Intrusion Detection and Prevention Systems in Wireless Networks

In society today, public and personal communication are often carried out through wireless technology. These technologies can be vulnerable to various types of attacks. Attackers can access the signal to listen or to cause more damage on the wireless networks. Intrusion Detection and Prevention System (IDPS) technology can be used to monitor and analyze the signal for any infiltration to prevent interception or other malicious intrusion. An overview description of IDPSs and their core functions, the primary types of intrusion detection mechanisms, and the limitations of IDPSs are discussed. This work perceives the requirements of developing new and sophisticated detection and prevention methods based on, and managed by, combining smart techniques including machine learning, data mining, and game theory along with risk analysis and assessment techniques. This assists wireless networks toremain secure and aids system administrators to effectively monitor their systems

Survey of main challenges (security and privacy) in wireless body area networks for healthcare applications

Abstract Wireless Body Area Network (WBAN) is a new trend in the technology that provides remote mechanism to monitor and collect patient's health record data using wearable sensors. It is widely recognized that a high level of system security and privacy play a key role in protecting these data when being used by the healthcare professionals and during storage to ensure that patient's records are kept safe from intruder's danger. It is therefore of great interest to discuss security and privacy issues in WBANs. In this paper, we reviewed WBAN communication architecture, security and privacy requirements and security threats and the primary challenges in WBANs to these systems based on the latest standards and publications. This paper also covers the state-of-art security measures and research in WBAN. Finally, open areas for future research and enhancements are explored

More-SPEED: Enhancing Protein Activity Prediction from DNA Sequences

This work presents More-SPEED, a novel model for accurately predicting protein activity while minimizing computational demands. Leveraging optimized structures and data preprocessing techniques, More-SPEED achieves high accuracy in protein activity prediction. The model incorporates the data compression three dimension (DC-3D) layer, utilizing the graph mining pattern-fist frequency graph mining (GMP-FFGM) algorithm for efficient preprocessing of complex Deoxyribonucleic acid (DNA) sequence datasets. Additionally, the deterministic structure network using the natural-inspired optimization algorithm called Whale Optimization Algorithm (DSN-WOA) structure optimizes parameters of the Biological dynamic long short term memory (BDLSTM) model, reducing processing time and eliminating manual parameter selection. The BDLSTM layer plays a crucial role in matching codons and predicting protein names, reducing computational complexity without compromising accuracy. The Bi-Rule layer efficiently determines protein activity, especially in disease contexts, providing valuable insights in a shorter time compared to alternative approaches. Evaluation metrics validate the effectiveness of More-SPEED in accurately predicting protein activity, making it a promising solution for advancing protein research

Creating a cutting-edge neurocomputing model with high precision

Abstract The prediction of oil prices has a significant impact on the economies of countries, particularly in oil-rich nations like Iraq, and affects the labor market. Prediction techniques are vital tools for extracting knowledge from complex databases, such as oil prices. This study aims to develop a prediction model that accurately determines oil prices based on seven fundamental characteristics, including Date, WTI, GOLD, SP 500, US DOLLAR INDEX, US 10YR BOND, and DJU. The proposed model utilizes advanced neurocomputing techniques that analyze the seven features over a ten-year period. The model comprises three main stages: preprocessing, determining feature importance through computing correlation, entropy, and information gain, and splitting the dataset into training and testing. The first part of the dataset builds the predictor called Hybrid Model to Oil Price based on Neurocomputing Techniques, while the second part evaluates model using three error measures: R2, MSE, and MAE. The model proves its ability to provide accurate predictions with low error rates. Multivariate analysis shows that WTI, GOLD, and US DOLLAR INDEX have a more significant impact on oil prices, with information gain values of WTI = 11.272, GOLD = 11.227, and DJU = 11.614. The Gate Recurrent Unit neurocomputing technique demonstrates its ability to handle datasets with features that behave differently over multiple years and provides accurate predictions with low errors in a short time, withR2 = 0.945, MSE = 0.0505, and MAE = 0.1948. This study provides valuable insights into the prediction of oil prices and highlights the efficacy of advanced neurocomputing techniques for extracting knowledge from complex databases

Main challenges (generation and returned energy) in a deep intelligent analysis technique for renewable energy applications.

In recent years, there has been an increasing demand for Renewable Energy (RE), which refers to energy generated from natural sources such as solar and wind power. Consequently, numerous scientific studies have been conducted to explore various approaches for controlling this type of energy. This work aims to highlight the main challenges associated with the generation and return of RE by employing intelligent data analysis techniques, specifically deep learning. These challenges are examined from different perspectives, including pre-processing, the methodology and techniques used in deep learning, and the evaluation measures employed. Some of the research in this area is focused on predicting the highest amount of energy that can be generated at a particular time and location, while others aim to predict the largest amount of electrical energy that can be returned to the electricity grid to optimize the use of surplus RE resources and maximize their benefits. These efforts are crucial to ensure the effective and continuous operation of the electrical grid. However, despite the efficiency and high accuracy of these models, they are hindered by complex calculations that require considerable time to produce the desired outcomes. Additionally, numerous measures are employed to evaluate the models' performance, including assessing their completion rate, quality of performance, accuracy of results, efficiency, error rate, feasibility of investing in RE, and the largest amount of surplus energy that can be returned to the electricity generation network.

Developed teamwork optimizer for model parameter estimation of the proton exchange membrane fuel cell

This paper proposes a new optimal methodology for model parameters estimation of the Proton Exchange Membrane Fuel Cell. The main purpose here is to design a newly developed metaheuristic technique to deliver a model with higher accuracy. In this study, we utilized two modifications for the Teamwork Optimizer to get higher accuracy. The two modifiers are opposition-based learning and chaotic mechanism. The results show that using the opposition-based learning, the population diversity has been kept, owing to the greater population size due to the solution space, and using the Chaos theory, the population diversity has been increased. This is proved by applying the Improved Teamwork Optimizer to minimize the Root Mean Square Error and Integral Absolute Error between the suggested model and empirical data. The validation has been done by applying the proposed Improved Teamwork Optimizer to two studied cases, which are Nexa Proton Exchange Membrane Fuel Cell and NedSstack PS6 Proton Exchange Membrane Fuel Cell, and comparing it with other published works. Simulation results showed that the proposed method with 1.14 Integral Absolute Error and 0.21 Root Mean Square Error for NedSstack PS6 Proton Exchange Membrane Fuel Cells and with 12 Integral Absolute Error and 0.17 Root Mean Square Error for Nexa Proton Exchange Membrane Fuel Cells provides the minimum error value among the other optimization techniques. This shows the higher potential of the proposed method for use as the parameter estimator for Proton Exchange Membrane Fuel Cells