The Dark Side(-Channel) of Mobile Devices: A Survey on Network Traffic Analysis

Conti, Mauro; Li, Qian Qian; Maragno, Alberto; Spolaor, Riccardo

doi:10.1109/comst.2018.2843533

Cited by 76 publications

(36 citation statements)

References 75 publications

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“…Our work also has some similarities with the broader topic of network traffic analysis which has been widely studied in recent years [6]. As defined in [7], "network traffic analysis studies inferential methods which take the network traces of a group of devices (from a few to thousands) as input, and give information about those devices, their users, their apps, or the traffic itself as output". Network traffic contains a lot of useful information about the type of devices, users and applications being used.…”

Section: B Network Traffic Analysismentioning

confidence: 86%

Automatic Device Classification from Network Traffic Streams of Internet of Things

Bai

Yao

Kanhere

et al. 2018

2018 IEEE 43rd Conference on Local Computer Networks (LCN)

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With the widespread adoption of Internet of Things (IoT), billions of everyday objects are being connected to the Internet. Effective management of these devices to support reliable, secure and high quality applications becomes challenging due to the scale. As one of the key cornerstones of IoT device management, automatic cross-device classification aims to identify the semantic type of a device by analyzing its network traffic. It has the potential to underpin a broad range of novel features such as enhanced security (by imposing the appropriate rules for constraining the communications of certain types of devices) or context-awareness (by the utilization and interoperability of IoT devices and their high-level semantics) of IoT applications. We propose an automatic IoT device classification method to identify new and unseen devices. The method uses the rich information carried by the traffic flows of IoT networks to characterize the attributes of various devices. We first specify a set of discriminating features from raw network traffic flows, and then propose a LSTM-CNN cascade model to automatically identify the semantic type of a device. Our experimental results using a real-world IoT dataset demonstrate that our proposed method is capable of delivering satisfactory performance. We also present interesting insights and discuss the potential extensions and applications.

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Section: B Network Traffic Analysismentioning

confidence: 86%

Automatic Device Classification from Network Traffic Streams of Internet of Things

Bai

Yao

Kanhere

et al. 2018

2018 IEEE 43rd Conference on Local Computer Networks (LCN)

View full text Add to dashboard Cite

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“…Since 5G systems will be much more complex compared to the previous generations, huge research efforts are dedicated to using ML for security within the novel technological concepts used in 5G, and the services that will be served by 5G networks, ranging from security of novel IoT devices and IoT services [4], [64] to virtual services in clouds [65]. In the physical layer security, ML has been demonstrated to perform well at protecting massive MIMO [66], demodulation [67], and from channel contamination in mmWave, as well as in traffic analysis and fingerprinting [68], [69]. .…”

Section: E Threats To Security Applicationsmentioning

confidence: 99%

Machine Learning Threatens 5G Security

et al. 2020

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Machine learning (ML) is expected to solve many challenges in the fifth generation (5G) of mobile networks. However, ML will also open the network to several serious cybersecurity vulnerabilities. Most of the learning in ML happens through data gathered from the environment. Un-scrutinized data will have serious consequences on machines absorbing the data to produce actionable intelligence for the network. Scrutinizing the data, on the other hand, opens privacy challenges. Unfortunately, most of the ML systems are borrowed from other disciplines that provide excellent results in small closed environments. The resulting deployment of such ML systems in 5G can inadvertently open the network to serious security challenges such as unfair use of resources, denial of service, as well as leakage of private and confidential information. Therefore, in this article we dig into the weaknesses of the most prominent ML systems that are currently vigorously researched for deployment in 5G. We further classify and survey solutions for avoiding such pitfalls of ML in 5G systems.

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“…These three tools all assume access to the source code of the application, whereas Profit uses a fully black-box approach. A number of works analyze mobile application for analyzing side-channels in networks of mobile devices [17], [18], [47].…”

Section: Related Workmentioning

confidence: 99%

Profit: Detecting and Quantifying Side Channels in Networked Applications

Rosner

Kadron

Bang

et al. 2019

Proceedings 2019 Network and Distributed System Security Symposium

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We present a black-box, dynamic technique to detect and quantify side-channel information leaks in networked applications that communicate through a TLS-encrypted stream. Given a user-supplied profiling-input suite in which some aspect of the inputs is marked as secret, we run the application over the inputs and capture a collection of variable-length network packet traces. The captured traces give rise to a vast side-channel feature space, including the size and timestamp of each individual packet as well as their aggregations (such as total time, median size, etc.) over every possible subset of packets. Finding the features that leak the most information is a difficult problem. Our approach addresses this problem in three steps: 1) Global analysis of traces for their alignment and identification of phases across traces; 2) Feature extraction using the identified phases; 3) Information leakage quantification and ranking of features via estimation of probability distribution. We embody this approach in a tool called Profit and experimentally evaluate it on a benchmark of applications from the DARPA STAC program, which were developed to assess the effectiveness of side-channel analysis techniques. Our experimental results demonstrate that, given suitable profiling-input suites, Profit is successful in automatically detecting information-leaking features in applications, and correctly ordering the strength of the leakage for differently-leaking variants of the same application.

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The Dark Side(-Channel) of Mobile Devices: A Survey on Network Traffic Analysis

Abstract: In recent years, mobile devices (e.g., smartphones

Cited by 76 publications

References 75 publications

Automatic Device Classification from Network Traffic Streams of Internet of Things

Automatic Device Classification from Network Traffic Streams of Internet of Things

Machine Learning Threatens 5G Security

Profit: Detecting and Quantifying Side Channels in Networked Applications

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