12,362 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
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MobileTrust: Secure Knowledge Integration in VANETs
Vehicular Ad hoc NETworks (VANET) are becoming popular due to the emergence of the Internet of Things and ambient intelligence applications. In such networks, secure resource sharing functionality is accomplished by incorporating trust schemes. Current solutions adopt peer-to-peer technologies that can cover the large operational area. However, these systems fail to capture some inherent properties of VANETs, such as fast and ephemeral interaction, making robust trust evaluation of crowdsourcing challenging. In this article, we propose MobileTrust—a hybrid trust-based system for secure resource sharing in VANETs. The proposal is a breakthrough in centralized trust computing that utilizes cloud and upcoming 5G technologies to provide robust trust establishment with global scalability. The ad hoc communication is energy-efficient and protects the system against threats that are not countered by the current settings. To evaluate its performance and effectiveness, MobileTrust is modelled in the SUMO simulator and tested on the traffic features of the small-size German city of Eichstatt. Similar schemes are implemented in the same platform to provide a fair comparison. Moreover, MobileTrust is deployed on a typical embedded system platform and applied on a real smart car installation for monitoring traffic and road-state parameters of an urban application. The proposed system is developed under the EU-founded THREAT-ARREST project, to provide security, privacy, and trust in an intelligent and energy-aware transportation scenario, bringing closer the vision of sustainable circular economy
Multitask Learning for Network Traffic Classification
Traffic classification has various applications in today's Internet, from
resource allocation, billing and QoS purposes in ISPs to firewall and malware
detection in clients. Classical machine learning algorithms and deep learning
models have been widely used to solve the traffic classification task. However,
training such models requires a large amount of labeled data. Labeling data is
often the most difficult and time-consuming process in building a classifier.
To solve this challenge, we reformulate the traffic classification into a
multi-task learning framework where bandwidth requirement and duration of a
flow are predicted along with the traffic class. The motivation of this
approach is twofold: First, bandwidth requirement and duration are useful in
many applications, including routing, resource allocation, and QoS
provisioning. Second, these two values can be obtained from each flow easily
without the need for human labeling or capturing flows in a controlled and
isolated environment. We show that with a large amount of easily obtainable
data samples for bandwidth and duration prediction tasks, and only a few data
samples for the traffic classification task, one can achieve high accuracy. We
conduct two experiment with ISCX and QUIC public datasets and show the efficacy
of our approach
An Experimental Evaluation of the Computational Cost of a DPI Traffic Classifier
A common belief in the scientific community is that traffic classifiers based on deep packet inspection (DPI) are far more expensive in terms of computational complexity compared to statistical classifiers. In this paper we counter this notion by defining accurate models for a deep packet inspection classifier and a statistical one based on support vector machines, and by evaluating their actual processing costs through experimental analysis. The results suggest that, contrary to the common belief, a DPI classifier and an SVM-based one can have comparable computational costs. Although much work is left to prove that our results apply in more general cases, this preliminary analysis is a first indication of how DPI classifiers might not be as computationally complex, compared to other approaches, as we previously though
No NAT'd User left Behind: Fingerprinting Users behind NAT from NetFlow Records alone
It is generally recognized that the traffic generated by an individual
connected to a network acts as his biometric signature. Several tools exploit
this fact to fingerprint and monitor users. Often, though, these tools assume
to access the entire traffic, including IP addresses and payloads. This is not
feasible on the grounds that both performance and privacy would be negatively
affected. In reality, most ISPs convert user traffic into NetFlow records for a
concise representation that does not include, for instance, any payloads. More
importantly, large and distributed networks are usually NAT'd, thus a few IP
addresses may be associated to thousands of users. We devised a new
fingerprinting framework that overcomes these hurdles. Our system is able to
analyze a huge amount of network traffic represented as NetFlows, with the
intent to track people. It does so by accurately inferring when users are
connected to the network and which IP addresses they are using, even though
thousands of users are hidden behind NAT. Our prototype implementation was
deployed and tested within an existing large metropolitan WiFi network serving
about 200,000 users, with an average load of more than 1,000 users
simultaneously connected behind 2 NAT'd IP addresses only. Our solution turned
out to be very effective, with an accuracy greater than 90%. We also devised
new tools and refined existing ones that may be applied to other contexts
related to NetFlow analysis
A user-oriented network forensic analyser: the design of a high-level protocol analyser
Network forensics is becoming an increasingly important tool in the investigation of cyber and computer-assisted crimes. Unfortunately, whilst much effort has been undertaken in developing computer forensic file system analysers (e.g. Encase and FTK), such focus has not been given to Network Forensic Analysis Tools (NFATs). The single biggest barrier to effective NFATs is the handling of large volumes of low-level traffic and being able to exact and interpret forensic artefacts and their context – for example, being able extract and render application-level objects (such as emails, web pages and documents) from the low-level TCP/IP traffic but also understand how these applications/artefacts are being used. Whilst some studies and tools are beginning to achieve object extraction, results to date are limited to basic objects. No research has focused upon analysing network traffic to understand the nature of its use – not simply looking at the fact a person requested a webpage, but how long they spend on the application and what interactions did they have with whilst using the service (e.g. posting an image, or engaging in an instant message chat). This additional layer of information can provide an investigator with a far more rich and complete understanding of a suspect’s activities. To this end, this paper presents an investigation into the ability to derive high-level application usage characteristics from low-level network traffic meta-data. The paper presents a three application scenarios – web surfing, communications and social networking and demonstrates it is possible to derive the user interactions (e.g. page loading, chatting and file sharing ) within these systems. The paper continues to present a framework that builds upon this capability to provide a robust, flexible and user-friendly NFAT that provides access to a greater range of forensic information in a far easier format
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