A Security Situation Awareness Approach for IoT Software Chain Based on Markov Game Model

Zhu, Xudong; Deng, Honggao

doi:10.9781/ijimai.2022.08.002

Cited by 9 publications

(3 citation statements)

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“…Liu et al [12] demonstrated a fog-based privacy-preservation scheme for SG data aggregation that enables the service provider for multiple function queries on encryption of meter data. The authors of [20][21][22][23][24] have worked on IoT applications and events where Markov model has been used. However, none of them is suitable for a smart grid environment.…”

Section: Related Workmentioning

confidence: 99%

SDAFA: Secure Data Aggregation in Fog-Assisted Smart Grid Environment

et al. 2023

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The tremendous growth of about 8 billion devices connected to each other in various domains of Internet of Things (IoT)-based applications have attracted researchers from both industry and academia. IoT is a network of several devices connected with each other to provide sensing capabilities, particularly in smart grid (SG) environment. Various challenges such as the efficient handling of massive IoT data can be addressed with advances in fog computing. The secure data aggregation challenge is one such issue in IoT-based smart grid systems, which include millions of smart meters. Typical SG-based data aggregation approaches have high computation and communication costs, however, many efforts have been made to overcome these limitations while leveraging fog computing but no satisfactory results have been obtained. Moreover, existing solutions also suffer from high storage requirements. The traditional data aggregation schemes such as GCEDA (Grouping of Clusters for Efficient Data Aggregation) and SPPDA (Secure Privacy-Preserving Data Aggregation) also suffer from a few shortcomings. SPPDA follows a mixed aggregation architecture that includes trees and clusters which can lead to some performance complexities and is not energy-efficient, whereas GCEDA does not support heterogeneity. To overcome these problems, this research provides a fog-assisted strategy for secure and efficient data aggregation in smart grid. The concept of smart grid is implemented in fog environment, which was not the case in previous schemes. We used communication between smart meters (SMs) and fog nodes (FNs) to transmit confidential data in compressed form towards FN. The FN further aggregates the received data which can then be updated in cloud repositories later. We presented two algorithms—data aggregation and data extraction at FN and cloud, respectively, to achieve secure communication. The performance of the proposed strategy has been evaluated against existing data aggregation techniques GCEDA and SPPDA for various performance parameters such as storage, communication cost and transmission cost. The proposed scheme overcomes the limitation of heterogeneity and mixed aggregation which was faced in GCEDA and SPPDA and the results revealed outstanding performance in comparison with both, so the proposed solution can be used in a smart grid environment for efficient and secure data transmission.

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Section: Related Workmentioning

confidence: 99%

SDAFA: Secure Data Aggregation in Fog-Assisted Smart Grid Environment

et al. 2023

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“…This can be done by deploying a botnet, or network of infected devices, to flood the target device or network with a lot of traffic, making it unavailable. Security techniques like firewalls, IDS, and traffic filtering can be used to detect and prevent malicious traffic from defending against DoS attacks in IoT [6] [7] [8].…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Machine Learning Classifiers for Predicting Denial-of-Service Attack in Internet of Things

Almomani,

Alsaaidah,

Shareha

et al. 2024

IJACSA

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Eliminating security threats on the Internet of Things (IoT) requires recognizing threat attacks. IoT and its implementations are currently the most common scientific field. When it comes to real-world implementations, IoT's attributes, on the one hand, make it simple to apply, but on the other hand, they expose it to cyber-attacks. Denial of Service (DoS) attack is a type of threat that is now widespread in the field of IoT. Its primary goal is to stop or damage service or capability on a target. Conventional Intrusion Detection Systems (IDS) are no longer sufficient for detecting these sophisticated attacks with unpredictable behaviors. Machine learning (ML)--based intrusion detection does not need a massive list of expected activities or a variety of threat signatures to create detection rules. This study aims to evaluate different ML classifiers for network intrusion detection that focus on DoS attacks in the IoT environment to determine the best ML classifier that can detect the DoS attack. The XGBoost, Decision Tree (DT), Gaussian Naive Bayes (NB), Random Forest (RF), Logistic Regression (LR), and Support Vector Machine (SVM) ML classifiers are used to evaluate the DoS attack. The UNSW-NB15 dataset was used for this study. The obtained accuracy rate for

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“…The optimisation of feature vectors mainly concerns their form and length, i.e., features that do not affect the accuracy of the operation are cyclically evaluated and removed. Reducing the size of the feature vectors speeds up the processing and therefore the response time [16][17][18][19].…”

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confidence: 99%

Autonomous Threat Response at the Edge Processing Level in the Industrial Internet of Things

Czeczot,

Rojek,

Mikołajewski

2024

Electronics

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Industrial Internet of Things (IIoT) technology, as a subset of the Internet of Things (IoT) in the concept of Industry 4.0 and, in the future, 5.0, will face the challenge of streamlining the way huge amounts of data are processed by the modules that collect the data and those that analyse the data. Given the key features of these analytics, such as reducing the cost of building massive data centres and finding the most efficient way to process data flowing from hundreds of nodes simultaneously, intermediary devices are increasingly being used in this process. Fog and edge devices are hardware devices designed to pre-analyse terabytes of data in a stream and decide in realtime which data to send for final analysis, without having to send the data to a central processing unit in huge local data centres or to an expensive cloud. As the number of nodes sending data for analysis via collection and processing devices increases, so does the risk of data streams being intercepted. There is also an increased risk of attacks on this sensitive infrastructure. Maintaining the integrity of this infrastructure is important, and the ability to analyse all data is a resource that must be protected. The aim of this paper is to address the problem of autonomous threat detection and response at the interface of sensors, edge devices, cloud devices with historical data, and finally during the data collection process in data centres. Ultimately, we would like to present a machine learning algorithm with reinforcements adapted to detect threats and immediately isolate infected nests.

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A Security Situation Awareness Approach for IoT Software Chain Based on Markov Game Model

Cited by 9 publications

References 0 publications

SDAFA: Secure Data Aggregation in Fog-Assisted Smart Grid Environment

SDAFA: Secure Data Aggregation in Fog-Assisted Smart Grid Environment

Performance Evaluation of Machine Learning Classifiers for Predicting Denial-of-Service Attack in Internet of Things

Autonomous Threat Response at the Edge Processing Level in the Industrial Internet of Things

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