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Bella K, Guezzaz A, Benkirane S, Azrour M, Fouad Y, S. Benyeogor M, Innab N. An efficient intrusion detection system for IoT security using CNN decision forest. PeerJ Comput Sci 2024; 10:e2290. [PMID: 39314707 PMCID: PMC11419618 DOI: 10.7717/peerj-cs.2290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Accepted: 08/06/2024] [Indexed: 09/25/2024]
Abstract
The adoption and integration of the Internet of Things (IoT) have become essential for the advancement of many industries, unlocking purposeful connections between objects. However, the surge in IoT adoption and integration has also made it a prime target for malicious attacks. Consequently, ensuring the security of IoT systems and ecosystems has emerged as a crucial research area. Notably, advancements in addressing these security threats include the implementation of intrusion detection systems (IDS), garnering considerable attention within the research community. In this study, and in aim to enhance network anomaly detection, we present a novel intrusion detection approach: the Deep Neural Decision Forest-based IDS (DNDF-IDS). The DNDF-IDS incorporates an improved decision forest model coupled with neural networks to achieve heightened accuracy (ACC). Employing four distinct feature selection methods separately, namely principal component analysis (PCA), LASSO regression (LR), SelectKBest, and Random Forest Feature Importance (RFFI), our objective is to streamline training and prediction processes, enhance overall performance, and identify the most correlated features. Evaluation of our model on three diverse datasets (NSL-KDD, CICIDS2017, and UNSW-NB15) reveals impressive ACC values ranging from 94.09% to 98.84%, depending on the dataset and the feature selection method. Notably, our model achieves a remarkable prediction time of 0.1 ms per record. Comparative analyses with other recent random forest and Convolutional Neural Networks (CNN) based models indicate that our DNDF-IDS performs similarly or even outperforms them in certain instances, particularly when utilizing the top 10 features. One key advantage of our novel model lies in its ability to make accurate predictions with only a few features, showcasing an efficient utilization of computational resources.
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Affiliation(s)
- Kamal Bella
- Technology Higher School Essaouira, Cadi Ayyad University, Essaouira, Morocco
| | - Azidine Guezzaz
- Technology Higher School Essaouira, Cadi Ayyad University, Essaouira, Morocco
| | - Said Benkirane
- Technology Higher School Essaouira, Cadi Ayyad University, Essaouira, Morocco
| | - Mourade Azrour
- IDMS Team, Faculty of Sciences and Technics, Moulay Ismail University of Meknès, Errachidia, Morocco
| | - Yasser Fouad
- Department of Applied Mechanical Engineering, College of Applied Engineering, Muzahimiyah Branch, King Saud University, Riyadh, Saudi Arabia
| | | | - Nisreen Innab
- Department of Computer Science and Information Systems, College of Applied Sciences, AlMaarefa University, Riyadh, Saudi Arabia
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Gbafore E, Segera DR, Kiruki CRM. Genetic Artificial Hummingbird Algorithm-Support Vector Machine for Timely Power Theft Detection. ScientificWorldJournal 2024; 2024:5568922. [PMID: 39257965 PMCID: PMC11383651 DOI: 10.1155/2024/5568922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2024] [Accepted: 08/10/2024] [Indexed: 09/12/2024] Open
Abstract
Utilities face serious obstacles from power theft, which calls for creative ways to maintain income and improve operational effectiveness. This study presents a novel hybrid genetic artificial hummingbird algorithm-support vector machine classifier to detect power theft. The proposed algorithm combines the artificial hummingbird algorithm exploration phase with the genetic algorithm's mutation and crossover operators, to optimize the support vector machine's hyperparameters and categorize users as fraudulent or nonfraudulent. It utilizes 7,270 rows of labeled historical electricity consumption data from the Liberia Electricity Corporation over 15 independent runs. The methodology entailed data preprocessing, data split into training, validation, and testing sets in an 80-10-10 ratio, z-score normalization, optimization, training, validation, testing, and computation of six evaluation metrics. Its performance is compared with 13 metaheuristic classifiers and the conventional support vector machine. Findings indicate that the genetic artificial hummingbird algorithm-support vector machine outperforms the 13 rivals and the standard support vector machine in the six assessment measures with an accuracy score of 0.9986, precision of 0.9971, f_score of 0.9986, recall of 1, Matthews correlation coefficient of 0.9972, and g_mean of 0.9987. Furthermore, 90% of the time, Wilcoxon rank-sum tests revealed statistically significant differences between the algorithm and its rivals, demonstrating its superiority. The average run time is 4,656 seconds, the 3rd highest among its competitors. Despite the time complexity trade-off, its excellent performance on the unimodal and multimodal benchmark test functions, placing joint best in 7 out of 7 and 5 out of 6, respectively, provides important insights into the model's capacity to balance exploitation and exploration, improve local search, and avoid becoming stuck in the local optimum. These findings address important metaheuristic optimization gaps highlighting the model's potential for power theft detection.
