart homes are enabled by the latest developments in sensors, communication technologies, internet protocols, and cloud services. The goal of a smart home is to have smart household devices collaborate without involvement of residents to deliver the variety of services needed for a higher quality of life. However, security and privacy challenges of smart homes have to be overcome in order to fully realize the smart home. Side channel attacks assume data is always leaking, and leakage of data from a smart home reveals sensitive information. This paper starts by reviewing side-channel attack categories, then it gives an overview on recent attack studies on different layers of a smart home and their malicious goals

On Privacy of Encrypted Speech Communications

Silence suppression, an essential feature of speech communications over the Internet, saves bandwidth by disabling voice packet transmissions when silence is detected. However, silence suppression enables an adversary to recover talk patterns from packet timing. In this paper, we investigate privacy leakage through the silence suppression feature. More specifically, we propose a new class of traffic analysis attacks to encrypted speech communications with the goal of detecting speakers of encrypted speech communications. These attacks are based on packet timing information only and the attacks can detect speakers of speech communications made with different codecs. We evaluate the proposed attacks with extensive experiments over different type of networks including commercial anonymity networks and campus networks. The experiments show that the proposed traffic analysis attacks can detect speakers of encrypted speech communications with high accuracy based on traces of 15 minutes long on average

DIFFERENTIALLY PRIVATE TRAFFIC PADDING FOR WEB APPLICATIONS

The wide adoption of Web applications in various sectors of our society, such as government, finance, education, health care, media, etc., has implicitly introduced new security challenges. Among such challenges are side channel attacks that may disclose private user inputs from encrypted raffic. Such attacks might have a serious impact upon user privacy in such applications. In this thesis, we propose a new concept and algorithms that can preserve user privacy in Web applications. In order to achieve this, we define a new privacy model based on a well known concept, namely, differential privacy. The intent is to make padded traffic differentially private such that adversaries cannot infer private user inputs even when they possess prior knowlege about such inputs. At the same time, we intent to achieve a balance bewteen privacy and the incurred communication overhead. In order to demonstrate the usefulness of our model, we implement the proposed algorithms and conduct experiments based on data collected from well known Web applications

Plausibilistic Entropy and Anonymity *

Abstract A common approach behind measuring anonymity is that the larger the anonymity set is the higher the degree of anonymity it supports. Our approach builds upon this intuition proposing a very general and yet precise measure for security properties. Introduced in a paper accepted for ARES 2013 conference, plausibilistic entropy promises to offer an expressive and cost effective solution for quantifying anonymity. This article focuses on a detailed side-by-side comparison between plausibilistic entropy and Shannon entropy and underlines a promising level of compatibility between the two of them. Towards the end we present our vision on how to define a measure for anonymity based on plausibilistic entropy and how such a definition can be employed to serve practical purposes

Practical Analysis of Encrypted Network Traffic

The growing use of encryption in network communications is an undoubted boon for user privacy. However, the limitations of real-world encryption schemes are still not well understood, and new side-channel attacks against encrypted communications are disclosed every year. Furthermore, encrypted network communications, by preventing inspection of packet contents, represent a significant challenge from a network security perspective: our existing infrastructure relies on such inspection for threat detection. Both problems are exacerbated by the increasing prevalence of encrypted traffic: recent estimates suggest that 65% or more of downstream Internet traffic will be encrypted by the end of 2016. This work addresses these problems by expanding our understanding of the properties and characteristics of encrypted network traffic and exploring new, specialized techniques for the handling of encrypted traffic by network monitoring systems. We first demonstrate that opaque traffic, of which encrypted traffic is a subset, can be identified in real-time and how this ability can be leveraged to improve the capabilities of existing IDS systems. To do so, we evaluate and compare multiple methods for rapid identification of opaque packets, ultimately pinpointing a simple hypothesis test (which can be implemented on an FPGA) as an efficient and effective detector of such traffic. In our experiments, using this technique to “winnow”, or filter, opaque packets from the traffic load presented to an IDS system significantly increased the throughput of the system, allowing the identification of many more potential threats than the same system without winnowing. Second, we show that side channels in encrypted VoIP traffic enable the reconstruction of approximate transcripts of conversations. Our approach leverages techniques from linguistics, machine learning, natural language processing, and machine translation to accomplish this task despite the limited information leaked by such side channels. Our ability to do so underscores both the potential threat to user privacy which such side channels represent and the degree to which this threat has been underestimated. Finally, we propose and demonstrate the effectiveness of a new paradigm for identifying HTTP resources retrieved over encrypted connections. Our experiments demonstrate how the predominant paradigm from prior work fails to accurately represent real-world situations and how our proposed approach offers significant advantages, including the ability to infer partial information, in comparison. We believe these results represent both an enhanced threat to user privacy and an opportunity for network monitors and analysts to improve their own capabilities with respect to encrypted traffic.Doctor of Philosoph