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.

Generally, the existing Intrusion Detection Systems (IDS) solutions suffer from low detection accuracy for some attack types compared with the overall detection accuracy of attacks. The data imbalance technically affects the ratio of detection accuracy of low frequent attacks class (e.g. zero‐day attack) compared to attacks with more instances. Therefore, IDS‐based machine learning algorithms potentially suffer from high false‐positive rates. To overcome the limitation of existing solutions, a hyper‐clustering model is proposed for dynamic intrusion detection based on the Density‐Based Spatial Clustering of Applications with Noise (DBSCAN) and cosine similarity. The proposed solution develops the standard DBSCAN by adding a new evolving process based on distance measures between the clusters to overcome the imbalance dataset. Moreover, a new classifier is proposed based on cosine similarity to predict the labelling of abnormal behaviour. The experimental results show that the proposed model outperformed the original DBCAN and the related works. The mean silhouette of the proposed DBSCAN achieves a high score of 0.87 compared to other solutions. Furthermore, the proposed DBSCAN reduces the mean square error from 0.66 to 0.13 and achieves 86.82%, 79.10% and 90.03% in general accuracy on KDDTest+, KDDTest‐21 NSL‐KDD and UNSW‐NB15 benchmark datasets, respectively.

Artificial intelligence has many applications in various industries, including agriculture. It can help overcome challenges by providing efficient solutions, especially in the early stages of development. When working with tree leaves to identify the type of disease, diseases often show up through changes in leaf color. Therefore, it is crucial to improve the color brightness before using them in intelligent agricultural systems. Color improvement should achieve a balance where no new colors appear, as this could interfere with accurate identification and diagnosis of the disease. This is considered one of the challenges in this field. This work proposes an effective model for olive disease diagnosis, consisting of five modules: image enhancement, feature extraction, clustering, and deep neural network. In image enhancement, noise reduction, balanced colors, and CLAHE are applied to LAB color space channels to improve image quality and visual stimulus. In feature extraction, raw images of olive leaves are processed through triple convolutional layers, max pooling operations, and flattening in the CNN convolutional phase. The classification process starts by dividing the data into clusters based on density, followed by the use of a deep neural network. The proposed model was tested on over 3200 olive leaf images and compared with two deep learning algorithms (VGG16 and Alexnet). The results of accuracy and loss rate show that the proposed model achieves (98%, 0.193), while VGG16 and Alexnet reach (96%, 0.432) and (95%, 1.74), respectively. The proposed model demonstrates a robust and effective approach for olive disease diagnosis that combines image enhancement techniques and deep learning-based classification to achieve accurate and reliable results.

Malware detection is a vital problem, and efficient methods that can efficiently detect malware are needed. The increasing use of mobile computers makes malware detection a vital part of security in an era where smartphones have come to play a key role in many of our daily lives. Earlier approaches, however, suffer from high false positive rates; they are not scalable for larger databases, or they are not amenable to adapt well to novel zero-day malware. For these reasons, the demand for more sensitive and flexible detection models is high. In this study, we develop a hybrid mobile malware detection framework that leverages ant colony optimization (ACO) and deep neural networks (DNNs) to improve detection accuracy, reduce the rate of false positives, and make the model resilient to new malware. AntDroidNet is a novel ACO-enabled feature selection model that dynamically reduces the feature dimensionality by selecting single instances to include the most informative properties and avoid dimensionality. A DNN is consequently constructed to train the determined set of features, improving the identified classification performance and decreasing the number of instances with false discoveries. In this way, a self-optimizing feedback loop can iteratively improve the feature selection process given the performance of the DNN, leading to a dynamic and efficient detection model. Using the CICMalDroid2020 dataset, the proposed AntDroidNet model achieves a remarkable accuracy of 99.89% and an excellent false positive rate of only 0.13% and outperforms the classical machine learning algorithms in terms of accuracy and efficiency. AntDroidNet is a scalable and powerful mobile malware detection model that eclipses all state-of-the-art methods and shows important enhancements in efficiency and reliability. By prototyping whitelisting systems, this work opens new avenues in mobile security and lays the groundwork for future work on building real-time detection components and system components able to scale to the fast pace of evolution of mobile malware in new connected ecosystems. Reason for Expression of Concern:The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in “AntDroidNet Cybersecurity Model: A Hybrid Integration of Ant Colony Optimization and Deep Neural Networks for Android Malware Detection”. The journal has initiated an additional editorial assessment of the article’s methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility. This notice is issued to ensure transparency while the review is ongoing. The Expression of Concern does not constitute a final determination regarding the validity of the work. The journal will update readers once the assessment is completed and will take any necessary editorial action in accordance with the journal’s policies and COPE guidance.See expression of concern available at:https://doi.org/10.58496/MJCS/2026/003https://mesopotamian.press/journals/index.php/CyberSecurity/article/view/1027

Tire defects are crucial for safe driving. Specialized experts or expensive tools such as stereo depth cameras and depth gages are usually used to investigate these defects. In image processing, feature extraction, reduction, and classification are presented as three challenging and symmetric ways to affect the performance of machine learning models. This paper proposes a hybrid system for cracked tire detection based on the adaptive selection of correlation features and deep belief neural networks. The proposed system has three steps: feature extraction, selection, and classification. First, the oriented gradient histogram extracts features from the tire images. Second, the proposed adaptive correlation feature selection selects important features with a threshold value adapted to the nature of the images. The last step of the system is to predict the image category based on the deep belief neural networks technique. The proposed model is tested and evaluated using real images of cracked and normal tires. The experimental results show that the proposed solution performs better than the current studies in effectively classifying tire defect images. The proposed hybrid cracked tire detection system based on adaptive correlation feature selection and Deep Belief Neural Networks’ performance provided better classification accuracy (88.90%) than that of Belief Neural Networks (81.6%) and Convolution Neural Networks (85.59%).

The rapid growth of the number of devices connected to the Internet and the increasing demand for electronic services have led to a huge growth in the number of cyberattacks targeting cyberspace and the development of their methodology. Therefore, there must be mechanisms, laws, and rules regulating the work of these applications and protecting them from electronic attacks. The Domain Name System (DNS) has several vulnerabilities that can be exploited by cyber attackers to launch their attacks, and the most important one of these vulnerabilities is that the response size is always greater than the size of the request. According to reports published by numerous security companies, distributed reflection denial of service (DRDoS) attacks against DNS are regarded as one of the most hazardous and rapidly spreading threats in recent years. An enhanced mechanism that is able to detect DNS‐based DRDoS attacks that exploit the DNS responses to launch their attacks is proposed. The proposed mechanism was designed based on modified metaheuristic optimization algorithms and an adaptive threshold. This mechanism consists of two models and four stages. The first model is called ‘Proactive Feature Selection,’ and the second model is called ‘Evolving Dynamic Fuzzy Clustering.’ The four stages of the proposed mechanism are: the preprocessing stage, feature selection stage, detection stage, and enhancement stage. The new mechanism has been implemented on the CICDDoS2019 standard dataset and achieves a detection accuracy of 95.44% with a false‐positive rate of 0.22%. The results show that the new mechanism is better than others depending on the detection accuracy and false positives.