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Affiliation(s)
- Emmanuel Gbafore
- Department of Electrical and Information Engineering University of Nairobi, Nairobi 301971, Kenya
| | - Davies Rene Segera
- Department of Electrical and Information Engineering University of Nairobi, Nairobi 301971, Kenya
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Bacanin N, Perisic M, Jovanovic G, Damaševičius R, Stanisic S, Simic V, Zivkovic M, Stojic A. The explainable potential of coupling hybridized metaheuristics, XGBoost, and SHAP in revealing toluene behavior in the atmosphere. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 929:172195. [PMID: 38631643 DOI: 10.1016/j.scitotenv.2024.172195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Revised: 04/01/2024] [Accepted: 04/01/2024] [Indexed: 04/19/2024]
Abstract
Toluene is a neurotoxic aromatic hydrocarbon and one of the major representatives of volatile organic compounds, known for its abundance, adverse health effects, and role in the formation of other atmospheric pollutants like ozone. This research introduces the enhanced version of the reptile search metaheuristics algorithm which has been utilized to tune the extreme gradient boosting hyperparameters, to investigate toluene atmospheric behavior patterns and interactions with other polluting species within defined environmental conditions. The study is based on a two-year database encompassing concentrations of inorganic gaseous contaminants every hour (NO, NO2, NOx, and O3), particulate matter fractions (PM1, PM2.5, and PM10), m,p-xylene, toluene, benzene, total non-methane hydrocarbons, and meteorological data. The experimental outcomes were validated against the results of extreme gradient boosting models optimized by seven other recent powerful metaheuristics algorithms. The best-performing model has been interpreted by employing Shapley additive explanations method. In the study, we have focused on the relationship between toluene and benzene, as its most important predictor, and provided a detailed description of environmental conditions which directed their interactions.
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Affiliation(s)
- Nebojsa Bacanin
- Informatics and Computing, Singidunum University, Danijelova 32, Belgrade 11010, Serbia; Sinergija University, Raje Banjicica, Bjeljina 76300, Bosnia and Herzegovina.
| | - Mirjana Perisic
- Informatics and Computing, Singidunum University, Danijelova 32, Belgrade 11010, Serbia; Institute of Physics Belgrade, University of Belgrade, Pregrevica 118, Belgrade 11010, Serbia.
| | - Gordana Jovanovic
- Informatics and Computing, Singidunum University, Danijelova 32, Belgrade 11010, Serbia; Institute of Physics Belgrade, University of Belgrade, Pregrevica 118, Belgrade 11010, Serbia.
| | - Robertas Damaševičius
- Centre of Real Time Computer Systems, Kaunas University of Technology, Barsausko 59, Kaunas 51423, Lithuania.
| | - Svetlana Stanisic
- Informatics and Computing, Singidunum University, Danijelova 32, Belgrade 11010, Serbia.
| | - Vladimir Simic
- Faculty of Transport and Traffic Engineering, University of Belgrade, Vojvode Stepe 305, Belgrade 44249, Serbia; Yuan Ze University, College of Engineering, Department of Industrial Engineering and Management, Taoyuan City 320315, Taiwan; Department of Computer Science and Engineering, College of Informatics, Korea University, Seoul 02841, Republic of Korea.
| | - Miodrag Zivkovic
- Informatics and Computing, Singidunum University, Danijelova 32, Belgrade 11010, Serbia.
| | - Andreja Stojic
- Informatics and Computing, Singidunum University, Danijelova 32, Belgrade 11010, Serbia; Sinergija University, Raje Banjicica, Bjeljina 76300, Bosnia and Herzegovina.
