{"title":"Optimizing Cluster Head Selection in Mobile Ad Hoc Networks: A Connectivity Probability Approach Using Poisson Distribution and Residual Energy","authors":"Mohammed Ali Tawfeeq","doi":"10.18280/isi.280524","DOIUrl":"https://doi.org/10.18280/isi.280524","url":null,"abstract":"ABSTRACT","PeriodicalId":38604,"journal":{"name":"Ingenierie des Systemes d''Information","volume":"105 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135977248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the dynamically evolving landscape of cybersecurity, safeguarding IT infrastructures has emerged as an imperative to thwart the escalation of cyber-attacks. Anomaly-based Intrusion Detection Systems (IDS) play a pivotal role in identifying aberrant behaviours that elude conventional detection mechanisms. Nonetheless, these systems are not without their shortcomings, manifesting as elevated false alarm rates and a diminished efficacy in detecting sophisticated attacks. In response to these challenges, a hybrid approach, entailing Machine Learning (ML) techniques, was employed to augment the performance of anomaly-based IDS in terms of detection accuracy, False Positive (FP) Rate, and detection time. The approach encompassed a two-fold optimization strategy: initial feature selection predicated on feature importance derived from the XGBoost classifier, followed by Bayesian optimization (BO) for hyperparameter tuning. The optimization was conducted with respect to two objective functions, namely the ROC-AUC score and the Average Precision score, each serving to identify the optimal hyperparameters for their respective maximization. Classifiers, including Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Stochastic Gradient Descent (SGD), were subjected to training under configurations encompassing both the hyperparameters resultant from BO and the default hyperparameters, the latter serving as reference models. Evaluation, conducted through a multifaceted metric analysis, substantiated the superiority of the optimized models over their reference counterparts, with the optimized XGBoost models demonstrating the most commendable performance. This paradigm offers a promising avenue for enhancing detection precision and mitigating false alarms, thereby fortifying the security of computer
{"title":"Enhancing Anomaly-Based Intrusion Detection Systems: A Hybrid Approach Integrating Feature Selection and Bayesian Hyperparameter Optimization","authors":"Naoual Berbiche, Jamila El Alami","doi":"10.18280/isi.280506","DOIUrl":"https://doi.org/10.18280/isi.280506","url":null,"abstract":"In the dynamically evolving landscape of cybersecurity, safeguarding IT infrastructures has emerged as an imperative to thwart the escalation of cyber-attacks. Anomaly-based Intrusion Detection Systems (IDS) play a pivotal role in identifying aberrant behaviours that elude conventional detection mechanisms. Nonetheless, these systems are not without their shortcomings, manifesting as elevated false alarm rates and a diminished efficacy in detecting sophisticated attacks. In response to these challenges, a hybrid approach, entailing Machine Learning (ML) techniques, was employed to augment the performance of anomaly-based IDS in terms of detection accuracy, False Positive (FP) Rate, and detection time. The approach encompassed a two-fold optimization strategy: initial feature selection predicated on feature importance derived from the XGBoost classifier, followed by Bayesian optimization (BO) for hyperparameter tuning. The optimization was conducted with respect to two objective functions, namely the ROC-AUC score and the Average Precision score, each serving to identify the optimal hyperparameters for their respective maximization. Classifiers, including Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Stochastic Gradient Descent (SGD), were subjected to training under configurations encompassing both the hyperparameters resultant from BO and the default hyperparameters, the latter serving as reference models. Evaluation, conducted through a multifaceted metric analysis, substantiated the superiority of the optimized models over their reference counterparts, with the optimized XGBoost models demonstrating the most commendable performance. This paradigm offers a promising avenue for enhancing detection precision and mitigating false alarms, thereby fortifying the security of computer","PeriodicalId":38604,"journal":{"name":"Ingenierie des Systemes d''Information","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135978222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Performance Analysis of a Generic Modular Adder via RTL Programming and IP Modeling Techniques on FPGA","authors":"Tukur Gupta, Gaurav Verma, Shamim Akhter","doi":"10.18280/isi.280514","DOIUrl":"https://doi.org/10.18280/isi.280514","url":null,"abstract":"ABSTRACT","PeriodicalId":38604,"journal":{"name":"Ingenierie des Systemes d''Information","volume":"148 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135978573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Farid Boumaza, Abou El Hassane Benyamina, Djaafar Zouache, Laith Abualigah, Ahmed Alsayat
ABSTRACT
{"title":"An Improved Harris Hawks Optimization Algorithm Based on Bi-Goal Evolution and Multi-Leader Selection Strategy for Multi-Objective Optimization","authors":"Farid Boumaza, Abou El Hassane Benyamina, Djaafar Zouache, Laith Abualigah, Ahmed Alsayat","doi":"10.18280/isi.280503","DOIUrl":"https://doi.org/10.18280/isi.280503","url":null,"abstract":"ABSTRACT","PeriodicalId":38604,"journal":{"name":"Ingenierie des Systemes d''Information","volume":"63 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135976343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Enhancing Lifespan and Energy Efficiency in Mobile Smart Dust Networks","authors":"Rajesh Dennison, Ramesh Dennison, Giji Kiruba Dasebenezer, Edwin Singh Chinnathurai","doi":"10.18280/isi.280520","DOIUrl":"https://doi.org/10.18280/isi.280520","url":null,"abstract":"ABSTRACT","PeriodicalId":38604,"journal":{"name":"Ingenierie des Systemes d''Information","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135977612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The increasing adoption of solar power as a renewable and eco-friendly energy source necessitates precise forecasting of solar power generation. Accurate predictions are crucial for effective grid management and the seamless integration of renewable energy into the power grid. This study proposes a novel hybrid meta-heuristic optimization framework, empowered by an ensemble deep learning model, to enhance the accuracy of solar power generation forecasting. The proposed methodology comprises several methodical phases: data pre-processing, feature extraction, feature selection, and deep learning-based forecasting. Initially, the collected raw data undergo a pre-processing stage involving data cleaning and standardization via the z-score method. Subsequent feature extraction transforms the pre-processed data into a reduced set of representative features, leveraging Linear Discriminant Analysis (LDA), measures of central tendency (Weighted arithmetic mean, Winsorized mean, standard deviation), statistical dispersion (Interquartile range (IQR), Median absolute deviation (MAD)), and Information Theoretic measures (Mutual Information and Information Gain). The optimal features are then selected through a newly proposed hybrid optimization approach, the Gorilla Customized Teaching Learning-Based Optimization (GC-TLBO) Algorithm, an innovative combination of the Artificial Gorilla Troops Optimizer (GTO) and the Teaching-Learning-Based Optimization (TLBO). Solar power forecasting is accomplished using a novel ensembled deep learning model, which integrates optimized Recurrent Neural Network (O-RNN) with a Deep Belief Network (DBN) and a Deep Convolutional Neural Network (DCNN). The final outcome is derived from the O-RNN, which inputs the results from the DBN and DCNN, respectively. The DBN and DCNN are trained using the optimal features derived from the GC-TLBO, while the weights of the RNN are fine-tuned using the same algorithm. The proposed model was implemented in Python (Google Colab), and its performance was evaluated using several metrics: Normalized Mean Square Error (NMSE), Mean Squared Relative Error (MSRE), Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE). The results demonstrate that the proposed model outperforms existing models, offering superior forecasting performance.
