Malware is one of the most frequent cyberattacks, with its prevalence growing daily across the network. Malware traffic is always asymmetrical compared to benign traffic, which is always symmetrical. Fortunately, there are many artificial intelligence techniques that can be used to detect malware and distinguish it from normal activities. However, the problem of dealing with large and high-dimensional data has not been addressed enough. In this paper, a high-performance malware detection system using deep learning and feature selection methodologies is introduced. Two different malware datasets are used to detect malware and differentiate it from benign activities. The datasets are preprocessed, and then correlation-based feature selection is applied to produce different feature-selected datasets. The dense and LSTM-based deep learning models are then trained using these different versions of feature-selected datasets. The trained models are then evaluated using many performance metrics (accuracy, precision, recall, and F1-score). The results indicate that some feature-selected scenarios preserve almost the same original dataset performance. The different nature of the used datasets shows different levels of performance changes. For the first dataset, the feature reduction ratios range from 18.18% to 42.42%, with performance degradation of 0.07% to 5.84%, respectively. The second dataset reduction rate is between 81.77% and 93.5%, with performance degradation of 3.79% and 9.44%, respectively.

Wireless sensor networks have become a vital component of the infrastructure for many modern applications. With the increasing use of wireless sensor networks, the challenges facing these networks in the field of security are escalating and growing, and with the rapid advancement of wireless communication technology, these networks are exposed to increasing, complex and continuous threats. Our research is characterized by innovation in the field of security technology to enhance protection, repel attacks and detect intrusions, among these innovations are intrusion detection systems based on machine learning as a creative and new solution. In this research, we highlight the effectiveness of different machine learning algorithms, such as supervised and unsupervised learning, in detecting anomalies and intrusions within wireless sensor networks, as our goal focuses on enhancing the security of wireless sensor networks (WSNs) by adopting intrusion detection systems (IDS) based on machine learning techniques. In this context, with a focus on using the WSN-DS dataset. The results of this research showed that machine-learning models could improve the security efficiency of wireless sensor networks by achieving accuracy ranging from 91% to 99.7% and testing time ranging from 0.006 to 0.1249, which enhances the ability to effectively retrieve and detect threats in real time.

While threats in cyberspace are in a state of constant evolution, the use of AI in cyber defense has numerous opportunities and dangers. This paper evaluates adversarial robustness for deep learning networks in network security applications by introducing a novel one-dimensional CNN model for malicious traffic detection. We conducted rigorous end-to-end processing and analysis of network traffic data, using a balanced dataset of 200,000 connections (46.52% benign, 53.48% malicious). Our model architecture includes three convolutional blocks (32, 64, and 128 filters, respectively) with batch normalization and dropout mechanisms (0.3 and 0.2, respectively). We use standardized feature scaling, label encoding for categorical features, and stratified sampling to maintain class distribution integrity. Our proposed approach achieved remarkable performance metrics compared to standard approaches with a 95% AUC-ROC result (15% better than baseline CNN models) and detection rate of 99.99% malicious traffic (compared to 98.5% with standard architectures). The model demonstrates better robustness with only 10 false negatives out of 107,895 malicious samples, a 67% enhancement compared to current state-of-the-art systems. Training dynamics show great stability with minimal overfitting (validationtraining loss difference of only 0.01), indicating good generalization ability.

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.

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