Poster Abstract: Passive Activity Classification of Smart Homes through Wireless Packet Sniffing

Choi, Kwon Nung; Dahanayaka, Thilini; Kennedy, David; Thilakarathna, Kanchana; Seneviratne, Suranga; Kanhere, Salil S.; Mohapatra, Prasant

doi:10.1109/ipsn48710.2020.00-13

Cited by 5 publications

(1 citation statement)

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“…Figure 1 shows the attacker sniffing wireless packets to detect the activities inside the home. From Figure 1, any passive hacker within 10 m of the target home can passively sniff encrypted packets of the interactions with smart home devices [22]. Mirai, a distributed denial of service (DDOS) attack, affected many IoT systems like IP cameras and home routers, which made the internet inaccessible to users.…”

Section: Introductionmentioning

confidence: 99%

Network intrusion detection system by applying ensemble model for smart home

Amru,

Jagadeesh Kannan,

Narasimhan Ganesh

et al. 2024

IJECE

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The exponential advancements in recent technologies for surveillance become an important part of life. Though the internet of things (IoT) has gained more attention to develop smart infrastructure, it also provides a large attack surface for intruders. Therefore, it requires identifying the attacks as soon as possible to provide a secure environment. In this work, the network intrusion detection system, by applying the ensemble model (NIDSE) for Smart Homes is designed to identify the attacks in the smart home devices. The problem of classifying attacks is considered a classification predictive modeling using eXtreme gradient boosting (XGBoosting). It is an ensemble approach where the models are added sequentially to correct the errors until no further improvements or high performance can be made. The performance of the NIDSE is tested on the IoT network intrusion (IoT-NI) dataset. It has various types of network attacks, including host discovery, synchronized sequence number (SYN), acknowledgment (ACK), and hypertext transfer protocol (HTTP) flooding. Results from the cross-validation approach show that the XGBoosting classifier classifies the nine attacks with micro average precision of 94% and macro average precision of 85%.

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Section: Introductionmentioning

confidence: 99%

Network intrusion detection system by applying ensemble model for smart home

Amru,

Jagadeesh Kannan,

Narasimhan Ganesh

et al. 2024

IJECE

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Performance Analysis of a Privacy-Preserving Frame Sniffer on a Raspberry Pi

Silva

Mishra

Viana

et al. 2022

2022 6th Cyber Security in Networking Conference (CSNet)

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networks are an abundant data source that may serve different applications such as epidemic tracking and prevention, disaster response, crowdsensing, or ubiquitous urban services. Nevertheless, collecting and exploiting such data brings many privacy liabilities, considering that each transmitted frame has the MAC address (a unique device identifier) of the corresponding personal device, also considered sensitive information. Literature has shown that the MAC randomization performed by manufacturers of phones is not enough to protect devices' identification. Data obfuscation is a promising solution to avoid storing advertised identifiers of devices and prevent attackers from acquiring sensitive data. Obfuscating such identifiers while also being able to differentiate frames sent by different devices poses a significant challenge for frame capturing by low-resource IoT devices in real-time. This paper illustrates the impact of on-the-fly hashing as an obfuscating measure to protect people's privacy. Since no popular off-the-shelf sniffer (using wireshark or tcpdump) allows for on-the-fly hashing, we build upon the scapy library a custommade sniffer capable of hashing the required data needed to protect user privacy. We demonstrate the viability of this privacypreserving IoT sniffer on a Raspberry Pi platform.

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