Characterizing home network traffic: an inside view

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

doi:10.1007/s00779-013-0711-x

Cited by 16 publications

(8 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As several home appliances, such as air conditioners, heaters, and refrigerators, are being connected to the Internet, and they became targets of cyberattacks [6], [7], which can cause serious problems such as compromising safety and even harming users [15]. Therefore, the methods to detect attacks for the home IoT devices have been proposed [10]- [14]. For example, Hodo et al proposed a method to detect intruded IoT devices.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Anomaly Detection in Smart Home Operation From User Behaviors and Home Conditions

Yamauchi

Ohsita

Murata

et al. 2020

IEEE Trans. Consumer Electron.

View full text Add to dashboard Cite

As several home appliances, such as air conditioners, heaters, and refrigerators, were connecting to the Internet, they became targets of cyberattacks, which cause serious problems such as compromising safety and even harming users. We have proposed a method to detect such attacks based on user behavior. This method models user behavior as sequences of user events including operation of home IoT (Internet of Things) devices and other monitored activities. Considering users behave depending on the condition of the home such as time and temperature, our method learns event sequences for each condition. To mitigate the impact of events of other users in the home included in the monitored sequence, our method generates multiple event sequences by removing some events and learning the frequently observed sequences. For evaluation, we constructed an experimental network of home IoT devices and recorded time data for four users entering/leaving a room and operating devices. We obtained detection ratios exceeding 90% for anomalous operations with less than 10% of misdetections when our method observed event sequences related to the operation. In this article, we also discuss the effectiveness of our method by comparing with a method learning users' behavior by Hidden Markov Models.

show abstract

Section: Related Workmentioning

confidence: 99%

“…Most of the current attacks targeting IoT devices aim to create botnets [8], [9]. Such attacks are detectable by methods based on analyses of attacker behaviors [10]- [12] or through usual traffic comparisons [13], [14].…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection in Smart Home Operation From User Behaviors and Home Conditions

Yamauchi

Ohsita

Murata

et al. 2020

IEEE Trans. Consumer Electron.

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: Toolsmentioning

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 16 publications

References 26 publications

Anomaly Detection in Smart Home Operation From User Behaviors and Home Conditions

Anomaly Detection in Smart Home Operation From User Behaviors and Home Conditions

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