An improved bio-inspired based intrusion detection model for a cyberspace

Otor, Samera; Akinyemi, Bodunde Odunola; Aladesanmi, T. A.; Aderounmu, G. A.; Kamagaté, B. H.

doi:10.1080/23311916.2020.1859667

Cited by 8 publications

(3 citation statements)

References 31 publications

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“…Due to the presence of many optimization techniques inherent in the bio-inspired algorithms, the cyber threat intelligence can be converged with a high rate. This intelligence will propel to develop a cyber threat detection model with metaheuristics so that the performance of the framework does not degrade [16]. knowing if the performance of the CTI framework is increasing or not [17].…”

Section: For Bridging the Research Gapmentioning

confidence: 99%

A Comprehensive Review -Application of Bio-inspired Algorithms for Cyber Threat Intelligence Framework

Yogi¹,

Aiswarya²

2023

RRRJ

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In most of the modern-day computing systems, security enhancements are a part of security design. The majority of the effort in providing robust security to a system is involved in the identification of cyber threats and how to recover from such cyberattacks. Many researchers have proposed sub-optimal strategies which has been the motivation of this research. This study summarises the research gaps and proposes research direction for mitigating the challenges concerned in that direction. This work reviews the current methodologies to provide a framework which can auto identify cyber threats and to determine how the bio-inspired algorithms can be applied to minimize the effort involved in identification and recovery from cyberattacks. Cyber threat intelligence frameworks serve as crucial elements in providing secure operating environment for the cyber practitioners. The design and development of cyber threat intelligence framework is challenging not only for the cost and effort involved in it but also due to intrinsic dependent entities of cyber security. This study proposes novel principles for bridging the identified research gaps through feature engineering, trust computing base, and bio-inspired based time optimization. There is a lot of research potential in this direction and this study is a sincere and ideal attempt towards the same goal.

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Section: For Bridging the Research Gapmentioning

confidence: 99%

A Comprehensive Review -Application of Bio-inspired Algorithms for Cyber Threat Intelligence Framework

Yogi¹,

Aiswarya²

2023

RRRJ

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“…A relatively new branch of research in intrusion detection belongs to the work of Ferreira and Mario (2020) and Otor et al (2021) where the authors study the bio-inspired algorithms for the intrusion detection problem. In the work of Otor et al (2021), the authors considers a spider web as the internet today and the preys as the attackers. Hence, the goal of the spiders is to catch-up the preys by using some optimization algorithms.…”

Section: Related Workmentioning

confidence: 99%

Improving the performance of the intrusion detection systems by the machine learning explainability

Dang

2021

IJWIS

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Purpose This study aims to explain the state-of-the-art machine learning models that are used in the intrusion detection problem for human-being understandable and study the relationship between the explainability and the performance of the models. Design/methodology/approach The authors study a recent intrusion data set collected from real-world scenarios and use state-of-the-art machine learning algorithms to detect the intrusion. The authors apply several novel techniques to explain the models, then evaluate manually the explanation. The authors then compare the performance of model post- and prior-explainability-based feature selection. Findings The authors confirm our hypothesis above and claim that by forcing the explainability, the model becomes more robust, requires less computational power but achieves a better predictive performance. Originality/value The authors draw our conclusions based on their own research and experimental works.

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“…Previously, technology-oriented research focused on how to improve performance and capacity [13], [14], [15], but more recent research has focused on making such swarm systems more safe, secure, and reliable to operate [7], [16], [17]. Studies in the development of new coordination algorithms that combine biological processes are based on self-regulation [18], [19], and environmental adaptability to allow a swarm of drones to work with greater sophistication, reliability, scalability, and flexibility [20].…”

Section: Introductionmentioning

confidence: 99%

Chaos Detection and Mitigation in Swarm of Drones Using Machine Learning Techniques and Chaotic Attractors

NEBE,

SANNI,

ADETONA

et al. 2022

IJACSA

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Most existing identification and tackling of chaos in swarm drone missions focus on single drone scenarios. There is a need to assess the status of a system with multiple drones, hence, this research presents an on-the-fly chaotic behavior detection model for large numbers of flying drones using machine learning techniques. A succession of three Artificial Intelligence knowledge discovery procedures, Logistic Regression (LR), Convolutional Neural Network (CNN), Gaussian Mixture Models (GMMs) and Expectation-Maximization (EM) were employed to reduce the dimension of the actual data of the swarm of drone's flight and classify it as non-chaotic and chaotic. A onedimensional, multi-layer perceptive, deep neural network-based classification system was also used to collect the related characteristics and distinguish between chaotic and non-chaotic conditions. The Rössler system was then employed to deal with such chaotic conditions. Validation of the proposed chaotic detection and mitigation technique was performed using realworld flight test data, demonstrating its viability for real-time implementation. The results demonstrated that swarm mobility horizon-based monitoring is a viable solution for real-time monitoring of a system's chaos with a significantly reduced commotion effect. The proposed technique has been tested to improve the performance of fully autonomous drone swarm flights.

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An improved bio-inspired based intrusion detection model for a cyberspace

Cited by 8 publications

References 31 publications

A Comprehensive Review -Application of Bio-inspired Algorithms for Cyber Threat Intelligence Framework

A Comprehensive Review -Application of Bio-inspired Algorithms for Cyber Threat Intelligence Framework

Improving the performance of the intrusion detection systems by the machine learning explainability

Chaos Detection and Mitigation in Swarm of Drones Using Machine Learning Techniques and Chaotic Attractors

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