Attack Pattern Recognition in the Internet of Things using Complex Event Processing and Machine Learning

Roldán-Gómez, José; Boubeta-Puig, Juan; Gómez, Juan Manuel Castelo; Carrillo-Mondéjar, Javier; Martínez, José Luis

doi:10.1109/smc52423.2021.9658711

Cited by 5 publications

(5 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Roldán‐Gómez, Boubeta‐Puig, Castelo Gómez, et al (2021) addressed aspects related to attack detection in real‐time IoT networks using CEP pattern rules. These intricate patterns typically necessitate domain experts attacks detection.…”

Section: Results Analysis and Discussionmentioning

confidence: 99%

“…While exploring various aspects of CEP systems, it has been observed that researchers have proposed various event‐driven systems (Roldán‐Gómez, Boubeta‐Puig, Gómez, et al, 2021) that work with dynamic data streams to achieve a low‐latency approach. Apart, several attempts have been made in recent times to design cloud‐based CEP systems, which is helpful for designing scalable event processing systems.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Rule based complex event processing forIoTapplications: Review, classification and challenges

Kumar,

Agarwal

2024

Expert Systems

View full text Add to dashboard Cite

In recent decades, data aggregation and correlation have emerged as a significant and challenging area of research for finding useful relationships between different features of streaming data. Similarly, complex event processing (CEP) has emerged as a vital tool for aggregating and finding patterns from streaming data such as video streaming, real‐time stock trades, sensor data and more. In this context, rule‐based classifier algorithms are widely employed to discover valuable patterns from streaming data and cause‐and‐effect relationships between events. However, streaming data is dynamic and complex in nature, and it is not feasible to update the rules manually by domain experts continuously. On the contrary, dynamic data sometimes fails to adopt rules, which leads to sub optimal CEP implementation. Rule‐based CEP systems come up with their own set of challenges, which primarily includes rules adaptability for streaming IoT data. In dynamic environment, shifting patterns in data streams can impact the performance of the CEP system, as well as the correlation between events, and finding useful patterns is very much challenging. In this article, our objective is to provide a comprehensive survey for CEP using rule‐based algorithms applied across various domain and applications. We present a broad literature review using a sequential expansion of methods used for CEP, which primarily include event producers, preprocessing of events, robust rule implementation, and decision support. Additionally, we present a detailed classification of approaches used for different applications. Finally, after applying the rigorous review, it was feasible to present the state‐of‐the‐art research, which focuses on designing robust rules, application specific insights, scalable event‐driven architectures and finding the domain's trends with significant challenges and future scope.

show abstract

Section: Results Analysis and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Rule based complex event processing forIoTapplications: Review, classification and challenges

Kumar,

Agarwal

2024

Expert Systems

View full text Add to dashboard Cite

show abstract

“…A natural evolution of the above-mentioned work is that of Rolda ´n-Go ´mez et al [27]. In this work authors the reduce the dimensions of individual events by using Principal Component Analysis (PCA), thereby achieving two goals.…”

Section: Supervised Without Prior Rulesmentioning

confidence: 99%

An automatic unsupervised complex event processing rules generation architecture for real-time IoT attacks detection

et al. 2023

Self Cite

View full text Add to dashboard Cite

In recent years, the Internet of Things (IoT) has grown rapidly, as has the number of attacks against it. Certain limitations of the paradigm, such as reduced processing capacity and limited main and secondary memory, make it necessary to develop new methods for detecting attacks in real time as it is difficulty to adapt as has the techniques used in other paradigms. In this paper, we propose an architecture capable of generating complex event processing (CEP) rules for real-time attack detection in an automatic and completely unsupervised manner. To this end, CEP technology, which makes it possible to analyze and correlate a large amount of data in real time and can be deployed in IoT environments, is integrated with principal component analysis (PCA), Gaussian mixture models (GMM) and the Mahalanobis distance. This architecture has been tested in two different experiments that simulate real attack scenarios in an IoT network. The results show that the rules generated achieved an F1 score of .9890 in detecting six different IoT attacks in real time.

show abstract

“…Roldán et al [126] integrated CEP with ML for cyber attack pattern recognition in IoT. The framework used ML method to enable automatic generation of CEP patterns from categorized or uncategorised data, depending on whether the goal is to classify attacks identify anomalies.…”

Section: Integration Of Cep and Ml/dlmentioning

confidence: 99%

Complex event processing for physical and cyber security in datacentres - recent progress, challenges and recommendations

et al. 2022

View full text Add to dashboard Cite

A datacentre stores information and manages data access in fast and reliable manner. Failure of datacentre operation is not an option and can be catastrophic. Internet of things (IoT) devices in datacentre can automate management tasks and reduce human intervention and error. IoT devices can be used to manage many datacentre routine tasks such as monitoring physical infrastructure, updating software and configuration, monitoring network traffic, and automating alerting reports to respective authorities. The physical and cyber security of the datacentre can be handled by IoT technology by intrusion detection methods. By 2025, more than 25 billion things will be connected to the internet network, therefore massive data will be generated by different heterogeneous sources, and powerful processing engines such as complex event processing (CEP) are needed to handle such a fast and continuous stream of big data. The integration of machine learning (ML) and deep learning (DL) can enhance CEP by introducing new features such as automated rule extraction and self-healing mechanism. This study aims to provide an overview of CEP, as well as its features and potential for integration with IoT applications and ML/DL techniques. We provide a review of recent research works to highlight the capability and applicability of CEP technology to monitor physical facilities and cyber security in detail. This review also highlights several issues and challenges, and provides suggestions for future research. The highlighted insights and recommendations in this paper could raise efforts toward the development of future datacentres based on CEP technology.

show abstract

Attack Pattern Recognition in the Internet of Things using Complex Event Processing and Machine Learning

Cited by 5 publications

References 17 publications

Rule based complex event processing forIoTapplications: Review, classification and challenges

Rule based complex event processing forIoTapplications: Review, classification and challenges

An automatic unsupervised complex event processing rules generation architecture for real-time IoT attacks detection

Complex event processing for physical and cyber security in datacentres - recent progress, challenges and recommendations

Contact Info

Product

Resources

About