1,348 research outputs found
Deep-IFS:Intrusion Detection Approach for Industrial Internet of Things Traffic in Fog Environment
The extensive propagation of industrial Internet of Things (IIoT) technologies has encouraged intruders to initiate a variety of attacks that need to be identified to maintain the security of end-user data and the safety of services offered by service providers. Deep learning (DL), especially recurrent approaches, has been applied successfully to the analysis of IIoT forensics but their key challenge of recurrent DL models is that they struggle with long traffic sequences and cannot be parallelized. Multihead attention (MHA) tried to address this shortfall but failed to capture the local representation of IIoT traffic sequences. In this article, we propose a forensics-based DL model (called Deep-IFS) to identify intrusions in IIoT traffic. The model learns local representations using local gated recurrent unit (LocalGRU), and introduces an MHA layer to capture and learn global representation (i.e., long-range dependencies). A residual connection between layers is designed to prevent information loss. Another challenge facing the current IIoT forensics frameworks is their limited scalability, limiting performance in handling Big IIoT traffic data produced by IIoT devices. This challenge is addressed by deploying and training the proposed Deep-IFS in a fog computing environment. The intrusion identification becomes scalable by distributing the computation and the IIoT traffic data across worker fog nodes for training the model. The master fog node is responsible for sharing training parameters and aggregating worker node output. The aggregated classification output is subsequently passed to the cloud platform for mitigating attacks. Empirical results on the Bot-IIoT dataset demonstrate that the developed distributed Deep-IFS can effectively handle Big IIoT traffic data compared with the present centralized DL-based forensics techniques. Further, the results validate the robustness of the proposed Deep-IFS across various evaluation measures
IoT Anomaly Detection Methods and Applications: A Survey
Ongoing research on anomaly detection for the Internet of Things (IoT) is a
rapidly expanding field. This growth necessitates an examination of application
trends and current gaps. The vast majority of those publications are in areas
such as network and infrastructure security, sensor monitoring, smart home, and
smart city applications and are extending into even more sectors. Recent
advancements in the field have increased the necessity to study the many IoT
anomaly detection applications. This paper begins with a summary of the
detection methods and applications, accompanied by a discussion of the
categorization of IoT anomaly detection algorithms. We then discuss the current
publications to identify distinct application domains, examining papers chosen
based on our search criteria. The survey considers 64 papers among recent
publications published between January 2019 and July 2021. In recent
publications, we observed a shortage of IoT anomaly detection methodologies,
for example, when dealing with the integration of systems with various sensors,
data and concept drifts, and data augmentation where there is a shortage of
Ground Truth data. Finally, we discuss the present such challenges and offer
new perspectives where further research is required.Comment: 22 page
CPS Data Streams Analytics based on Machine Learning for Cloud and Fog Computing: A Survey
Cloud and Fog computing has emerged as a promising paradigm for the Internet of things (IoT) and cyber-physical systems (CPS). One characteristic of CPS is the reciprocal feedback loops between physical processes and cyber elements (computation, software and networking), which implies that data stream analytics is one of the core components of CPS. The reasons for this are: (i) it extracts the insights and the knowledge from the data streams generated by various sensors and other monitoring components embedded in the physical systems; (ii) it supports informed decision making; (iii) it enables feedback from the physical processes to the cyber counterparts; (iv) it eventually facilitates the integration of cyber and physical systems. There have been many successful applications of data streams analytics, powered by machine learning techniques, to CPS systems. Thus, it is necessary to have a survey on the particularities of the application of machine learning techniques to the CPS domain. In particular, we explore how machine learning methods should be deployed and integrated in cloud and fog architectures for better fulfilment of the requirements, e.g. mission criticality and time criticality, arising in CPS domains. To the best of our knowledge, this paper is the first to systematically study machine learning techniques for CPS data stream analytics from various perspectives, especially from a perspective that leads to the discussion and guidance of how the CPS machine learning methods should be deployed in a cloud and fog architecture
Machine Learning in IoT Security:Current Solutions and Future Challenges
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 resource-constrained, 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
Machine learning for Internet of Things data analysis: A survey
Rapid developments in hardware, software, and communication technologies have
allowed the emergence of Internet-connected sensory devices that provide
observation and data measurement from the physical world. By 2020, it is
estimated that the total number of Internet-connected devices being used will
be between 25 and 50 billion. As the numbers grow and technologies become more
mature, the volume of data published will increase. Internet-connected devices
technology, referred to as Internet of Things (IoT), continues to extend the
current Internet by providing connectivity and interaction between the physical
and cyber worlds. In addition to increased volume, the IoT generates Big Data
characterized by velocity in terms of time and location dependency, with a
variety of multiple modalities and varying data quality. Intelligent processing
and analysis of this Big Data is the key to developing smart IoT applications.
This article assesses the different machine learning methods that deal with the
challenges in IoT data by considering smart cities as the main use case. The
key contribution of this study is presentation of a taxonomy of machine
learning algorithms explaining how different techniques are applied to the data
in order to extract higher level information. The potential and challenges of
machine learning for IoT data analytics will also be discussed. A use case of
applying Support Vector Machine (SVM) on Aarhus Smart City traffic data is
presented for a more detailed exploration.Comment: Digital Communications and Networks (2017
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