Security threat model under internet of things using deep learning and edge analysis of cyberspace governance

Li, Zhi; Ge, Yuemeng; Guo, Jieying; Chen, Mengyao; Wang, Junwei

doi:10.1007/s13198-021-01533-w

Cited by 6 publications

(2 citation statements)

References 22 publications

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“…Rather than directly targeting a specific organization, supply chain attacks compromise trusted software or hardware suppliers to gain unauthorized access to their customers' systems. This approach can have widespread and far-reaching impacts [47].…”

Section: Figure 1 Classifications Of Cyber-attacks [44]mentioning

confidence: 99%

Machine learning in cybersecurity: A review of threat detection and defense mechanisms

Ugochukwu Ikechukwu Okoli,

Ogugua Chimezie Obi,

Adebunmi Okechukwu Adewusi

et al. 2024

World J. Adv. Res. Rev.

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The cybersecurity concerns get increasingly intricate as the digital world progresses. In light of the increasing complexity of cyber threats, it is imperative to develop and implement advanced and flexible security strategies. Machine Learning (ML) has become a potent tool in strengthening cybersecurity, providing the capacity to scrutinise extensive information, recognise trends, and improve threat detection and defence methods. This paper examines the significance of ML in the field of cybersecurity, with a special emphasis on the identification of threats and the implementation of protective measures. By incorporating ML algorithms into cybersecurity frameworks, organisations may automate decision-making processes, facilitating prompt responses to ever-changing threats. The initial segment explores the terrain of cyber threats, highlighting the necessity for dynamic and aggressive security methods. Conventional solutions that rely on signatures are frequently inadequate when it comes to handling sophisticated, shape-shifting attacks. ML algorithms, in contrast, have exceptional proficiency in identifying nuanced patterns and irregularities within extensive datasets, therefore offering a more efficient method of detecting potential threats. The second section delves into several ML methodologies utilised in cybersecurity, including supervised and unsupervised learning, deep learning, and reinforcement learning. Every approach is assessed based on its suitability for threat detection, demonstrating its advantages and constraints. Furthermore, the relevance of feature engineering and data pretreatment in improving machine learning models for cybersecurity applications. The versatility of ML algorithms allows them to grow with emerging threats, making them a useful tool in the ever-changing arena of cyber warfare. The final segment focuses on real-world applications of machine learning in cybersecurity, presenting successful use cases across sectors. From anomaly detection to behavior analysis, ML algorithms contribute to the discovery of dangerous activity, lowering false positives and strengthening the overall security posture. Lastly, the paper covers the obstacles and ethical issues related to the adoption of ML in cybersecurity. Issues like as adversarial assaults, skewed datasets, and the interpretability of ML models are examined, highlighting the necessity for a holistic strategy that integrates modern technology with ethical considerations. The fusion of human expertise and machine intelligence offers a formidable defense against evolving cyber threats, paving the way for a more resilient and secure digital future.

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Section: Figure 1 Classifications Of Cyber-attacks [44]mentioning

confidence: 99%

Machine learning in cybersecurity: A review of threat detection and defense mechanisms

Ugochukwu Ikechukwu Okoli,

Ogugua Chimezie Obi,

Adebunmi Okechukwu Adewusi

et al. 2024

World J. Adv. Res. Rev.

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“…As a result, researchers have been searching for solutions to address fairness in networks. Li et al proposed an IoT security threat model that uses deep learning to create a fair environment by autonomously learning from attacks [14]. Additionally, Hu et al discussed the vulnerability of trust relationships between network nodes and introduced a blockchain-based software-defined network architecture that rewards nodes based on contract theory to achieve fairness [15].…”

Section: State Of the Artmentioning

confidence: 99%

Introducing a Fair Tax Method to Harden Industrial Blockchain Applications against Network Attacks: A Game Theory Approach

Ghajar

Reich

2023

Computers

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Industrial Internet of Things (IIoT) systems are enhancing the delivery of services and boosting productivity in a wide array of industries, from manufacturing to healthcare. However, IIoT devices are susceptible to cyber-threats such as the leaking of important information, products becoming compromised, and damage to industrial controls. Recently, blockchain technology has been used to increase the trust between stakeholders collaborating in the supply chain in order to preserve privacy, ensure the provenance of material, provide machine-led maintenance, etc. In all cases, such industrial blockchains establish a novel foundation of trust for business transactions which could potentially streamline and expedite economic processes to a significant extent. This paper presents an examination of “Schloss”, an industrial blockchain system architecture designed for multi-factory environments. It proposes an innovative solution to increase trust in industrial networks by incorporating a fairness concept as a subsystem of an industrial blockchain. The proposed mechanism leverages the concept of taxes imposed on blockchain nodes to enforce ethical conduct and discipline among participants. In this paper, we propose a game theory-based mechanism to address security and trust difficulties in industrial networks. The mechanism, inspired by the ultimatum game, progressively punishes malicious actors to increase the cost of fraud, improve the compensation system, and utilise the reward reporting capabilities of blockchain technology to further discourage fraudulent activities. Furthermore, the blockchain’s incentive structure is utilised to reduce collusion and speed up the process of reaching equilibrium, thereby promoting a secure and trustworthy environment for industrial collaboration. The objective of this paper is to address lack of trust among industrial partners and introduce a solution that brings security and trust to the forefront of industrial blockchain applications.

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Machine learning in supply chain: prediction of real-time e-order arrivals using ANFIS

Hamdan

Wulamu

Zhang

et al. 2023

Int J Syst Assur Eng Manag

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Security threat model under internet of things using deep learning and edge analysis of cyberspace governance

Cited by 6 publications

References 22 publications

Machine learning in cybersecurity: A review of threat detection and defense mechanisms

Machine learning in cybersecurity: A review of threat detection and defense mechanisms

Introducing a Fair Tax Method to Harden Industrial Blockchain Applications against Network Attacks: A Game Theory Approach

Machine learning in supply chain: prediction of real-time e-order arrivals using ANFIS

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