Journal of Cybersecurity and Information Management

The global shift towards renewable energy sources is vital for environmental protection and sustainable development. However, the increasing reliance on data-driven technologies and interconnected systems in this sector introduces significant information security challenges. This research investigates a novel approach to enhance environmental protection in renewable energy development by integrating cybersecurity principles into a data-driven decision tree (DT-DD) framework. We analyze the vulnerabilities of renewable energy systems to cyber threats, focusing on the potential for malicious data manipulation to disrupt operations, compromise data integrity, and undermine environmental protection efforts. Our proposed DT-DD method leverages big data analytics and machine learning to model the complex interplay between energy production, environmental impact, and economic factors, while incorporating security measures to ensure data integrity and model robustness. The experimental analysis demonstrates the effectiveness of the DT-DD approach in achieving environmental protection goals, with results indicating [mention key findings, e.g., improved accuracy in pollution reduction, enhanced efficiency in resource management, and better evaluation of environmental impact]. Furthermore, we highlight the critical role of information security in safeguarding the data used in the DT-DD model and ensuring the reliable operation of renewable energy systems. By integrating cybersecurity into the development and deployment of renewable energy technologies, we can build a more resilient and sustainable energy future. This research contributes to a deeper understanding of the intersection between information security, renewable energy, and environmental protection, paving the way for more secure and effective strategies for a greener future.

Several researchers have paid attention to designing deployment algorithms in WSNs. In fact, there are many different ways to deploy sensors in sensors' fields. Selecting one of them mainly is based on the application for which WSN design. However, two main factors should be considered when designing a deployment approach in WSN: coverage and connectivity. In this paper, we present a genetic algorithm (GA) to enhance the sensor deployment in WSNs while concurrently improving the coverage and connectivity rate. The most popular deployment approach is to deploy sensor nodes randomly in the field. Although this approach is simple and easy, it may not achieve good results. In the proposed GA algorithm, the metaheuristic algorithm is used to deploy sensors. Simulations demonstrate that GA achieves a good deployment result compared to other research papers by ensuring maximum network coverage and connectivity rate by achieving efficient coverage and connectivity.

The paper presents the state-of-the-art natural language processing (NLP) models and methods, such as BERT and DistilBERT, to evaluate textual data and extract noteworthy insights. Preprocessing textual input, tokenization, and the implementation of deep learning architectures such as bidirectional LSTMs for classification tasks are all components of the approach that has been presented. To achieve the goal of producing accurate prediction models with the least amount of validation loss possible. Natural language processing (NLP) is a major focus of the manuscript in multiple areas such as sentiment analysis, language understanding, and text classification. The results show that our proposed NLP models perform exceptionally well. Long-term memory and natural language processing (NLP) go hand in hand. Therefore, these results demonstrate the value and relevance of our natural language processing approach to obtaining unstructured text data to improve and develop a variety of applications, such as chatbots, virtual assistants, and information retrieval systems, as well as to gain insights and help make better decisions, and the flexibility and generalizability of the models, while confirming their ability to handle a range of activities and textual materials. Excellent and accurate results were obtained in terms of validation, with the experimental models often exceeding the 99.85% accuracy benchmark. Another crucial factor to consider is that the average validation loss metrics for all tests remained remarkably low at 0.0058.

Given the growing demand for cybersecurity education, the practice of protecting network and software systems from digital and electronic attacks, investing in educational cybersecurity helps significantly reduce the risk of data breaches and protect against security breaches, and given the urgent need and growing number of students worldwide, it is also a way to connect with and between customers by building trust-based relationships, especially regarding essays. Automated Essay Scoring (AES) is a scalable solution for grading large amounts of essays with multiple uses, making it ideal for cybersecurity certification programs, online courses, and standardized tests. In the field of educational cybersecurity, automated essay scoring poses unique challenges due to specialized terminology, persistent and evolving threats. These automated scoring systems use domain-defined ontologies to grade essays but struggle to manage uncertainties, such as ambiguous language and partially valid arguments, which can influence the accuracy of their scoring. Traditional ontologies often struggle to interpret such uncertainties, leading to inconsistent results. Type 2 neutrosophic clustering (T2NS) as a novel approach introduced in this paper is combined with an automated article scoring system based on the cybersecurity learning ontology to address these challenges. The main steps include extracting concepts relevant to this research area from the articles, formalizing the cybersecurity scoring criteria as ontological rules and extending the ontology using T2NS, as well as defining membership functions to measure uncertainty and inconsistency levels. This evaluation using benchmark datasets of cybersecurity articles shows that this approach significantly enhances the scoring reliability and robustness of the approach compared to the basic AES methods.  

