ABSTRACT
A smart grid (SG) is a cutting-edge electrical grid that utilizes digital communication technology and automation to effectively handle electricity consumption, distribution, and generation. It incorporates energy storage systems, smart meters, and renewable energy sources for bidirectional communication and enhanced energy flow between grid modules. Due to their cyberattack vulnerability, SGs need robust safety measures to protect sensitive data, ensure public safety, and maintain a reliable power supply. Robust safety measures, comprising intrusion detection systems (IDSs), are significant to protect against malicious manipulation, unauthorized access, and data breaches in grid operations, confirming the electricity supply chain's integrity, resilience, and reliability. Deep learning (DL) improves intrusion recognition in SGs by effectually analyzing network data, recognizing complex attack patterns, and adjusting to dynamic threats in real-time, thereby strengthening the reliability and resilience of the grid against cyber-attacks. This study develops a novel Mountain Gazelle Optimization with Deep Ensemble Learning based intrusion detection (MGODEL-ID) technique on SG environment. The MGODEL-ID methodology exploits ensemble learning with metaheuristic approaches to identify intrusions in the SG environment. Primarily, the MGODEL-ID approach utilizes Z-score normalization to convert the input data into a uniform format. Besides, the MGODEL-ID approach employs the MGO model for feature subset selection. Meanwhile, the detection of intrusions is performed by an ensemble of three classifiers such as long short-term memory (LSTM), deep autoencoder (DAE), and extreme learning machine (ELM). Eventually, the dung beetle optimizer (DBO) is utilized to tune the hyperparameter tuning of the classifiers. A widespread simulation outcome is made to demonstrate the improved security outcomes of the MGODEL-ID model. The experimental values implied that the MGODEL-ID model performs better than other models.
ABSTRACT
Enterprise risk management (ERM) frameworks convey vital principles that help create a consistent risk management culture, irrespective of employee turnover or industry standards. Enterprise Management System (EMS) are becoming a popular research area for assuring a company's long-term success. Statistical pattern recognition, federated learning, database administration, visualization technology, and social networking are all used in this field, which includes artificial intelligence (AI), data science, and statistics. Risk assessment in EMS is critical for enterprise decision-making to be effective. Recent advancements in AI, machine learning (ML), and deep learning (DL) concepts have enabled the development of effective risk assessment models for EMS. This special issue seeks groundbreaking research articles that showcase the application of applied probability and statistics to interdisciplinary studies. This study offers Improved Metaheuristics with a Deep Learning Enabled Risk Assessment Model (IMDLRA-SES) for Smart Enterprise Systems. Using feature selection (FS) and DL models, the provided IMDLRA-SES technique estimates business risks. Preprocessing is used in the IMDLRA-SES technique to change the original financial data into a usable format. In addition, an FS technique based on oppositional lion swarm optimization (OLSO) is utilized to find the best subset of features. In addition, the presence or absence of financial hazards in firms is classified using the triple tree seed algorithm (TTSA) with a probabilistic neural network (PNN) model. The TTSA is used as a hyperparameter optimizer to improve the efficiency of the PNN-based categorization. An extensive set of experimental evaluations is performed on German and Australian credit datasets to illustrate the IMDLRA-SES model's improved performance. The performance validation of the IMDLRA-SES model portrayed a superior accuracy value of 95.70% and 96.09% over existing techniques.