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

Alaghbari, Khaled A.; Saad, Mohamad Hanif Md; Hussain, Aini; Alam, Muhammad Raisul

doi:10.1186/s13677-022-00338-x

Cited by 10 publications

(3 citation statements)

References 114 publications

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“…However, these protocols also introduce some communication costs and may require some level of coordination between untrusting parties. [18]- [20] Zero-knowledge proofs are cryptographic procedures which let one party to prove to the other party that he knows particular knowledge without need to reveal the knowledge items. It allows performing authentication and verification procedures without the need to transmit any sensitive data.…”

Section: Literature Reviewmentioning

confidence: 99%

Advanced Privacy-Preserving Framework for Enhancing Fog Computing to Secure IoT Data Stream

Prabhat Kumar

2024

jes

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The proposed privacy-preserving framework based on fog computing for securing IoT data was examined through 10 experiment trials, with each trial dissecting a number of performance related metrics. Specifically, across the trials, latency values varied between 45 and 55 milliseconds, which signified that communication overhead was minute and that data were processed efficiently. Throughput values also varied considerably, yet only between 95 and 110 megabits per second, which signalled that the framework would allow processing data at high speeds. The rates of resource utilization measured in terms of MHR from CPU and Mused in the memory of specific fog nodes, varied between 58% and 76% . Regarding the scalability of the proposed framework, it was assessed based on the data collected and divided into the corresponding categories. From the energy consumption analysis, the values varied between 470 and 530 joules , which was recognized as the change caused by the shifting performance of the proposed IoT solution. Finally, communication overhead values varied from 970 to 1050 bytes, showing the differences in the effects which privacy-preserving frameworks have on data transmission. In conclusion, the results indicate that the proposed complex is immensely efficient in terms of protecting sensitive IoT data, ensuring a high level of security, preserving privacy, maintaining the current performance, and being adjusted to the new threats and security challenges.

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Section: Literature Reviewmentioning

confidence: 99%

Advanced Privacy-Preserving Framework for Enhancing Fog Computing to Secure IoT Data Stream

Prabhat Kumar

2024

jes

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“…A rules engine is adopted by many IoT edge frameworks to enable the autonomous and dynamic operation of various service scenarios in the IoT edge platforms [20][21][22][23]. As a rule-based system, the CEP filters incoming events from sensors to trigger actions according to the defined rules.…”

Section: Related Workmentioning

confidence: 99%

Transparent Rule Enablement Based on Commonization Approach in Heterogeneous IoT Edge Networks

Jin,

Hong,

Song

et al. 2023

Sensors

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The paradigm of the Internet of Things (IoT) and edge computing brings a number of heterogeneous devices to the network edge for monitoring and controlling the environment. For reacting to events dynamically and automatically in the environment, rule-enabled IoT edge platforms operate the deployed service scenarios at the network edge, based on filtering events to perform control actions. However, due to the heterogeneity of the IoT edge networks, deploying a consistent rule context for operating a consistent rule scenario on multiple heterogeneous IoT edge platforms is difficult because of the difference in protocols and data formats. In this paper, we propose a transparent rule enablement, based on the commonization approach, for enabling a consistent rule scenario in heterogeneous IoT edge networks. The proposed IoT Edge Rule Agent Platform (IERAP) deploys device proxies to share consistent rules with IoT edge platforms without considering the difference in protocols and data formats. Therefore, each device proxy only considers the translation of the corresponding platform-specific and common formats. Also, the rules are deployed by the corresponding device proxy, which enables rules to be deployed to heterogeneous IoT edge platforms to perform the consistent rule scenario without considering the format and underlying protocols of the destination platform.

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“…Bashlite (also called Gafgyt) and its successor Mirai are examples of botnets that have recently caused large-scale DDoS attacks by exploiting IoT devices. Due to the open source codes of these botnets, attackers managed to create many novel variations [3,4].…”

Section: Introductionmentioning

confidence: 99%

Deep Autoencoder-Based Integrated Model for Anomaly Detection and Efficient Feature Extraction in IoT Networks

Alaghbari,

Lim,

Saad

et al. 2023

IoT

Self Cite

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The intrusion detection system (IDS) is a promising technology for ensuring security against cyber-attacks in internet-of-things networks. In conventional IDS, anomaly detection and feature extraction are performed by two different models. In this paper, we propose a new integrated model based on deep autoencoder (AE) for anomaly detection and feature extraction. Firstly, AE is trained based on normal network traffic and used later to detect anomalies. Then, the trained AE model is employed again to extract useful low-dimensional features for anomalous data without the need for a feature extraction training stage, which is required by other methods such as principal components analysis (PCA) and linear discriminant analysis (LDA). After that, the extracted features are used by a machine learning (ML) or deep learning (DL) classifier to determine the type of attack (multi-classification). The performance of the proposed unified approach was evaluated on real IoT datasets called N-BaIoT and MQTTset, which contain normal and malicious network traffics. The proposed AE was compared with other popular anomaly detection techniques such as one-class support vector machine (OC-SVM) and isolation forest (iForest), in terms of performance metrics (accuracy, precision, recall, and F1-score), and execution time. AE was found to identify attacks better than OC-SVM and iForest with fast detection time. The proposed feature extraction method aims to reduce the computation complexity while maintaining the performance metrics of the multi-classifier models as much as possible compared to their counterparts. We tested the model with different ML/DL classifiers such as decision tree, random forest, deep neural network (DNN), conventional neural network (CNN), and hybrid CNN with long short-term memory (LSTM). The experiment results showed the capability of the proposed model to simultaneously detect anomalous events and reduce the dimensionality of the data.

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

Cited by 10 publications

References 114 publications

Advanced Privacy-Preserving Framework for Enhancing Fog Computing to Secure IoT Data Stream

Advanced Privacy-Preserving Framework for Enhancing Fog Computing to Secure IoT Data Stream

Transparent Rule Enablement Based on Commonization Approach in Heterogeneous IoT Edge Networks

Deep Autoencoder-Based Integrated Model for Anomaly Detection and Efficient Feature Extraction in IoT Networks

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