In today's interconnected world, the need for robust intrusion and malware detection and prevention has never been more critical. Despite significant advances in developing intrusion detection systems (IDS), achieving holistic security continues to be an evolving challenge. The sophistication of cyber threats necessitates IDS models that are both robust and adaptable.  
In 2010, a seminal work by [PERSON] and [PERSON] discussed the challenges of applying machine learning (ML) for anomaly detection [1]. They highlighted limitations such as imbalanced training cases and high false-positive rates, as well as the importance of feature selection. In response, researchers have explored various ML algorithms to enhance IDS for detecting cyber attacks, such as spam classification [2, 3], malware detection [4], and intrusion detection [5, 6, 7, 8].  
Recent years have witnessed the rise of Deep Learning (DL) methods as powerful tools in detecting cyber threats [9, 10, 11, 12, 13, 14]. Various ML/DL models have been leveraged, including autoencoder [11, 15], LSTM [16], recurrent neural networks (RNNs) [14, 17], Bayesian [12], Feedforward Deep Neural Network (FFDNN) [13], Convolutional Neural Network (CNN) and LSTM [18], and GRU [19]. These studies consistently demonstrate that ML and DL models significantly enhance the accuracy of detecting normal and anomalous traffic [9].  
Since AI models require vast amounts of data for training, to effectively utilize these models in intrusion detection systems, we need to address two main challenges: feature selection and data imbalance [1].  
Feature selection is the process of identifying the most relevant features for use in building a machine learning model. The accuracy of ML-based methods is heavily influenced by the quality of the feature space [20, 21, 22, 23]. Therefore, developing efficient feature selection techniques is crucial for optimizing detection accuracy.  
Data imbalance is another significant challenge. IDS datasets often contain far fewer examples of malicious traffic compared to normal traffic. Training models on such imbalanced datasets can lead to poor detection performance [24]. Techniques such as random oversampling and the Synthetic Minority Oversampling Technique (SMOTE) are commonly used to create balanced datasets from imbalanced data, thereby improving model accuracy [25, 26, 27].  
While AI-based detection models have made significant strides, there remains a crucial need for robust non-AI-based solutions. The opaque nature of AI models, often referred to as \"black boxes\", can make it difficult to understand their decision-making processes. Non-AI approaches, based on well-defined rules and logic, provide greater explainability and transparency. These solutions can complement AI-based defenses by offering clear and understandable detection mechanisms, which are particularly valuable in scenarios like malware detection and identifying malicious behaviors.  
A comprehensive IDS should leverage a diverse set of tools, incorporating both AI and non-AI solutions. By understanding and integrating the strengths and limitations of each approach, we can develop a more resilient and effective defense against the continuously evolving threat landscape.  
This Special Issue [28] is dedicated to advancing the field of intrusion and malware detection across various network environments, including future Internet architectures, 5G and beyond wireless networks, enterprises, data centers, edge and cloud networks, software-defined networking (SDN), optical networks, and IoT-scale networks. From the 23 manuscripts submitted to this Special Issue, 10 were rigorously reviewed and accepted for publication. These contributions, listed below, reflect the diverse and innovative approaches in both AI and non-AI realms.