{"title":"An Advanced Hybrid Meta-Heuristic Model for Solar Power Generation Forecasting via Ensemble Deep Learning","authors":"K.V.B. Saraswathi Devi, Muktevi Srivenkatesh","doi":"10.18280/isi.280528","DOIUrl":"https://doi.org/10.18280/isi.280528","url":null,"abstract":"The increasing adoption of solar power as a renewable and eco-friendly energy source necessitates precise forecasting of solar power generation. Accurate predictions are crucial for effective grid management and the seamless integration of renewable energy into the power grid. This study proposes a novel hybrid meta-heuristic optimization framework, empowered by an ensemble deep learning model, to enhance the accuracy of solar power generation forecasting. The proposed methodology comprises several methodical phases: data pre-processing, feature extraction, feature selection, and deep learning-based forecasting. Initially, the collected raw data undergo a pre-processing stage involving data cleaning and standardization via the z-score method. Subsequent feature extraction transforms the pre-processed data into a reduced set of representative features, leveraging Linear Discriminant Analysis (LDA), measures of central tendency (Weighted arithmetic mean, Winsorized mean, standard deviation), statistical dispersion (Interquartile range (IQR), Median absolute deviation (MAD)), and Information Theoretic measures (Mutual Information and Information Gain). The optimal features are then selected through a newly proposed hybrid optimization approach, the Gorilla Customized Teaching Learning-Based Optimization (GC-TLBO) Algorithm, an innovative combination of the Artificial Gorilla Troops Optimizer (GTO) and the Teaching-Learning-Based Optimization (TLBO). Solar power forecasting is accomplished using a novel ensembled deep learning model, which integrates optimized Recurrent Neural Network (O-RNN) with a Deep Belief Network (DBN) and a Deep Convolutional Neural Network (DCNN). The final outcome is derived from the O-RNN, which inputs the results from the DBN and DCNN, respectively. The DBN and DCNN are trained using the optimal features derived from the GC-TLBO, while the weights of the RNN are fine-tuned using the same algorithm. The proposed model was implemented in Python (Google Colab), and its performance was evaluated using several metrics: Normalized Mean Square Error (NMSE), Mean Squared Relative Error (MSRE), Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE). The results demonstrate that the proposed model outperforms existing models, offering superior forecasting performance.","PeriodicalId":38604,"journal":{"name":"Ingenierie des Systemes d''Information","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135976491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sri Widaningsih, Wardani Muhamad, Robbi Hendriyanto, Heru Nugroho
ABSTRACT
{"title":"An ID3 Decision Tree Algorithm-Based Model for Predicting Student Performance Using Comprehensive Student Selection Data at Telkom University","authors":"Sri Widaningsih, Wardani Muhamad, Robbi Hendriyanto, Heru Nugroho","doi":"10.18280/isi.280508","DOIUrl":"https://doi.org/10.18280/isi.280508","url":null,"abstract":"ABSTRACT","PeriodicalId":38604,"journal":{"name":"Ingenierie des Systemes d''Information","volume":"10 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135976318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Arif Ridho Lubis, Yuyun Yusnida Lase, Darwis Abdul Rahman, Deden Witarsyah
ABSTRACT
{"title":"Improving Spell Checker Performance for Bahasa Indonesia Using Text Preprocessing Techniques with Deep Learning Models","authors":"Arif Ridho Lubis, Yuyun Yusnida Lase, Darwis Abdul Rahman, Deden Witarsyah","doi":"10.18280/isi.280522","DOIUrl":"https://doi.org/10.18280/isi.280522","url":null,"abstract":"ABSTRACT","PeriodicalId":38604,"journal":{"name":"Ingenierie des Systemes d''Information","volume":"41 26","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135976337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Artificial Intelligence and Machine Learning Approaches to Document Digitization in the Banking Industry: An Analysis","authors":"Archana Lopes, Kolla Bhanu Prakash","doi":"10.18280/isi.280521","DOIUrl":"https://doi.org/10.18280/isi.280521","url":null,"abstract":"","PeriodicalId":38604,"journal":{"name":"Ingenierie des Systemes d''Information","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135976507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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