Protecting big data has become an extremely vital necessity in the context of cybersecurity, given the significant impact that this data has on institutions and clients. The importance of this type of data is highlighted as a basis for decision-making processes and policy guidance. Therefore, attacks on this data can lead to serious losses through illicit access, resulting in a loss of integrity, reliability, confidentiality, and availability of this data. The second problem in this context arises from the necessity of reducing the attack detection period and its vital importance in classifying malicious and non-harmful patterns. Structured Query Language Injection Attack (SQLIA) is among the common attacks targeting data, which is the focus of interest in the proposed model. The aim of this research revolves around developing an approach aimed at detecting and distinguishing patterns of loads sent by the user. The proposed method is based on training a model using random forest technology, which is considered one of the machine learning (ML) techniques while taking advantage of the Spark ML library that interacts effectively with big data frameworks. This is accompanied by a comprehensive analysis of the effectiveness of ML techniques in monitoring and detecting SQLIA. The study was conducted using the SQL dataset available on the Kaggle platform and showed promising results as the proposed method achieved an accuracy of 98.12%. While the proposed approach takes 0.046 seconds to determine the SQL type. It is concluded from these results that using the Spark ML library based on ML techniques contributes to achieving higher accuracy and requires less time to identify the class of request sent due to its ability to be distributed in memory.

Accurate weather forecasting is critical for sectors like agriculture, transportation, disaster management, and public safety. This paper presents a comprehensive methodology integrating traditional machine learning models, deep learning techniques, and ensemble learning approaches to enhance the precision and reliability of weather predictions. Using a combination of four datasets—two for classification and two for regression—the study evaluates various machine learning models such as Decision Trees, Support Vector Machines, and KNearest Neighbors, alongside ensemble methods like Bagging and AdaBoost. Additionally, deep learning models, particularly the Multilayer Perceptron (MLP), are employed to handle complex weather patterns. The Random Forest Regressor and Bagging Regressor consistently outperformed other models in terms of accuracy, precision, and F1-score. By comparing the performance of these models across different weather datasets, this research demonstrates the effectiveness of cross-validation and the importance of optimizing hyperparameters. The findings contribute valuable insights into enhancing the robustness and efficiency of weather forecasting systems, with potential applications in environmental monitoring, event planning, and climate change analysis.The findings indicate that Random Forest Regression consistently outperformed the other machine learning models evaluated. For ensemble learning, the Bagging Regressor was the top performer. In deep learning, the Multilayer Perceptron without cross-validation delivered outstanding performance. For the classification datasets, Random Forest achieved the highest accuracy, precision, and F-score. Our study also highlights the importance of cross-validation to prevent overfitting and ensure model robustness, as well as the impact of class imbalance on overall performance metrics.

The last decade has seen a massive explosion of data, with a lot of Personally Identifiable Information (PII) flooding devices and the cyberspace. This has necessitated the growing call and global awareness for data protection, to ensure the responsible use of data, protect the privacy of data subjects, and prevent crimes such as identity theft and cybercrime. This paper investigated the presence of residual data and Personally Identifiable Information (PII) in refurbished hard drives bought from a retail shop. The study leveraged digital forensic tools to perform data recovery on refurbished hard drives, and analyses for presence of PII. The study adopted a modified form of the steps in Digital Investigation outlined by NIST IR 8354. Result of this study showed that one out of the 3 hard drives that were reportedly formatted and sanitized by the vendors had residual data with PII. Data recovered includes 28691 files with size on disk as 152.20GB, including PII and sensitive data. Digital Forensic tools used for this study includes EaseUS Data Recovery Wizard and Autopsy. The findings of this study are quite relevant to current studies in privacy and data protection, including recent legislations such as Nigeria Data Protection Act (NDPA), General Data Protection Regulation (GDPR), and others. The paper also presents a comprehensive and forensically sound software-based methodology focused on the recovery of deleted data from hard drives.

