Feature Selection by Improved Sand Cat Swarm Optimizer for Intrusion Detection

Jovanovic, Dijana; Marjanović, Marina; Antonijevic, Milos; Živković, Miodrag; Budimirović, Nebojša; Bačanin, Nebojša

doi:10.1109/aie57029.2022.00134

Cited by 20 publications

(14 citation statements)

References 24 publications

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“…Algorithms can be combined with other metaheuristic solutions to provide a higher performing algorithm or they can be combined with a machine learning solution to optimize its actions. Some examples are optimizing cloud computing scheduling, edge and fog computing [3,6,16,24,28,45,59], feature selection [9,20,23,32,36,47,61], dropout regularization [12], COVID-19 detection and fake news detection [26,58,[62][63][64], tuning artificial neural networks [4,7,8,11,14,19,44], text clustering [22,48] and cryptocurrency price prediction as well [41].…”

Section: Swarm Intelligence and Literature Reviewmentioning

confidence: 99%

Forecasting Bitcoin Price by Tuned Long Short Term Memory Model

Petrovic¹,

Jovanovic²,

Živković³

et al. 2023

Proceedings of the 1st International Conference on Innovation in Information Technology and Business (ICIITB 2022)

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The interest for cryptocurrencies is high and hence this work focuses on providing a practical real-world application of the swarm metaheuristics and long short term memory model (LSTM). The goal is price forecasting which is interesting due to the high volatility of the cryptocurrencies. The authors apply LSTM for the solution of the problem which has been proven to reap results with this type of problem. The LSTM is further optimized by a swarm metaheuristic -arithmetic optimization algorithm (AOA). The solution was tested alongside familiar high-performing competitors with the use of standard metrics mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and root mean squared error (RMSE). These metrics have been used for comparison between the solutions, upon which the proposed solution obtained overall best performance that testifies to the improvement of the solution.

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Section: Swarm Intelligence and Literature Reviewmentioning

confidence: 99%

Forecasting Bitcoin Price by Tuned Long Short Term Memory Model

Petrovic¹,

Jovanovic²,

Živković³

et al. 2023

Proceedings of the 1st International Conference on Innovation in Information Technology and Business (ICIITB 2022)

Self Cite

View full text Add to dashboard Cite

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“…Other successful applications of metaheuristics optimizers include tuning of the cloud, edge and fog computing [2,5,15,23,46,59], feature selection challenge [8,19,22,32,37,49,61], dropout regularization [11], a variety of COVID-19 applications [25,58,[62][63][64], tuning artificial neural networks [3,6,7,10,13,18,44], text clustering [21,50] and cryptocurrency price forecast [42].…”

Section: Metaheuristics Optimizationmentioning

confidence: 99%

Artificial Neural Network Tuning by Improved Sine Cosine Algorithm for HealthCare 4.0

Gajevic¹,

Milutinovic²,

Krstovic³

et al. 2023

Proceedings of the 1st International Conference on Innovation in Information Technology and Business (ICIITB 2022)

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This paper explores classification of datasets for Healthcare 4.0 using artificial neural networks which are tuned by improved sine cosine algorithm (SCA). Healthcare 4.0 themes include internet of things (IoT), industrial IoT (IIoT), cognitive computing, artificial intelligence, cloud computing, fog computing, edge computing, and other industry 4.0 procedures. Health issues identification are critical since prompt treatment improves the quality of life for individuals affected. One of the most difficult challenges for artificial intelligence (AI) is selecting control parameters that are appropriate for the situation at hand. This paper presents a metaheuristics-based method for training the artificial neural network, by utilizing the SCA.

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“…NP-hard complexity with real world problems is common and hence the application of these algorithms is diverse. Some notable examples are artificial neural network optimization [7][8][9][10]12,14,15,19,21,26,32,36,48,53,54], wireless sensors networks (WSNs) [4,11,13,52,65,75], cryptocurrency trends estimations [44,49], finally the COVID-19 global epidemic-associated applications [22,25,64,66,[69][70][71]73], computer-conducted MRI classification and sickness determination [17,20,24,33,55], cloud-edge and fog computing and task scheduling [3,5,6,16,23,50,67], and lastly securing networks through intrusion detection [2,31,43,62,…”

Section: Swarm Intelligence Applications In Machine Learningmentioning

confidence: 99%

Intrusion Detection by XGBoost Model Tuned by Improved Multi-verse Optimizer

Petrovic¹,

Antonijevic²,

Strumberger³

et al. 2023

Proceedings of the 1st International Conference on Innovation in Information Technology and Business (ICIITB 2022)

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Artificial intelligence and internet of things (IoT) fields have contributed to the flourishment of the industry 4.0 concept. The main benefits include the improvements in terms of device communication, productivity, and efficiency. Nevertheless, there is a downside concerning the security of these systems. The amount of devices and their diversity prove a security risk. Due to this intrusion detection systems are paramount. This paper proposes a novel framework exploiting extreme gradient boosting machine learning model which is optimized by a modified version of the multi-verse optimizer metaheuristic. The UNSW-NB intrusion dataset was used for experimental purposes on which the other cutting-edge techniques were tested and compared. The results provide the proof of improvement as the proposed method outperformed all other overall metaheuristic performances. Furthermore, the units for truthfulness and polarity for the case have been established as a standard evaluation system. True and false positives exist alongside the same negative counterparts. The results provided by these metrics have been visualized and used for further comparison proving the superiority of the performance of the proposed solution.

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Feature Selection by Improved Sand Cat Swarm Optimizer for Intrusion Detection

Cited by 20 publications

References 24 publications

Forecasting Bitcoin Price by Tuned Long Short Term Memory Model

Forecasting Bitcoin Price by Tuned Long Short Term Memory Model

Artificial Neural Network Tuning by Improved Sine Cosine Algorithm for HealthCare 4.0

Intrusion Detection by XGBoost Model Tuned by Improved Multi-verse Optimizer

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