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Radanliev P, Santos O, Brandon-Jones A, Joinson A. Ethics and responsible AI deployment. Front Artif Intell 2024; 7:1377011. [PMID: 38601110 PMCID: PMC11004481 DOI: 10.3389/frai.2024.1377011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Accepted: 03/18/2024] [Indexed: 04/12/2024] Open
Abstract
As Artificial Intelligence (AI) becomes more prevalent, protecting personal privacy is a critical ethical issue that must be addressed. This article explores the need for ethical AI systems that safeguard individual privacy while complying with ethical standards. By taking a multidisciplinary approach, the research examines innovative algorithmic techniques such as differential privacy, homomorphic encryption, federated learning, international regulatory frameworks, and ethical guidelines. The study concludes that these algorithms effectively enhance privacy protection while balancing the utility of AI with the need to protect personal data. The article emphasises the importance of a comprehensive approach that combines technological innovation with ethical and regulatory strategies to harness the power of AI in a way that respects and protects individual privacy.
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Affiliation(s)
- Petar Radanliev
- Department of Computer Sciences, University of Oxford, Oxford, United Kingdom
- School of Management, University of Bath, Bath, United Kingdom
| | | | | | - Adam Joinson
- School of Management, University of Bath, Bath, United Kingdom
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Damaševičius R, Jovanovic L, Petrovic A, Zivkovic M, Bacanin N, Jovanovic D, Antonijevic M. Decomposition aided attention-based recurrent neural networks for multistep ahead time-series forecasting of renewable power generation. PeerJ Comput Sci 2024; 10:e1795. [PMID: 38259888 PMCID: PMC10803097 DOI: 10.7717/peerj-cs.1795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 12/14/2023] [Indexed: 01/24/2024]
Abstract
Renewable energy plays an increasingly important role in our future. As fossil fuels become more difficult to extract and effectively process, renewables offer a solution to the ever-increasing energy demands of the world. However, the shift toward renewable energy is not without challenges. While fossil fuels offer a more reliable means of energy storage that can be converted into usable energy, renewables are more dependent on external factors used for generation. Efficient storage of renewables is more difficult often relying on batteries that have a limited number of charge cycles. A robust and efficient system for forecasting power generation from renewable sources can help alleviate some of the difficulties associated with the transition toward renewable energy. Therefore, this study proposes an attention-based recurrent neural network approach for forecasting power generated from renewable sources. To help networks make more accurate forecasts, decomposition techniques utilized applied the time series, and a modified metaheuristic is introduced to optimized hyperparameter values of the utilized networks. This approach has been tested on two real-world renewable energy datasets covering both solar and wind farms. The models generated by the introduced metaheuristics were compared with those produced by other state-of-the-art optimizers in terms of standard regression metrics and statistical analysis. Finally, the best-performing model was interpreted using SHapley Additive exPlanations.
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Affiliation(s)
| | - Luka Jovanovic
- Faculty of Technical Sciences, Singidunum University, Belgrade, Serbia
| | - Aleksandar Petrovic
- Faculty of Informatics and Computing, Singidunum University, Belgrade, Serbia
| | - Miodrag Zivkovic
- Faculty of Informatics and Computing, Singidunum University, Belgrade, Serbia
| | - Nebojsa Bacanin
- Faculty of Informatics and Computing, Singidunum University, Belgrade, Serbia
| | | | - Milos Antonijevic
- Faculty of Informatics and Computing, Singidunum University, Belgrade, Serbia
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