The continual increase of cyber dangers necessitates creative techniques to better the identification and mitigation of malware. This research provides a cutting-edge examination of employing the Random Forest Classifier in combination with electromagnetic side-channel analysis for finding malicious software. Electromagnetic side-channel analysis harnesses the accidental information leakage from electronic systems, giving it a formidable tool for studying the underlying workings of gadgets. This study reveals how these electromagnetic side-channel signals may be used to identify subtle and evasive malware activities. The paper goes into the theoretical basis of electromagnetic side-channel analysis and the actual application of the Random Forest Classifier in this setting. By analyzing electromagnetic emissions, a wide range of devices and systems can be scrutinized for the telltale signs of malware-induced behaviors. Experimental results illustrate the effectiveness of this approach, showcasing the model demonstrated high accuracy, with an accuracy rate of up to 97%, demonstrating its ability to effectively leverage electromagnetic side-channel information for malicious program detection for enhanced cybersecurity measures.

Industrial Automation and Control Systems (IACS) are necessary for enabling secure information exchange between smart devices; ensuring security in Industrial Control Systems (ICS) is of importance due to the presence of these devices at distant locations and their control over vital plant activities. Intelligent devices and hosts use protocols such as Modbus, DNP3, IEC 60870, IEC 61850, and others. This paper focuses on the analysis and development of techniques for detecting of network traffic within the industrial environment, more specifically anomalies in the application ZZZAlayer in the to the protocol called Distribution Network Protocol (DNP3) is an open-source protocol used in Supervisory Control and Data Acquisition (SCADA) systems and widely recognized as the standard for the water, sewage, and oil and gas industries. it is used in the realm of industrial automation; they are critical facilities for the population and must be secured against any security breaches. One of the main objectives of cyber attackers is related with these systems. In This paper presents an architecture that, classification system by Deep Learning algorithm with (CNN). The proposed model was evaluated using standard Intrusion Detection Dataset for DNP3, with 7326) and 86field. The CNN algorithm obtained the best results accuracy

The world is witnessing an unprecedented boom in the development of information technology, which has come to encompass all aspects of life, Smart networks based on the Industrial Internet of Things (IIoT) are among the latest technologies used in various industries, contributing to improved production efficiency, reduced costs, and enhanced security, With the increasing reliance on this technology, the challenge of complex cyberattacks are also on the rise, These attacks are considered one of the major challenges facing smart networks, as attackers can exploit vulnerabilities in systems to access sensitive data or disrupt industrial operations, To counteract these threats, advanced intrusion detection systems should be developed, leveraging artificial intelligence and big data analytics to effectively detect and respond to attacks in real-time. Therefore, it is imperative to strive towards developing advanced and intelligent security systems to combat cyberattacks, ensuring the safety of industrial operations and data protection. This paper provides two IDS based on AI that are developed to negate the raising sophisticated cyberattacks. IN the first technique, Group of ML techniques such as Decision tree, Random Forrest classifiers, support vector classifier, and K_Nearest Neigbor are used with Feature reduction algorithms classifying network traffic subspecies to enhancing the accuracy and efficiency of detection systems. The second proposed technique for specifying the type of intrusion advantage various methodologies, particularly in the context of IoT networks and deep learning, the two algorithms are trained and tested using three well-known datasets to investigate wide domain of cyberattacks targeting the IIoT infrastructure. Results of the simulation show that the algorithm proposed in this work provides high improvement in detection of cyberattacks. The first algorithm achieved an accuracy of 99.9% and a very low false positive rate of just 0.1%. In addition, the second proposed algorithm identifies type of attack with a detection ratio of 99.76%. These results demonstrate how the proposed IDS based on AI algorithms can effectively detect network intrusion, and significantly enhance the security of IIoT system

Smart grids (SGs) are integral to modern utility systems, managing power generation, energy consumption, and communication networks. However, as these systems become increasingly interconnected, they are exposed to sophisticated cyber threats that can compromise their functionality and security. To address these challenges, this paper presents an AI-driven detection framework designed to significantly enhance cybersecurity in smart grids. The proposed system combining Recurrent Neural Networks (RNNs) with Support vector classifier to improve detection accuracy, recognition capabilities, and system robustness. The methodology comprises four main stages: (1) data preprocessing to ensure high-quality input for analysis, (2) traffic detection using RNNs to capture temporal patterns, (3) classification of traffic as normal or abnormal via support vector classifier (SVC), and (4) identification of specific attack types through another SVC for refined threat categorization. This integrated approach enables real-time detection of both known and emerging threats, focusing on minimizing false positives and maximizing detection precision. The system was evaluated on three comprehensive benchmark datasets: UNSW_NB15 and BoT-IoT, achieving an average accuracy of 100%. These results underscore the superiority of this AI-based solution over traditional intrusion detection systems, providing a robust and scalable framework for securing smart grids and other critical infrastructures.

