Securing Smart Homes via Software-Defined Networking and Low-Cost Traffic Classification

Gordon, Holden; Batula, Christopher; Tushir, Bhagyashri; Dezfouli, Behnam; Liu, Yuhong

doi:10.1109/compsac51774.2021.00143

Cited by 20 publications

(8 citation statements)

References 29 publications

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“…Traffic classification as a knowledge generation step by collecting flow features such as protocol percentage, packet count, packet size, IP diversity ratio, etc. using a K-nearest neighbor has been realized in [144], in order to classify different IoT devices and detect different DDoS attacks such as TCP-SYN, UDP, and ICMP.…”

Section: Generating Knowledge Using Machine Learning Methodsmentioning

confidence: 99%

A Comprehensive Survey on Knowledge-Defined Networking

Wijesekara

Gunawardena

2023

Telecom

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Traditional networking is hardware-based, having the control plane coupled with the data plane. Software-Defined Networking (SDN), which has a logically centralized control plane, has been introduced to increase the programmability and flexibility of networks. Knowledge-Defined Networking (KDN) is an advanced version of SDN that takes one step forward by decoupling the management plane from control logic and introducing a new plane, called a knowledge plane, decoupled from control logic for generating knowledge based on data collected from the network. KDN is the next-generation architecture for self-learning, self-organizing, and self-evolving networks with high automation and intelligence. Even though KDN was introduced about two decades ago, it had not gained much attention among researchers until recently. The reasons for delayed recognition could be due to the technology gap and difficulty in direct transformation from traditional networks to KDN. Communication networks around the globe have already begun to transform from SDNs into KDNs. Machine learning models are typically used to generate knowledge using the data collected from network devices and sensors, where the generated knowledge may be further composed to create knowledge ontologies that can be used in generating rules, where rules and/or knowledge can be provided to the control, management, and application planes for use in decision-making processes, for network monitoring and configuration, and for dynamic adjustment of network policies, respectively. Among the numerous advantages that KDN brings compared to SDN, enhanced automation and intelligence, higher flexibility, and improved security stand tall. However, KDN also has a set of challenges, such as reliance on large quantities of high-quality data, difficulty in integration with legacy networks, the high cost of upgrading to KDN, etc. In this survey, we first present an overview of the KDN architecture and then discuss each plane of the KDN in detail, such as sub-planes and interfaces, functions of each plane, existing standards and protocols, different models of the planes, etc., with respect to examples from the existing literature. Existing works are qualitatively reviewed and assessed by grouping them into categories and assessing the individual performance of the literature where possible. We further compare and contrast traditional networks and SDN against KDN. Finally, we discuss the benefits, challenges, design guidelines, and ongoing research of KDNs. Design guidelines and recommendations are provided so that identified challenges can be mitigated. Therefore, this survey is a comprehensive review of architecture, operation, applications, and existing works of knowledge-defined networks.

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Section: Generating Knowledge Using Machine Learning Methodsmentioning

confidence: 99%

A Comprehensive Survey on Knowledge-Defined Networking

Wijesekara

Gunawardena

2023

Telecom

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“…Numerous studies have been conducted to investigate these concerns and develop solid precautions. Here, we provide a comprehensive overview of the current state of research on the topic of smart home IoT device security [ 27 , 28 , 29 ], discussing the most pivotal works and their impacts on the field. In this section, we discuss earlier approaches related to face recognition, highlight their strengths and limitations, and provide references to support our analysis.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Smart Home Security: Anomaly Detection and Face Recognition in Smart Home IoT Devices Using Logit-Boosted CNN Models

Rahim

Zhong

Ahmad

et al. 2023

Sensors

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Internet of Things (IoT) devices for the home have made a lot of people’s lives better, but their popularity has also raised privacy and safety concerns. This study explores the application of deep learning models for anomaly detection and face recognition in IoT devices within the context of smart homes. Six models, namely, LR-XGB-CNN, LR-GBC-CNN, LR-CBC-CNN, LR-HGBC-CNN, LR-ABC-CNN, and LR-LGBM-CNN, were proposed and evaluated for their performance. The models were trained and tested on labeled datasets of sensor readings and face images, using a range of performance metrics to assess their effectiveness. Performance evaluations were conducted for each of the proposed models, revealing their strengths and areas for improvement. Comparative analysis of the models showed that the LR-HGBC-CNN model consistently outperformed the others in both anomaly detection and face recognition tasks, achieving high accuracy, precision, recall, F1 score, and AUC-ROC values. For anomaly detection, the LR-HGBC-CNN model achieved an accuracy of 94%, a precision of 91%, a recall of 96%, an F1 score of 93%, and an AUC-ROC of 0.96. In face recognition, the LR-HGBC-CNN model demonstrated an accuracy of 88%, precision of 86%, recall of 90%, F1 score of 88%, and an AUC-ROC of 0.92. The models exhibited promising capabilities in detecting anomalies, recognizing faces, and integrating these functionalities within smart home IoT devices. The study’s findings underscore the potential of deep learning approaches for enhancing security and privacy in smart homes. However, further research is warranted to evaluate the models’ generalizability, explore advanced techniques such as transfer learning and hybrid methods, investigate privacy-preserving mechanisms, and address deployment challenges.

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“…This can include adjusting the thermostat, turning lights on and off, and controlling appliances such as washing machines and dryers. • Home security: smart home devices such as security cameras, door locks, and motion sensors can be used to enhance home security [144]. These devices can be linked to a home automation system, allowing homeowners to monitor their homes remotely and receive alerts for suspicious activity.…”

Section: Smart Homementioning

confidence: 99%

Integrating Edge Computing and Software Defined Networking in Internet of Things: A Systematic Review

Kamarudin,

Ameedeen,

Ab Razak

et al. 2023

ijcsm

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The Internet of Things (IoT) has transformed our interaction with the world by connecting devices, sensors, and systems to the Internet, enabling real-time monitoring, control, and automation in various applications such as smart cities, healthcare, transportation, homes, and grids. However, challenges related to latency, privacy, and bandwidth have arisen due to the massive influx of data generated by IoT devices and the limitations of traditional cloud-based architectures. Moreover, network management, interoperability, security, and scalability issues have emerged due to the rapid growth and heterogeneous nature of IoT devices. To overcome such problems, researchers proposed a new architecture called Software Defined Networking for Edge Computing in the Internet of Things (SDN-EC-IoT), which combines Edge Computing for the Internet of Things (EC-IoT) and Software Defined Internet of Things (SDIoT). Although researchers have studied EC-IoT and SDIoT as individual architectures, they have not yet addressed the combination of both, creating a significant gap in our understanding of SDN-EC-IoT. This paper aims to fill this gap by presenting a comprehensive review of how the SDN-EC-IoT paradigm can solve IoT challenges. To achieve this goal, this study conducted a literature review covering 74 articles published between 2019 and 2023. Finally, this paper identifies future research directions for SDN-EC-IoT, including the development of interoperability platforms, scalable architectures, low latency and Quality of Service (QoS) guarantees, efficient handling of big data, enhanced security and privacy, optimized energy consumption, resource-aware task offloading, and incorporation of machine learning.

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Securing Smart Homes via Software-Defined Networking and Low-Cost Traffic Classification

Cited by 20 publications

References 29 publications

A Comprehensive Survey on Knowledge-Defined Networking

A Comprehensive Survey on Knowledge-Defined Networking

Enhancing Smart Home Security: Anomaly Detection and Face Recognition in Smart Home IoT Devices Using Logit-Boosted CNN Models

Integrating Edge Computing and Software Defined Networking in Internet of Things: A Systematic Review

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