2,483 research outputs found
Run-Time Attack Detection in Cryptographic APIs
Cryptographic APIs are often vulnerable to attacks that compromise sensitive cryptographic keys. In the literature we find many proposals for preventing or mitigating such attacks but they typically require to modify the API or to configure it in a way that might break existing applications. This makes it hard to adopt such proposals, especially because security APIs are often used in highly sensitive settings, such as financial and critical infrastructures, where systems are rarely modified and legacy applications are very common. In this paper we take a different approach. We propose an effective method to monitor existing cryptographic systems in order to detect, and possibly prevent, the leakage of sensitive cryptographic keys. The method collects logs for various devices and cryptographic services and is able to detect, offline, any leakage of sensitive keys, under the assumption that a key fingerprint is provided for each sensitive key. We define key security formally and we prove that the method is sound, complete and efficient. We also show that without key fingerprinting completeness is lost, i.e., some attacks cannot be detected. We discuss possible practical implementations and we develop a proof-of-concept log analysis tool for PKCS#11 that is able to detect, on a significant fragment of the API, all key-management attacks from the literature
Prochlo: Strong Privacy for Analytics in the Crowd
The large-scale monitoring of computer users' software activities has become
commonplace, e.g., for application telemetry, error reporting, or demographic
profiling. This paper describes a principled systems architecture---Encode,
Shuffle, Analyze (ESA)---for performing such monitoring with high utility while
also protecting user privacy. The ESA design, and its Prochlo implementation,
are informed by our practical experiences with an existing, large deployment of
privacy-preserving software monitoring.
(cont.; see the paper
Eight years of rider measurement in the Android malware ecosystem: evolution and lessons learned
Despite the growing threat posed by Android malware,
the research community is still lacking a comprehensive
view of common behaviors and trends exposed by malware families
active on the platform. Without such view, the researchers
incur the risk of developing systems that only detect outdated
threats, missing the most recent ones. In this paper, we conduct
the largest measurement of Android malware behavior to date,
analyzing over 1.2 million malware samples that belong to 1.2K
families over a period of eight years (from 2010 to 2017). We
aim at understanding how the behavior of Android malware
has evolved over time, focusing on repackaging malware. In
this type of threats different innocuous apps are piggybacked
with a malicious payload (rider), allowing inexpensive malware
manufacturing.
One of the main challenges posed when studying repackaged
malware is slicing the app to split benign components apart from
the malicious ones. To address this problem, we use differential
analysis to isolate software components that are irrelevant to the
campaign and study the behavior of malicious riders alone. Our
analysis framework relies on collective repositories and recent
advances on the systematization of intelligence extracted from
multiple anti-virus vendors. We find that since its infancy in
2010, the Android malware ecosystem has changed significantly,
both in the type of malicious activity performed by the malicious
samples and in the level of obfuscation used by malware to avoid
detection. We then show that our framework can aid analysts
who attempt to study unknown malware families. Finally, we
discuss what our findings mean for Android malware detection
research, highlighting areas that need further attention by the
research community.Accepted manuscrip
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sPECTRA: a Precise framEwork for analyzing CrypTographic vulneRabilities in Android apps
The majority of Android applications (apps) deals with user's personal data. Users trust these apps and allow them to access all sensitive data. Cryptography, when employed in an appropriate way, can be used to prevent misuse of data. Unfortunately, cryptographic libraries also include vulnerable cryptographic services. Since Android app developers may not be cryptographic experts, this makes apps become the target of various attacks due to cryptographic vulnerabilities. In this work, we present sPECTRA: an automated framework for analyzing wide range of cryptographic vulnerabilities in Android apps at large scale. sPECTRA is more precise and accurate in comparison to state-of-the-art approaches as it reduces both false negatives and false positives. The inclusion of Intelligent UI exploration during dynamic analysis makes sPECTRA deployable to analyze apps at large scale. Moreover, sPECTRA works on apk files without the need of any source code. We evaluate sPECTRA on 7,000 apps collected from 7 most popular Android app stores. Results indicate that 90% of apps are exploitable because of cryptographic vulnerabilities. We made sPECTRA available as an open source
Undermining User Privacy on Mobile Devices Using AI
Over the past years, literature has shown that attacks exploiting the
microarchitecture of modern processors pose a serious threat to the privacy of
mobile phone users. This is because applications leave distinct footprints in
the processor, which can be used by malware to infer user activities. In this
work, we show that these inference attacks are considerably more practical when
combined with advanced AI techniques. In particular, we focus on profiling the
activity in the last-level cache (LLC) of ARM processors. We employ a simple
Prime+Probe based monitoring technique to obtain cache traces, which we
classify with Deep Learning methods including Convolutional Neural Networks. We
demonstrate our approach on an off-the-shelf Android phone by launching a
successful attack from an unprivileged, zeropermission App in well under a
minute. The App thereby detects running applications with an accuracy of 98%
and reveals opened websites and streaming videos by monitoring the LLC for at
most 6 seconds. This is possible, since Deep Learning compensates measurement
disturbances stemming from the inherently noisy LLC monitoring and unfavorable
cache characteristics such as random line replacement policies. In summary, our
results show that thanks to advanced AI techniques, inference attacks are
becoming alarmingly easy to implement and execute in practice. This once more
calls for countermeasures that confine microarchitectural leakage and protect
mobile phone applications, especially those valuing the privacy of their users
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