Modelling of oppositional Aquila Optimizer with machine learning enabled secure access control in Internet of drones environment

Perumalla, Subhadra; Chatterjee, Santanu; Kumar, Abhishek

doi:10.1016/j.tcs.2022.08.019

Cited by 12 publications

(4 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The improved versions of AO can handle a large range of difficult real-world optimization problems better than the standard AO. The strategies used in AO are hybridization with NIOAs [22,23], oppositional-based learning [24], chaotic sequence [25], Levy flight-based strategy [26], Gauss map and crisscross operator [27], Niche Thought with Dispersed Chaotic Swarm [28], random learning mechanism and Nelder-Mead Simplex Search [29], wavelet mutation [30], Weighted Adaptive Searching Technique [31], Binay AO [32], and multi-objective AO [33].…”

Section: Previous Work On Ao and Dolmentioning

confidence: 99%

Dynamic Random Walk and Dynamic Opposition Learning for Improving Aquila Optimizer: Solving Constrained Engineering Design Problems

Varshney,

Kumar,

Ali

et al. 2024

Biomimetics

View full text Add to dashboard Cite

One of the most important tasks in handling real-world global optimization problems is to achieve a balance between exploration and exploitation in any nature-inspired optimization method. As a result, the search agents of an algorithm constantly strive to investigate the unexplored regions of a search space. Aquila Optimizer (AO) is a recent addition to the field of metaheuristics that finds the solution to an optimization problem using the hunting behavior of Aquila. However, in some cases, AO skips the true solutions and is trapped at sub-optimal solutions. These problems lead to premature convergence (stagnation), which is harmful in determining the global optima. Therefore, to solve the above-mentioned problem, the present study aims to establish comparatively better synergy between exploration and exploitation and to escape from local stagnation in AO. In this direction, firstly, the exploration ability of AO is improved by integrating Dynamic Random Walk (DRW), and, secondly, the balance between exploration and exploitation is maintained through Dynamic Oppositional Learning (DOL). Due to its dynamic search space and low complexity, the DOL-inspired DRW technique is more computationally efficient and has higher exploration potential for convergence to the best optimum. This allows the algorithm to be improved even further and prevents premature convergence. The proposed algorithm is named DAO. A well-known set of CEC2017 and CEC2019 benchmark functions as well as three engineering problems are used for the performance evaluation. The superior ability of the proposed DAO is demonstrated by the examination of the numerical data produced and its comparison with existing metaheuristic algorithms.

show abstract

Section: Previous Work On Ao and Dolmentioning

confidence: 99%

Dynamic Random Walk and Dynamic Opposition Learning for Improving Aquila Optimizer: Solving Constrained Engineering Design Problems

Varshney,

Kumar,

Ali

et al. 2024

Biomimetics

View full text Add to dashboard Cite

show abstract

“…Perumalla et al [19] an oppositional Aquila Optimizerbased FS with ML-assisted IDS (OAOFS-MLIDS) in the IoD platform is introduced. The suggested technique's main aim is to achieve safe access control through the intrusion detection that exists in it.…”

Section: Literature Workmentioning

confidence: 99%

Enhancing Security in IoT-Assisted UAV Networks Using Adaptive Mongoose Optimization Algorithm With Deep Learning

Alotaibi,

Sayed,

Samir Abd Elhameed

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Due to the sensitive and mission-critical nature of the data collected and transferred, security in IoT-assisted UAV networks is of great significance. Intrusion detection in IoT-assisted UAV networks includes the deployment of complex monitoring systems to identify and respond to cyberattacks, physical breaches, or unauthorized access. This system employs a combination of anomaly detection and signaturebased methods to find malicious or unusual activities within the network. A robust intrusion detection mechanism is essential for protecting the security and integrity of the UAVs and the data collected, ensuring that any possible vulnerabilities are promptly addressed and identified. Consequently, this study introduces an adaptive mongoose optimizer algorithm with a deep learning-based intrusion detection (AMOA-DLID) method in IoT-assisted UAV networks. The AMOA-DLID technique intends to ensure security in the IoTassisted UAV networks via an intrusion detection process. In the presented AMOA-DLID technique, AMOA is initially applied for the feature selection process. The following sparse autoencoder (SAE) model can be exploited for the recognition of the intrusions. Lastly, the recognition rate of the SAE model can be improved by employing the Harris Hawks optimizer (HHO) technique. The detailed experimental study of the AMOA-DLID model is performed on the benchmark dataset of IDS. The extensive results portrayed that the AMOA-DLID technique reaches improved security over other models on the IoT-assisted UAV networks.

show abstract

“…Researchers applied OBL and its variations to AO and observed that it was a decent method for increasing efficiency. For the improvement of the AO, classical OBL [ 61 , 62 ], quasi based OBL [ 63 , 64 ], random OBL [ 65 , 66 ] had been successfully applied. In an IoD environment, Perumalla et al [ 61 ] presented an oppositional AO-based feature selection with ML enabled intrusion detection system (OAOFS-MLIDS) model.…”

Section: Related Work On Classical Ao and Its Improved Variantsmentioning

confidence: 99%

“…For the improvement of the AO, classical OBL [ 61 , 62 ], quasi based OBL [ 63 , 64 ], random OBL [ 65 , 66 ] had been successfully applied. In an IoD environment, Perumalla et al [ 61 ] presented an oppositional AO-based feature selection with ML enabled intrusion detection system (OAOFS-MLIDS) model. With the use of intrusion detection, the suggested model can ensure safe access to the system.…”

Section: Related Work On Classical Ao and Its Improved Variantsmentioning

confidence: 99%

A Comprehensive Survey on Aquila Optimizer

Sasmal

Hussien

Das

et al. 2023

Arch Computat Methods Eng

View full text Add to dashboard Cite

Aquila Optimizer (AO) is a well-known nature-inspired optimization algorithm (NIOA) that was created in 2021 based on the prey grabbing behavior of Aquila. AO is a population-based NIOA that has demonstrated its effectiveness in the field of complex and nonlinear optimization in a short period of time. As a result, the purpose of this study is to provide an updated survey on the topic. This survey accurately reports on the designed enhanced AO variations and their applications. In order to properly assess AO, a rigorous comparison between AO and its peer NIOAs is conducted over mathematical benchmark functions. The experimental results show the AO provides competitive outcomes.

show abstract

Modelling of oppositional Aquila Optimizer with machine learning enabled secure access control in Internet of drones environment

Cited by 12 publications

References 24 publications

Dynamic Random Walk and Dynamic Opposition Learning for Improving Aquila Optimizer: Solving Constrained Engineering Design Problems

Dynamic Random Walk and Dynamic Opposition Learning for Improving Aquila Optimizer: Solving Constrained Engineering Design Problems

Enhancing Security in IoT-Assisted UAV Networks Using Adaptive Mongoose Optimization Algorithm With Deep Learning

A Comprehensive Survey on Aquila Optimizer

Contact Info

Product

Resources

About