The production, testing, and processing of signals without any interpretation is a crucial task with time scale periods in today's biological applications. As a result, the proposed work attempts to use a deep learning model to handle difficulties that arise during the processing stage of biomedical information. Deep Conviction Systems (DCS) are employed at the integration step for this procedure, which uses classification processes with a large number of characteristics. In addition, a novel system model for analyzing the behavior of biomedical signals has been developed, complete with an output tracking mechanism that delivers transceiver results in a low-power implementation approach. Because low-power transceivers are integrated, the cost of implementation for designated output units will be decreased. To prove the effectiveness of DCS feasibility, convergence and robustness characteristics are observed by incorporating an interface system that is processed with a deep learning toolbox. They compared test results using DCS to prove that all experimental scenarios prove to be much more effective for about 79 percent for variations with time periods.

Detecting intrusions from the supervisory control and data acquisition (SCADA) systems is one of the most essential and challenging processes in recent times. Most of the conventional works aim to develop an efficient intrusion detection system (IDS) framework for increasing the security of SCADA against networking attacks. Nonetheless, it faces the problems of complexity in classification, requiring more time for training and testing, as well as increased misprediction results and error outputs. Hence, this research work intends to develop a novel IDS framework by implementing a combination of methodologies, such as clustering, optimization, and classification. The most popular and extensively utilized SCADA attacking datasets are taken for this system’s proposed IDS framework implementation and validation. The main contribution of this work is to accurately detect the intrusions from the given SCADA datasets with minimized computational operations and increased accuracy of classification. Additionally the proposed work aims to develop a simple and efficient classification technique for improving the security of SCADA systems. Initially, the dataset preprocessing and clustering processes were performed using the multifacet data clustering model (MDCM) in order to simplify the classification process. Then, the hybrid gradient descent spider monkey optimization (GDSMO) mechanism is implemented for selecting the optimal parameters from the clustered datasets, based on the global best solution. The main purpose of using the optimization methodology is to train the classifier with the optimized features to increase accuracy and reduce processing time. Moreover, the deep sequential long short term memory (DS-LSTM) is employed to identify the intrusions from the clustered datasets with efficient data model training. Finally, the proposed optimization-based classification methodology’s performance and results are validated and compared using various evaluation metrics.

Electricity theft harms smart grids and results in huge revenue losses for electric companies. Deep learning (DL), machine learning (ML), and statistical methods have been used in recent research studies to detect anomalies and illegal patterns in electricity consumption (EC) data collected by smart meters. In this paper, we propose a hybrid DL model for detecting theft activity in EC data. The model combines both a gated recurrent unit (GRU) and a convolutional neural network (CNN). The model distinguishes between legitimate and malicious EC patterns. GRU layers are used to extract temporal patterns, while the CNN is used to retrieve optimal abstract or latent patterns from EC data. Moreover, imbalance of data classes negatively affects the consistency of ML and DL. In this paper, an adaptive synthetic (ADASYN) method and TomekLinks are used to deal with the imbalance of data classes. In addition, the performance of the hybrid model is evaluated using a real-time EC dataset from the State Grid Corporation of China (SGCC). The proposed algorithm is computationally expensive, but on the other hand, it provides higher accuracy than the other algorithms used for comparison. With more and more computational resources available nowadays, researchers are focusing on algorithms that provide better efficiency in the face of widespread data. Various performance metrics such as F1-score, precision, recall, accuracy, and false positive rate are used to investigate the effectiveness of the hybrid DL model. The proposed model outperforms its counterparts with 0.985 Precision–Recall Area Under Curve (PR-AUC) and 0.987 Receiver Operating Characteristic Area Under Curve (ROC-AUC) for the data of EC.

Owing to the development and expansion of energy-aware sensing devices and autonomous and intelligent systems, the Internet of Things (IoT) has gained remarkable growth and found uses in several day-to-day applications. However, IoT devices are highly prone to botnet attacks. To mitigate this threat, a lightweight and anomaly-based detection mechanism that can create profiles for malicious and normal actions on IoT networks could be developed. Additionally, the massive volume of data generated by IoT gadgets could be analyzed by machine learning (ML) methods. Recently, several deep learning (DL)-related mechanisms have been modeled to detect attacks on the IoT. This article designs a botnet detection model using the barnacles mating optimizer with machine learning (BND-BMOML) for the IoT environment. The presented BND-BMOML model focuses on the identification and recognition of botnets in the IoT environment. To accomplish this, the BND-BMOML model initially follows a data standardization approach. In the presented BND-BMOML model, the BMO algorithm is employed to select a useful set of features. For botnet detection, the BND-BMOML model in this study employs an Elman neural network (ENN) model. Finally, the presented BND-BMOML model uses a chicken swarm optimization (CSO) algorithm for the parameter tuning process, demonstrating the novelty of the work. The BND-BMOML method was experimentally validated using a benchmark dataset and the outcomes indicated significant improvements in performance over existing methods.

With the advent of smart city networks and increased utilization of vehicles, the Internet of Vehicles (IoV) has attracted more attention from researchers. But, providing security to this type of network is one of the challenging and demanding tasks in the present day. For this purpose, the conventional works developed many networking frameworks and methodologies to enhance the privacy and security of smart city systems. Still, it has the significant limitations of high complexity in algorithm design, requires more time consumption for processing, reduced maintenance, and does not require proper authentication verification. Therefore, the primary purpose of this work is to develop a new security model for smart city networks using a combination of methodologies. Here, the Collaborative Mutual Authentication (CMA) mechanism is used to validate the identity of users based on the private key, public key, session key, and generated hash function. In addition, the Meta-heuristic Genetic Algorithm -Random Forest (MGA-RF) technique is deployed to detect the attacks in the network, ensuring the security of the smart city. During an evaluation, the proposed authentication-based security mechanism's performance is validated using various parameters, and the results are compared with the recent state-of-the-art models.