A Lightweight Cyber-Security Defense Framework for Smart Homes

Spanos, Georgios; Giannoutakis, Konstantinos M.; Votis, Konstantinos; Viano, Brais; Augusto-Gonzalez, Javier; Aivatoglou, Georgios; Tzovaras, Dimitrios

doi:10.1109/inista49547.2020.9194689

Cited by 9 publications

(6 citation statements)

References 26 publications

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“…For example, Gajewski et al [101] proposed a two-tier intrusion detection mechanism that used machine learning to combine anomaly detection at local level in each home combined with global anomaly detection across homes conducted by the network service provider. Likewise, deep learning approaches to detect IoT device anomalies have been proposed [102][103][104][105]. Similarly, Pan et al [106] implemented a context aware intrusion detection framework that could accurately find and classify various kinds of Building Automation and Control Networking protocol (BacNet) attacks.…”

Section: ) Rq1: Smart Home Device Securitymentioning

confidence: 99%

Ranking Security of IoT-Based Smart Home Consumer Devices

AlLifah

Zualkernan

2022

IEEE Access

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Smart home consumer devices like home theatres, music players, voice-based assistants, smart lighting, and security cameras have widely adopted the Internet of Things (IoT). These devices pose a significant security risk to consumers because the devices are exposed to mobile applications and cloud-based services with known security vulnerabilities. IoT technologies also introduce additional vulnerabilities specific to smart home architectures. Most current home consumer devices provide little or no information about the level of security they afford. Since most consumers are not tech-savvy, it is currently difficult for a consumer to make an informed decision about which consumer device model (e.g., which smart television model?) has the best security. Hence, consumers need an objective security ranking of each type (e.g., security cameras) of home consumer devices. This paper proposes a novel methodology to systematically build such security rankings for home consumer devices. The proposed methodology can be applied by utilizing data from any security assessment study. The paper discusses previous efforts in applying Analytic Hierarchy Process (AHP) to rank security risks in general. The paper also presents a systematic survey of security vulnerabilities of smart home consumer devices when viewed from an IoT lens. Using the proposed methodology, a case study where an AHP model for ranking commonly used home consumer devices including home theatres, security cameras, smart lighting, smart speakers, video surveillance, smart switches, home automation systems, home security systems, smart routers, wireless doorbell cameras, and home audio systems, was developed. Relative security rankings for each type of consumer device were derived from the AHP model. According to the AHP model, network security was the primary driver of smart home device security with a priority of 0.6893 while application security had the least priority of 0.0591. Critical Vulnerabilities were the most important for device security (priority=0.4397), Man-in-The-Middle attacks for network security (priority=0.2019), exploitable services for cloud security (priority=0.26), and sensitive data for application security (0.7626). The AHP model was internally consistent (Consistency Ratio < 0.1). Sensitivity analysis showed that the AHP model was robust against pairing assumptions.INDEX TERMS AHP, consumer applications, cloud computing, home consumer devices, Internet of Things, network security, smart home.

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Section: ) Rq1: Smart Home Device Securitymentioning

confidence: 99%

Ranking Security of IoT-Based Smart Home Consumer Devices

AlLifah

Zualkernan

2022

IEEE Access

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“…To the best of our knowledge, this is the first smart-home traffic data-set containing data from multiple various wireless protocols on the same real-world installation. The compiled GHOST-IoT-data-set enabled the testing and evaluation of the GHOST modules which deal with anomaly detection and identification of abnormal incidents within a smart-home ecosystem [ 26 ]. Furthermore, it can be used for the validation of techniques focusing on the analysis of users’ normal and abnormal behavior, for anomaly detection of network traffic, detection of specific to IoT attacks, such as physical access attacks, and similar research efforts.…”

Section: Discussionmentioning

confidence: 99%

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

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Viano³

et al. 2020

Sensors

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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.

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“…5 depicts the tested IoT system. For example, Agmon et al [13], Sachidananda et al [41], and Spanos et al [42] use the number of hosts as IVs; however, the number of exploits and coverage discovered for each host is the focus of the measurement.…”

Section: Iot Systems Testedmentioning

confidence: 99%

“…Spanos et al [42] proposed a mechanism for anomaly detection by combining ML and statistical methodologies to detect network traffic time series risks. The framework is a lightweight cybersecurity solution for IoT-based edge computing.…”

Section: Vulnerability Detection and Response Formulationmentioning

confidence: 99%

A Review on Attack Graph Analysis for IoT Vulnerability Assessment: Challenges, Open Issues, and Future Directions

et al. 2023

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Vulnerability assessment in industrial IoT networks is critical due to the evolving nature of the domain and the increasing complexity of security threats. This study aims to address the existing gaps in the literature by conducting a comprehensive survey on the use of attack graphs for vulnerability assessment in IoT networks. Attack graphs serve as a valuable cybersecurity tool for modeling and analyzing potential attack scenarios on systems, networks, or applications. The survey covers the research conducted between 2016 and 2021(34 peer-reviewed journal articles and 28 conference papers), identifying and categorizing the main methodologies and technologies employed in generating and analyzing attack graphs. In this review, core modeling techniques for IoT vulnerability assessment are highlighted, such as Markov Decision Processes (MDP), Feature Pyramid Networks (FPN), K-means clustering, and logistic regression models, along with other techniques involving genetic algorithms like fast-forward (FF), contingent fast-forwards (CFF), advanced reinforcement-learning algorithms, and HARMs models. The evaluation of the performance of these attack graph models using IoT networks or devices as case studies is also emphasized. This survey provides valuable insights into the state-of-the-art attack graph techniques for IoT network vulnerability assessment, identifying various applications, performances, research opportunities, and challenges. As a reference source, it serves to inform academicians and practitioners interested in leveraging attack graphs for IoT network vulnerability assessment and guides future research directions in this area.INDEX TERMS Attack graph, the Internet of Things, network vulnerabilities, vulnerability assessment.

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A Lightweight Cyber-Security Defense Framework for Smart Homes

Cited by 9 publications

References 26 publications

Ranking Security of IoT-Based Smart Home Consumer Devices

Ranking Security of IoT-Based Smart Home Consumer Devices

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

A Review on Attack Graph Analysis for IoT Vulnerability Assessment: Challenges, Open Issues, and Future Directions

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