The XGBoost Model for Network Intrusion Detection Boosted by Enhanced Sine Cosine Algorithm

AlHosni, Nadheera; Jovanovic, Luka; Antonijevic, Milos; Bukumira, Milos; Živković, Miodrag; Strumberger, Ivana; Mani, Joseph; Bačanin, Nebojša

doi:10.1007/978-3-031-12413-6_17

Cited by 34 publications

(9 citation statements)

References 50 publications

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“…Therefore, the scientists have experimented with wide range of optimization algorithms on a variety of practical problems. Some of the most promising domains include medical diagnostics support [14,20,24,33,43], wireless sensor network tuning [4,9,12,48,57,66], stock price estimations [16], as well as intrusion detection and security domain [1,31,41,45,55,56,60,65] and plant classifying task [17].…”

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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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)

Self Cite

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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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“…A significant number of metaheuristics methods exist today, explained by the no free lunch theorem [57], stating that an universal solution to all optimization problems is not existing. Consequently, the researchers have implemented a large number of algorithms, and employed them on a wide range of real-world problems from different domains, such as medical diagnostics [15,21,25,37,47], wireless sensor networks [5,10,13,53,63,72], stock price forecasting [17], intrusion detection and other security applications [2,35,45,51,61,62,66,71] and plant classification problem [18]. Metaheuristics algorithms have been also employed to tune the cloud, edge and fog computing [3,6,16,24,52,65], feature selection [9,20,23,36,41,54,67], dropout regularization [12], a wide spectrum of COVID-19 challenges [26,64,[68][69][70], artificial neural networks optimization [4,7,8,…”

Section: Metaheuristics Optimizationmentioning

confidence: 99%

Training a Logistic Regression Machine Learning Model for Spam Email Detection Using the Teaching-Learning-Based-Optimization Algorithm

Berrou¹,

Kalbani²,

Antonijevic³

et al. 2023

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

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Spam and emails have always been intrinsically linked since the creation of the Advanced Research Projects Agency Network, otherwise known as (ARPANET). The latter witnessed, on May 3rd, 1978, the first known spam email to date. Today, spam emails negatively affect the users' productivity and private lives. A significant number of approaches emerged in the past two decades that deal with the spam detection problem, with limited success. Therefore, the current paper presents an intelligent and automated solution to spam email detection using a logistic regression model trained by a teaching-learning-based optimization algorithm. The proposed solution has been tested on two benchmark spam email datasets (CSDMC2010 and TurkishEmail), and evaluated against seven other contending cutting-edge metaheuristics utilized in the same experimental setup. The simulation outcomes without a doubt indicate the superior level of accuracy achieved by the proposed solution.

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The XGBoost Model for Network Intrusion Detection Boosted by Enhanced Sine Cosine Algorithm

Cited by 34 publications

References 50 publications

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

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

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

Training a Logistic Regression Machine Learning Model for Spam Email Detection Using the Teaching-Learning-Based-Optimization Algorithm

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