Characterizing Home Network Traffic: An Inside View

Xu, Kuai; Wang, Feng; Gu, Lin; Gao, Jianhua; Jin, Yaohui

doi:10.1007/978-3-642-31869-6_5

Cited by 7 publications

(5 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similarly, Xu et al [3], constructed a collections platform using OpenWRT and performed principal component analysis on home network traffic. Their work focused primarily on protocols and port clusters.…”

Section: Only Targets Classification Using Unbalanced Datamentioning

confidence: 99%

See 1 more Smart Citation

Identification in Encrypted Wireless Networks Using Supervised Learning

Swartz¹,

Joshi²

2014

2014 IEEE Military Communications Conference

View full text Add to dashboard Cite

In recent years, not only has the number of wireless devices significantly increased, but also their level of integration into daily life. Devices ranging from laptops and cell phones to cameras and TVs are now connected to networks. As the ability to secure these devices advances, public and private organizations are adopting and establishing both public and private wireless networks. Wireless networks ease this integration, but not without cost. The nature of this medium presents challenges. This work aims to demonstrate and codify a mechanism by which we can increase our ability to verify and validate the identity of the device through encrypted data observation. This paper focuses on device identification. Multiple supervised learning techniques were vetted and a reference implementation was constructed and executed using real traffic. Incremental learning methods were identified as the classification mechanism of choice for streaming data.

show abstract

Section: Only Targets Classification Using Unbalanced Datamentioning

confidence: 99%

“…Post analysis was performed on a desktop PC. Others have proposed, and successfully implemented, capturing at the AP [3].…”

Section: Platform and Devicesmentioning

confidence: 99%

Identification in Encrypted Wireless Networks Using Supervised Learning

Swartz¹,

Joshi²

2014

2014 IEEE Military Communications Conference

View full text Add to dashboard Cite

show abstract

“…However, as we observe, the majority of them focus on the IP-based networks, and neglect to consider other wireless protocol. In a preliminary work, Xu et al [ 8 ] developed a monitoring platform to capture network traffic from IP-based IoT devices within a smart-home network. They grouped the packets into flows and exported for each flow useful aggregated attributes such as source and destination IP addresses, ports, protocol, number of packets and bytes.…”

Section: Related Workmentioning

confidence: 99%

Tracing Your Smart-Home Devices Conversations: A Real World IoT Traffic Data-Set

Αναγνωστόπουλος

Σπαθούλας

Viano³

et al. 2020

Sensors

View full text Add to dashboard Cite

Smart-home installations exponential growth has raised major security concerns. To this direction, the GHOST project, a European Union Horizon 2020 Research and Innovation funded project, aims to develop a reference architecture for securing smart-homes IoT ecosystem. It is required to have automated and user friendly security mechanisms embedded into smart-home environments, to protect the users’ digital well being. GHOST project aims to fulfill this requirement and one of its main functionalities is the traffic monitoring for all IoT related network protocols. In this paper, the traffic capturing and monitoring mechanism of the GHOST system, called NDFA, is presented, as the first mechanism that is able to monitor smart-home activity in a holistic way. With the help of the NDFA, we compile the GHOST-IoT-data-set, an IoT network traffic data-set, captured in a real world smart-home installation. This data-set contains traffic from multiple network interfaces with both normal real life activity and simulated abnormal functioning of the devices. The GHOST-IoT-data-set is offered to the research community as a proof of concept to demonstrate the ability of the NDFA module to process the raw network traffic from a real world smart-home installation with multiple network interfaces and IoT devices.

show abstract

“…The recent growth of traffic, applications and connected devices in the digital homes have attracted a rich body of research on residential broadband networks and home networks . A number of studies have focused on end‐to‐end performance in home networks, while are devoted to the characterization, measurement, monitoring, and diagnosis of traffic, gateways, and connected devices in residential home networks.…”

Section: Related Workmentioning

confidence: 99%

Secure the Internet, one home at a time

Wang

Jia

2016

Security Comm. Networks

Self Cite

View full text Add to dashboard Cite

The rapid growth of residential broadband connections and Internet‐enabled home devices have driven the success of many useful applications such as video streaming and remote healthcare. However, poorly managed routers and connected devices in the home are vulnerable under persistent threats and exploitations from cyber attackers across the Internet who continuously identify, compromise, and control devices as part of botnets for launching click fraud, denial of service attacks, spam campaigns. These growing threats and broad damages have made it imperative to understand, characterize, filter, and reduce exploit traffic towards millions of home routers and billions of connected devices in the home. This paper presents a bloom‐filter based analytics framework to capture persistent threats towards the same home routers and to identify correlated attacks towards distributed home networks. Our experimental results based on network traffic collected from real homes over 18months have revealed a number of interesting findings on persistent and correlated threats towards home networks, which calls for improved security and management of home networks. To the best of our knowledge, this paper is the first effort to characterize cyber threats towards home networks and to propose a simple and yet effective approach to identify persistent and aggressive attacks towards home networks. Copyright © 2016 John Wiley & Sons, Ltd.

show abstract

Characterizing Home Network Traffic: An Inside View

Cited by 7 publications

References 19 publications

Identification in Encrypted Wireless Networks Using Supervised Learning

Identification in Encrypted Wireless Networks Using Supervised Learning

Tracing Your Smart-Home Devices Conversations: A Real World IoT Traffic Data-Set

Secure the Internet, one home at a time

Contact Info

Product

Resources

About