Detection of anomalies in data for monitoring of security components in the Internet of Things

Desnitsky, Vasily; Kotenko, Igor; Nogin, S. B.

doi:10.1109/scm.2015.7190452

Cited by 28 publications

(15 citation statements)

References 5 publications

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“…When comparing energy consumption, however, the study found that it was possible to save up to 6000 mJ of energy when running the lightweight technique, which represents a worthwhile energy saving given the low-resource nature of IoT. Desnitsky et al (2015) proposed a method for detecting anomalies in IoT applications using domain-specific knowledge to create a list of constraints for the application. For example, the temperature in a home should not exceed 30 degrees Celsius, or the constraints could be drawn from the history of the data, for example a motion sensor in an office stops providing data.…”

Section: Anomaly Detectionmentioning

confidence: 99%

IoT reliability: a review leading to 5 key research directions

Moore¹,

Nugent²,

Zhang³

et al. 2020

CCF Trans. Pervasive Comp. Interact.

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The Internet of Things (IoT) is rapidly changing the way in which we engage with technology on a daily basis. The IoT paradigm enables low-resource devices to intercommunicate in a fully flexible and pervasive manner, and the data from these devices is used for decision-making in critical applications such as; traffic infrastructure, health-care and home security, to name but a few. Due to the scarce resources available in these IoT devices, being able to quantify the reliability of them is a critical function. This report presents a detailed evolution of the area of reliability measurement, followed by an in-depth review of the state-of-the-art for quantification of reliability in the IoT, revealing the many challenges associated with this task. From this in-depth review, a set of key research directions for IoT reliability is determined. Despite the critical nature of the research area, at this current moment, this study is the first detailed review available in the area of assessing IoT reliability.

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Section: Anomaly Detectionmentioning

confidence: 99%

IoT reliability: a review leading to 5 key research directions

Moore¹,

Nugent²,

Zhang³

et al. 2020

CCF Trans. Pervasive Comp. Interact.

View full text Add to dashboard Cite

show abstract

“…Therefore, researchers from academia and industry are analysing the security aspects of IoT systems [48]. Current research directions are mainly focusing on anomaly detection for security components in IoT systems [16,46,60] and penetration testing for IoT systems [12]. However, with the rapid growth of the IoT/IIoT systems, security analysis and manual security testing of an IoT system can often not cope with large scale IoT networks.…”

Section: Research Gap and Related Workmentioning

confidence: 99%

Requirements and Recommendations for IoT/IIoT Models to automate Security Assurance through Threat Modelling, Security Analysis and Penetration Testing

Ankele

Marksteiner

Nahrgang

et al. 2019

Proceedings of the 14th International Conference on Availability, Reliability and Security

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The factories of the future require efficient interconnection of their physical machines into the cyber space to cope with the emerging need of an increased uptime of machines, higher performance rates, an improved level of productivity and a collective collaboration along the supply chain. With the rapid growth of the Internet of Things (IoT), and its application in industrial areas, the so called Industrial Internet of Things (IIoT)/Industry 4.0 emerged. However, further to the rapid growth of IoT/IIoT systems, cyber attacks are an emerging threat and simple manual security testing can often not cope with the scale of large IoT/IIoT networks.In this paper, we suggest to extract metadata from commonly used diagrams and models in a typical software development process, to automate the process of threat modelling, security analysis and penetration testing, without detailed prior security knowledge.In that context, we present requirements and recommendations for metadata in IoT/IIoT models that are needed as necessary input parameters of security assurance tools.

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“…Today, it is commonly observed that trust status is never static, but may change to another, which may be initiated by changes of surrounding services and environments. Dynamic trust status transitions have been analyzed in a number of scenarios including multiple assurance levels [1], dynamic trust level elevation [18], creation of trust by Self-Issued IdP [19], reflection of security monitoring [20], [21] and continuous authentication by behavior monitoring and analysis [22], [23].…”

Section: Related Workmentioning

confidence: 99%

Elastic Trust Model for Dynamically Evolving Trust Frameworks

Satō

Yamamoto

2019

IEICE Trans. Inf. & Syst.

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Today, trust plays a central role in services in distributed environments. Conventionally deployed trust has been based on static framework in which a server responds to a service request under statically determined policies. However, in accordance with evolution of distributed environments empowered with IoT and federated access mechanisms, dynamic behavior must be analyzed and taken into service provision, which conventional trust cannot properly handle. In this paper, we propose an extension of PDP (Policy Decision Point) in which assertions together with service requests are evaluated. Furthermore, the evaluation may be dynamically configured in dynamically evolving trust environment. We propose an elastic trust model in view of dynamic trust environment. This enables intuitionistic modeling of typical concrete elastic distributed services.

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Detection of anomalies in data for monitoring of security components in the Internet of Things

Cited by 28 publications

References 5 publications

IoT reliability: a review leading to 5 key research directions

IoT reliability: a review leading to 5 key research directions

Requirements and Recommendations for IoT/IIoT Models to automate Security Assurance through Threat Modelling, Security Analysis and Penetration Testing

Elastic Trust Model for Dynamically Evolving Trust Frameworks

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