Evolutionary Game for Confidentiality in IoT-Enabled Smart Grids

Boudko, Svetlana; Aursand, Peder; Abie, Habtamu

doi:10.3390/info11120582

Cited by 7 publications

(3 citation statements)

References 46 publications

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“…Furthermore, researchers have used the theoretical framework of the Nash equilibrium [41] to identify optimal techniques for distributing resources in intrusion detection [42,43]. In addition, evolutionary game theory has been used to analyze the concurrent development of methods employed by attackers and defenders in IoT systems [44,45]. These significant findings emphasize the significance of game theory as a vital tool in advancing IoT-based IDS.…”

Section: Game-theoretical Perspective On Ids For Iotmentioning

confidence: 99%

Enhanced Network Intrusion Detection System for Internet of Things Security Using Multimodal Big Data Representation with Transfer Learning and Game Theory

Ullah,

Turab,

Ullah

et al. 2024

Sensors

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Internet of Things (IoT) applications and resources are highly vulnerable to flood attacks, including Distributed Denial of Service (DDoS) attacks. These attacks overwhelm the targeted device with numerous network packets, making its resources inaccessible to authorized users. Such attacks may comprise attack references, attack types, sub-categories, host information, malicious scripts, etc. These details assist security professionals in identifying weaknesses, tailoring defense measures, and responding rapidly to possible threats, thereby improving the overall security posture of IoT devices. Developing an intelligent Intrusion Detection System (IDS) is highly complex due to its numerous network features. This study presents an improved IDS for IoT security that employs multimodal big data representation and transfer learning. First, the Packet Capture (PCAP) files are crawled to retrieve the necessary attacks and bytes. Second, Spark-based big data optimization algorithms handle huge volumes of data. Second, a transfer learning approach such as word2vec retrieves semantically-based observed features. Third, an algorithm is developed to convert network bytes into images, and texture features are extracted by configuring an attention-based Residual Network (ResNet). Finally, the trained text and texture features are combined and used as multimodal features to classify various attacks. The proposed method is thoroughly evaluated on three widely used IoT-based datasets: CIC-IoT 2022, CIC-IoT 2023, and Edge-IIoT. The proposed method achieves excellent classification performance, with an accuracy of 98.2%. In addition, we present a game theory-based process to validate the proposed approach formally.

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Section: Game-theoretical Perspective On Ids For Iotmentioning

confidence: 99%

Enhanced Network Intrusion Detection System for Internet of Things Security Using Multimodal Big Data Representation with Transfer Learning and Game Theory

Ullah,

Turab,

Ullah

et al. 2024

Sensors

View full text Add to dashboard Cite

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“…Smart Grid Interoperability Standards [4] Interconnection between heterogeneous cyber-physical systems [5] Blockchain technology for smart grids [6] ML models for Electricity Theft Detection [7] Load frequency control of smart grids [8] Confidentiality in Smart Grids [9] Metering and data access infrastructures in smart grid [10] Policy enforcement in smart grid [11] Access control for smart grid services based on publish/subscribe [12] Blockchain-based authorization system [13] Proposed work…”

Section: Data Integrity Interoperabilitymentioning

confidence: 99%

“…[8] improves the security of existing SCADA systems within smart grids using a cyber-physical digital signature scheme. In [9], Advanced Metering Infrastructure (AMI), which is an important component of an IoT based Smart Grid is analyzed separately and secured based on evolutionary game theory. Ref.…”

Section: Security Authorization Approachesmentioning

confidence: 99%

SAMGRID: Security Authorization and Monitoring Module Based on SealedGRID Platform

Suciu¹,

Farao

Bernardinetti

et al. 2022

Sensors

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IoT devices present an ever-growing domain with multiple applicability. This technology has favored and still favors many areas by creating critical infrastructures that are as profitable as possible. This paper presents a hierarchical architecture composed of different licensing entities that manage access to different resources within a network infrastructure. They are conducted on the basis of well-drawn policy rules. At the same time, the security side of these resources is also placed through a context awareness module. Together with this technology, IoT is used and Blockchain is enabled (for network consolidation, as well as the transparency with which to monitor the platform). The ultimate goal is to implement a secure and scalable security platform for the Smart Grid. The paper presents the work undertaken in the SealedGRID project and the steps taken for implementing security policies specifically tailored to the Smart Grid, based on advanced concepts such as Opinion Dynamics and Smart Grid-related Attribute-based Access Control.

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