In response to the increasing volume of network traffic and the growing sophistication of cyber threats, this study examines the use of deep learning-based intrusion detection systems (IDSs) in large-scale network environments. Traditional IDS face challenges such as high false positive rates, complex feature engineering, and class imbalances in datasets, all of which impede accurate threat detection. To overcome these limitations, we implement various deep learning models, including multilayer perceptron (MLP), convolutional neural network (CNN), and long short-term memory (LSTM), alongside traditional machine learning algorithms such as logistic regression, naive Bayes, random forest, K-nearest neighbors, and decision trees. A significant contribution of this study is the application of the synthetic minority over-sampling technique (SMOTE) to address class imbalance, enhancing the representativeness of the learning process. Additionally, we conduct a comprehensive performance comparison of the models, incorporating correlation-based feature selection and hyperparameter tuning to maximize detection accuracy. Our results indicate that deep learning models, particularly CNN and LSTM, outperform traditional machine learning approaches in cyber threat detection, achieving accuracy rates of 98%. However, random forest achieves the highest accuracy at 99.9%, demonstrating its effectiveness in structured intrusion detection tasks. Moreover, we evaluate computational efficiency and practical deployment considerations, discussing trade-offs between accuracy and resource consumption. These findings highlight the potential of deep learning-based IDS for large-scale network security applications while addressing key challenges such as interpretability and computational overhead. The study provides actionable insights for selecting the most suitable IDS models based on specific network environments and security requirements.

Phishing attacks are a growing concern for individuals and organizations alike, with the potential to cause significant financial and reputational damage. Traditional methods for detecting phishing attacks, such as blacklists and signature-based techniques, have limitations that have led to developing more advanced techniques. In recent years, machine learning and deep learning techniques have gained attention for their potential to improve the accuracy of phishing detection. Deep learning algorithms, such as CNNs and LSTMs, are designed to learn from patterns and identify anomalies in data, making them more effective in detecting sophisticated phishing attempts. To develop a comprehensive understanding of the current state of research on the use of deep learning techniques for phishing detection, a systematic literature review is necessary. This review aims to identify the various deep learning techniques used for phishing detection, their effectiveness, and areas for future research. By synthesizing the findings of relevant studies, this review identifies the strengths and limitations of different approaches and provides insights into the challenges that need to be addressed to improve the accuracy and effectiveness of phishing detection. This review aims to contribute to developing a coherent and evidence-based understanding of the use of deep learning techniques for phishing detection. The review identifies gaps in the literature and informs the development of future research questions and areas of focus. With the increasing sophistication of phishing attacks, applying deep learning in this area is a critical and rapidly evolving field. This systematic literature review aims to provide insights into the current state of research and identify areas for future research to advance the field of phishing detection using deep learning.

The generative model and discriminative model are the two categories of statistical models used in keystroke biometric areas. Generative models have the trait of handling missing or irregular data, and perform well for limited training data. Discriminative models are fast in making predictions for new data, resulting in faster classification of new data compared to the generative models. In an attempt to build an efficient model for keystroke biometric user identification, this study proposes a hybrid POHMM/SVM method taking advantage of both generative and discriminative models. The partially observable hidden Markov model (POHMM) is an extension of the hidden Markov model (HMM), which has shown promising performance in user verification and handling missing or infrequent data. On the other hand, the support vector machine (SVM) has been a widely used discriminative model in keystroke biometric systems for the last decade and achieved a higher accuracy rate for large data sets. In the proposed model, features are extracted using the POHMM model, and a one-class support vector machine is used as the anomaly detector. For user identification, the study examines POHMM parameters using five different discriminative classifiers: support vector machines, k-nearest neighbor, random forest, multilayer perceptron (MLP) neural network, and logistic regression. The best accuracy of 91.3% (mean 0.868, SD 0.132) is achieved by the proposed hybrid POHMM/SVM approach among all generative and discriminative models.

Diabetes is one of the most common health conditions in the United States, affecting more than 37 million U.S. adults. Despite how many people are affected by this disease, a permanent cure does not currently exist. Therefore, it is very important for patients to get the proper diagnosis and treatment. If diabetes is not diagnosed and properly treated to control blood sugar levels, serious health problems can occur. Though undiagnosed cases are very dangerous, still 1 in 5 U.S. adults with diabetes do not know that they have it. Therefore, a method for early detection or prediction of the disease is imperative for healthcare workers to provide timely distribution of treatment. Presently, hospitals collect large volumes of patient data with various tests and provide appropriate treatment based on diagnoses. With big data analytics, hidden patterns can be found in the data that help to predict diagnosis outcomes. Such predictions can help lower the rate of undiagnosed patients. In our paper, we propose a prediction model for classification of diabetes based on various features of patient data. We developed and applied six machine learning algorithms on two diabetes data sets to predict diagnoses. We found that the best algorithms for prediction with our two data sets were Ridge Classifier with an accuracy of 83.12%, along with Random Forest and Decision Tree classifiers, both achieving accuracies of 95%.

Access control is the process of protecting resources by allowing or rejecting access to particular resources. It is an integral part of physical security systems and protects resources from unauthorized use. Over the past decade, there has been a huge demand and need for more sophisticated and secure access technologies. Scientists are developing cutting edge technologies to fulfill the requirements for secure access control experiences. Access control systems, using the latest biometric technologies such as fingerprint, facial recognition, iris recognition, DNA, and signature recognition have offered a higher level of security than the conventional systems. Biometrics plays a vital role in the identification and authentication phase of access control. Every biometric system has weaknesses and benefits. The system success depends on selecting the proper type of biometric modality to use and understanding how it affects system performance, cost, and limitations. Fingerprint holds the first place according to its popularity but facial recognition and iris recognition continue to grow for its accuracy and are considered as the future of biometric recognition. Besides biometric technology, other future possibilities for secure access control are- mobile access control, cloud-based security, and multi-factor authentication. This paper presents access control components, functions, and types. The aim of the paper is to discuss and analyze the latest biometric access control to secure sensitive information. This paper also provides an overview of access control, discusses the latest biometric technology, and presents the future trend of access control.

Traditional signature-based anti-malware tools often fail to detect zero-day ransomware attacks due to their reliance on known patterns. This paper presents a real-time ransomware detection framework that models system behavior as a Reinforcement Learning (RL) environment. Behavioral features—including file entropy, CPU usage, and registry changes—are extracted from dynamic analysis logs generated by Cuckoo Sandbox. A (DQN) agent is trained to proactively block malicious actions by maximizing long-term rewards based on observed behavior. Experimental evaluation across multiple ransomware families such as WannaCry, Locky, Cerber, and Ryuk demonstrates that the proposed RL agent achieves a superior detection accuracy, precision, and F1-score compared to existing static and supervised learning methods. Furthermore, ablation tests and latency analysis confirm the model’s robustness and suitability for real-time deployment. This work introduces a behavior-driven, generalizable approach to ransomware defense that adapts to unseen threats through continual learning.