Image steganography is a technique used to conceal secret information within digital images in such a way that the existence of the hidden data is not perceptible to the human eye. This method leverages the vast amount of data contained in image files, embedding the secret message by altering certain pixel values in a manner that is undetectable. The primary goal of image steganography is to ensure that the embedded information is secure and invisible, maintaining the original image's appearance and quality. Applications of image steganography include secure communication, digital watermarking, and copyright protection. Advanced methods often employ complex algorithms and machine learning models to enhance the robustness and imperceptibility of the hidden data, making it resistant to detection and manipulation.. The main idea of the proposed work is to utilize features extracted from images to construct a Hash Table, which will be employed for concealing and revealing a secret message. Since the same CNN model and input image (i.e., cover image) produce identical features, even if the cover image is slightly affected by noise, the same features (and consequently the same Hash Table) will be generated. The work demonstrated promising results in regenerating images when the cover image is slightly affected. However, as the noise level increases on the cover image, the regenerated images begin to lose more details.

In the realm of cardiovascular health, early detection and proactive management of heart disease are critical for improving patient outcomes. This paper introduces a novel real-time prediction model designed to assess heart disease risk during medical consultations and continuous health monitoring. Leveraging advanced machine learning techniques and a diverse dataset comprising patient demographics, medical history, and biometric measurements, our model provides immediate, actionable insights into an individual’s cardiovascular health. The model integrates seamlessly with electronic health record (EHR) systems and wearable health devices, offering real-time risk assessments that aid healthcare professionals in making informed decisions and tailoring personalized treatment plans. Through extensive validation and testing, our model demonstrates high accuracy and reliability, with potential to significantly enhance early intervention strategies and patient engagement in heart disease prevention. This research underscores the transformative potential of real-time predictive analytics in clinical practice and highlights pathways for future development and integration of intelligent health monitoring solutions.

Advanced Persistent Threats (APT) are intelligent, sophisticated cyberattacks that frequently evade detection by gradually interfering with vital systems or focusing on sensitive data. It is proposed herein the new approach of the Hybrid Dipper Throated Sine Cosine Optimization Algorithm (HDT-SCO) for APT detection in association with the EfficientDense-ViT model. It handles the class imbalance issue with advanced processing Adaptive Synthetic Minority Oversampling Technique (ADASYN), including min-max scaling for normalization, and median imputation for missing values. In terms of feature engineering, ResNet-152 and Symbolic Aggregate Approximation (SAX) are adopted for statistical, deep, and time series feature extraction. HDT-SCO optimizes the selection of relevant features to refine by integrating into it the three approaches: PCA, RFE, RF Feature Importance, and L1 Regularization (Lasso). Compared to current detection techniques, the best detection model shows high performance and efficiency through the hybrid deep learning model known as EfficientDense-ViT, which is a combination of EfficientNet, DenseNet, and Vision Transformers (ViT) that can detect APTs reliably. This method shows considerable improvement in both accuracy (0.98741 for the 70/30 split and 0.99143 for the 80/20 split) and efficiency as compared to existing models in the detection of APTs in cybersecurity.

As a dynamic paradigm, Cognitive radio networks (CRNs) in wireless transmission enable devices to intelligently adapt their communication parameter based on real-world spectrum availability. Spectrum sensing lies at the core of CRNs, where nodes continue to monitor the spectrum for underutilized or unused band detection. However, the presence of malicious users (MUs) has a significant impact reliability and performance of the network. MUs detection is indispensable to prevent interference or unauthorized access and ensure network integrity. Advanced techniques combining game theory, machine learning, and signal processing are used for effectively identifying and mitigating malicious activities. CRNs can ensure efficient spectrum utilization and enhance security in heterogeneous and dynamic environments by incorporating robust MU detection systems into spectrum sensing protocols. This article presents a Malicious User Recognition using the Coot Optimization Algorithm with Bayesian Belief Network (MUR-COABBN) technique for CRN. The MUR-COABBN technique exploits metaheuristics with a Bayesian machine-learning method for the classification of the MUs in the CRN. In the MUR-COABBN technique, the COA is initially used to choose better feature subsets. Moreover, the detection of MUs can be performed by the use of BBN. Finally, the parameter tuning of the BBN model is carried out using an improved seeker optimization algorithm (ISOA). The experimental evaluation of the MUR-COABBN technique takes place with respect to distinct aspects. The experimentation outcomes implied the improved performance of the MUR-COABBN methodology with other methods under distinct measures. Therefore, the MUR-COABBN model can effectually and accurately improve security in the CRN.