Safeguarding modern networks from cyber intrusions has become increasingly challenging as attackers continually refine their evasion tactics.Although numerousmachine-learning-based intrusion detection systems(IDS)have...Safeguarding modern networks from cyber intrusions has become increasingly challenging as attackers continually refine their evasion tactics.Although numerousmachine-learning-based intrusion detection systems(IDS)have been developed,their effectiveness is often constrained by high dimensionality and redundant features that degrade both accuracy and efficiency.This study introduces a hybrid feature-selection framework that integrates the exploration capability of Prairie Dog Optimization(PDO)with the exploitation behavior of Ant Colony Optimization(ACO).The proposed PDO–ACO algorithm identifies a concise yet discriminative subset of features from the NSLKDD dataset and evaluates them using a Support Vector Machine(SVM)classifier.Experimental analyses reveal that the PDO–ACO model achieves superior detection accuracy of 98%while significantly lowering false alarms and computational overhead.Further validation on the CEC2017 benchmark suite confirms the robustness and adaptability of the hybrid model across diverse optimization landscapes,positioning PDO–ACO as an efficient and scalable approach for intelligent intrusion detection.展开更多
In designing a modern home with a focus on the comfort of residents and energy usage optimization simultaneously,the rise of the Internet of Things and its incorporation with sensor technology plays a vital role these...In designing a modern home with a focus on the comfort of residents and energy usage optimization simultaneously,the rise of the Internet of Things and its incorporation with sensor technology plays a vital role these days.The first and foremost task is to predict the energy consumption based on the available data.This study investigates the integration of artificial neural networks in smart home technology to improve energy usage prediction and efficiency,without compromising the comfort of occupants.A dynamic model based on an artificial neural network model is designed in this study which artificially controls light,heating process,and cooling to cut down energy wastage.Energy consumption data of 114 single-family apartments was collected between 2014 and 2016.Energy consumption is predicted by the current model with an accuracy of up to 99.9%for energy usage patterns,which helps to optimize resource management in real time.A robust modeling approach i.e.multi-layer perceptron networks was implemented along with energy usage data.Seventy percent of the data is used for training the neural networks,and the rest is used for testing and validation purposes.The current defined model shows a significant improvement in prediction accuracy of energy usage and efficiency when compared to state-of-the-art models.Metrics such as R-values and mean square error are employed to check accuracy.These results show the essential role of artificial intelligence in improving energy management for smart buildings,with potential benefits including significant energy usage and loss management to help improve sustainable living.展开更多
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2026R500)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Safeguarding modern networks from cyber intrusions has become increasingly challenging as attackers continually refine their evasion tactics.Although numerousmachine-learning-based intrusion detection systems(IDS)have been developed,their effectiveness is often constrained by high dimensionality and redundant features that degrade both accuracy and efficiency.This study introduces a hybrid feature-selection framework that integrates the exploration capability of Prairie Dog Optimization(PDO)with the exploitation behavior of Ant Colony Optimization(ACO).The proposed PDO–ACO algorithm identifies a concise yet discriminative subset of features from the NSLKDD dataset and evaluates them using a Support Vector Machine(SVM)classifier.Experimental analyses reveal that the PDO–ACO model achieves superior detection accuracy of 98%while significantly lowering false alarms and computational overhead.Further validation on the CEC2017 benchmark suite confirms the robustness and adaptability of the hybrid model across diverse optimization landscapes,positioning PDO–ACO as an efficient and scalable approach for intelligent intrusion detection.
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R 500)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘In designing a modern home with a focus on the comfort of residents and energy usage optimization simultaneously,the rise of the Internet of Things and its incorporation with sensor technology plays a vital role these days.The first and foremost task is to predict the energy consumption based on the available data.This study investigates the integration of artificial neural networks in smart home technology to improve energy usage prediction and efficiency,without compromising the comfort of occupants.A dynamic model based on an artificial neural network model is designed in this study which artificially controls light,heating process,and cooling to cut down energy wastage.Energy consumption data of 114 single-family apartments was collected between 2014 and 2016.Energy consumption is predicted by the current model with an accuracy of up to 99.9%for energy usage patterns,which helps to optimize resource management in real time.A robust modeling approach i.e.multi-layer perceptron networks was implemented along with energy usage data.Seventy percent of the data is used for training the neural networks,and the rest is used for testing and validation purposes.The current defined model shows a significant improvement in prediction accuracy of energy usage and efficiency when compared to state-of-the-art models.Metrics such as R-values and mean square error are employed to check accuracy.These results show the essential role of artificial intelligence in improving energy management for smart buildings,with potential benefits including significant energy usage and loss management to help improve sustainable living.
