A brief survey of machine learning methods and their sensor and IoT applications

Shanthamallu, Uday Shankar; Spanias, Andreas; Tepedelenlioğlu, Cihan; Stanley, Mike

doi:10.1109/iisa.2017.8316459

Cited by 213 publications

(101 citation statements)

References 117 publications

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“…The deployment of IoT on massive scale also brings challenges. Some of these challenges include design of networking and storage architecture for smart devices, efficient data communication protocols, proactive identification and protection of IoT from malicious attacks, standardization of technologies, and devices and application interfaces [19], [20] etc. Inter-connectivity: IoT devices are expected to be connected to global information and communication infrastructure and can be accessed from anywhere and anytime.…”

Section: A Characteristics Of Iot Networkmentioning

confidence: 99%

Machine Learning in IoT Security: Current Solutions and Future Challenges

Hussain

Hassan

et al. 2020

IEEE Commun. Surv. Tutorials

593

270

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The future Internet of Things (IoT) will have a deep economical, commercial and social impact on our lives. The participating nodes in IoT networks are usually resourceconstrained, which makes them luring targets for cyber attacks. In this regard, extensive efforts have been made to address the security and privacy issues in IoT networks primarily through traditional cryptographic approaches. However, the unique characteristics of IoT nodes render the existing solutions insufficient to encompass the entire security spectrum of the IoT networks. This is, at least in part, because of the resource constraints, heterogeneity, massive real-time data generated by the IoT devices, and the extensively dynamic behavior of the networks. Therefore, Machine Learning (ML) and Deep Learning (DL) techniques, which are able to provide embedded intelligence in the IoT devices and networks, are leveraged to cope with different security problems. In this paper, we systematically review the security requirements, attack vectors, and the current security solutions for the IoT networks. We then shed light on the gaps in these security solutions that call for ML and DL approaches. We also discuss in detail the existing ML and DL solutions for addressing different security problems in IoT networks. At last, based on the detailed investigation of the existing solutions in the literature, we discuss the future research directions for ML-and DL-based IoT security.

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Section: A Characteristics Of Iot Networkmentioning

confidence: 99%

Machine Learning in IoT Security: Current Solutions and Future Challenges

Hussain

Hassan

et al. 2020

IEEE Commun. Surv. Tutorials

593

270

View full text Add to dashboard Cite

show abstract

“…Recently, several research efforts 30,[36][37][38][39] have started to apply machine learning techniques to detect malicious traffic data and behaviors of devices running in IoT environments. Consequently, we need efficient security controls that can be automated and quickly analyse and differentiate between benign and malicious IoT data.…”

Section: Machine Learning: An Opportunity For Improving Iot Securitymentioning

confidence: 99%

“…Consequently, we need efficient security controls that can be automated and quickly analyse and differentiate between benign and malicious IoT data. Recently, several research efforts 30,[36][37][38][39] have started to apply machine learning techniques to detect malicious traffic data and behaviors of devices running in IoT environments. Several drivers are responsible for this surge in interest in machine learning techniques aimed at improving IoT security.…”

Section: Machine Learning: An Opportunity For Improving Iot Securitymentioning

confidence: 99%

Securing Internet of Things (IoT) with machine learning

Zeadally

Tsikerdekis

2019

Int J Communication

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Advances in hardware, software, communication, embedding computing technologies along with their decreasing costs and increasing performance have led to the emergence of the Internet of Things (IoT) paradigm. Today, several billions of Internet-connected devices are part of the IoT ecosystem.IoT devices have become an integral part of the information and communication technology (ICT) infrastructure that supports many of our daily activities. The security of these IoT devices has been receiving a lot of attention in recent years. Another major recent trend is the amount of data that is being produced every day which has reignited interest in technologies such as machine learning and artificial intelligence. We investigate the potential of machine learning techniques in enhancing the security of IoT devices. We focus on the deployment of supervised, unsupervised learning techniques, and reinforcement learning for both host-based and network-based security solutions in the IoT environment. Finally, we discuss some of the challenges of machine learning techniques that need to be addressed in order to effectively implement and deploy them so that they can better protect IoT devices. KEYWORDSattack, internet of things, machine learning, security

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“…We used a support-vector machine (SVM) for C-support vector classification. SVMs are linear classifiers that attempt to construct a maximum-margin hyperplane to optimally separate data into two categories to provide the best generalization capacity, and are suitable for binary classification problems such as ours [30]. We used the minutes online and elapse seconds as two features for this model (Figure 6a).…”

Section: Experiment: Proactive Ftmentioning

confidence: 99%

Providing Fault Tolerance via Complex Event Processing and Machine Learning for IoT Systems

Power

Kotonya

2019

Proceedings of the 9th International Conference on the Internet of Things

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Fault-tolerance (FT) support is a key challenge for ensuring dependable Internet of Things (IoT) systems. Many existing FT-support mechanisms in IoT are static, tightly coupled, inflexible implementations that struggle to adapt in dynamic IoT environments. This paper proposes Complex Patterns of Failure (CPoF), an approach to providing reactive and proactive FT using Complex Event Processing (CEP) and Machine Learning (ML). Error-detection strategies are defined as nondeterministic finite automata (NFA) and implemented via CEP systems. Reactive-FT support is monitored and learned from to train ML models that proactively handle imminent future occurrences of known errors. We evaluated CPoF on an indoor agriculture system with experiments that used time and error correlations to preempt battery-depletion failures. We trained predictive models to learn from reactive-FT support and provide preemptive error recovery. CCS CONCEPTS • Computer systems organization → Embedded and cyberphysical systems; Dependable and fault-tolerant systems and networks; • Computing methodologies → Machine learning.

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A brief survey of machine learning methods and their sensor and IoT applications

Cited by 213 publications

References 117 publications

Machine Learning in IoT Security: Current Solutions and Future Challenges

Machine Learning in IoT Security: Current Solutions and Future Challenges

Securing Internet of Things (IoT) with machine learning

Providing Fault Tolerance via Complex Event Processing and Machine Learning for IoT Systems

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