782 research outputs found
Your Smart Home Can't Keep a Secret: Towards Automated Fingerprinting of IoT Traffic with Neural Networks
The IoT (Internet of Things) technology has been widely adopted in recent
years and has profoundly changed the people's daily lives. However, in the
meantime, such a fast-growing technology has also introduced new privacy
issues, which need to be better understood and measured. In this work, we look
into how private information can be leaked from network traffic generated in
the smart home network. Although researchers have proposed techniques to infer
IoT device types or user behaviors under clean experiment setup, the
effectiveness of such approaches become questionable in the complex but
realistic network environment, where common techniques like Network Address and
Port Translation (NAPT) and Virtual Private Network (VPN) are enabled. Traffic
analysis using traditional methods (e.g., through classical machine-learning
models) is much less effective under those settings, as the features picked
manually are not distinctive any more. In this work, we propose a traffic
analysis framework based on sequence-learning techniques like LSTM and
leveraged the temporal relations between packets for the attack of device
identification. We evaluated it under different environment settings (e.g.,
pure-IoT and noisy environment with multiple non-IoT devices). The results
showed our framework was able to differentiate device types with a high
accuracy. This result suggests IoT network communications pose prominent
challenges to users' privacy, even when they are protected by encryption and
morphed by the network gateway. As such, new privacy protection methods on IoT
traffic need to be developed towards mitigating this new issue
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
The Good, the Bad, and the Actively Verified
We believe that we can use active probing for compromise recovery. Our intent is to exploit the differences in behavior between compromised and uncompromised systems and use that information to identify those which are not behaving as expected. Those differences may indicate a deviation in either con figuration or implementation from what we expect on the network, either of which suggests that the misbehaving entity might not be trustworthy. In this work, we propose and build a case for a method for using altered behavior directly resulting from or introduced as a side-effect of the compromise of a network service to detect the presence of such a compromise. We use several case studies to illustrate our technique, and demonstrate its feasibility with a software tool developed using our method
Fingerprinting Internet DNS Amplification DDoS Activities
This work proposes a novel approach to infer and characterize Internet-scale
DNS amplification DDoS attacks by leveraging the darknet space. Complementary
to the pioneer work on inferring Distributed Denial of Service (DDoS)
activities using darknet, this work shows that we can extract DDoS activities
without relying on backscattered analysis. The aim of this work is to extract
cyber security intelligence related to DNS Amplification DDoS activities such
as detection period, attack duration, intensity, packet size, rate and
geo-location in addition to various network-layer and flow-based insights. To
achieve this task, the proposed approach exploits certain DDoS parameters to
detect the attacks. We empirically evaluate the proposed approach using 720 GB
of real darknet data collected from a /13 address space during a recent three
months period. Our analysis reveals that the approach was successful in
inferring significant DNS amplification DDoS activities including the recent
prominent attack that targeted one of the largest anti-spam organizations.
Moreover, the analysis disclosed the mechanism of such DNS amplification DDoS
attacks. Further, the results uncover high-speed and stealthy attempts that
were never previously documented. The case study of the largest DDoS attack in
history lead to a better understanding of the nature and scale of this threat
and can generate inferences that could contribute in detecting, preventing,
assessing, mitigating and even attributing of DNS amplification DDoS
activities.Comment: 5 pages, 2 figure
Web Tracking: Mechanisms, Implications, and Defenses
This articles surveys the existing literature on the methods currently used
by web services to track the user online as well as their purposes,
implications, and possible user's defenses. A significant majority of reviewed
articles and web resources are from years 2012-2014. Privacy seems to be the
Achilles' heel of today's web. Web services make continuous efforts to obtain
as much information as they can about the things we search, the sites we visit,
the people with who we contact, and the products we buy. Tracking is usually
performed for commercial purposes. We present 5 main groups of methods used for
user tracking, which are based on sessions, client storage, client cache,
fingerprinting, or yet other approaches. A special focus is placed on
mechanisms that use web caches, operational caches, and fingerprinting, as they
are usually very rich in terms of using various creative methodologies. We also
show how the users can be identified on the web and associated with their real
names, e-mail addresses, phone numbers, or even street addresses. We show why
tracking is being used and its possible implications for the users (price
discrimination, assessing financial credibility, determining insurance
coverage, government surveillance, and identity theft). For each of the
tracking methods, we present possible defenses. Apart from describing the
methods and tools used for keeping the personal data away from being tracked,
we also present several tools that were used for research purposes - their main
goal is to discover how and by which entity the users are being tracked on
their desktop computers or smartphones, provide this information to the users,
and visualize it in an accessible and easy to follow way. Finally, we present
the currently proposed future approaches to track the user and show that they
can potentially pose significant threats to the users' privacy.Comment: 29 pages, 212 reference
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