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A review on challenges and future research directions for machine learning-based intrusion detection system
Research in the field of Intrusion Detection is focused on develo** an efficient strategy that
can identify network attacks. One of the important strategies is to supervise the network …
can identify network attacks. One of the important strategies is to supervise the network …
Machine learning for human emotion recognition: a comprehensive review
Emotion is an interdisciplinary research field investigated by many research areas such as
psychology, philosophy, computing, and others. Emotions influence how we make …
psychology, philosophy, computing, and others. Emotions influence how we make …
Feature selection for classification with Spearman's rank correlation coefficient-based self-information in divergence-based fuzzy rough sets
Feature selection facilitates uncertainty disposal and information mining, and it has received
widespread research interests. Divergence-based fuzzy rough sets (Div-FRSs), a new kind …
widespread research interests. Divergence-based fuzzy rough sets (Div-FRSs), a new kind …
[HTML][HTML] Recursive feature elimination with cross-validation with decision tree: Feature selection method for machine learning-based intrusion detection systems
The frequency of cyber-attacks on the Internet of Things (IoT) networks has significantly
increased in recent years. Anomaly-based network intrusion detection systems (NIDSs) offer …
increased in recent years. Anomaly-based network intrusion detection systems (NIDSs) offer …
Dynamic multi-scale topological representation for enhancing network intrusion detection
Network intrusion detection systems (NIDS) play a crucial role in maintaining network
security. However, current NIDS techniques tend to neglect the topological structures of …
security. However, current NIDS techniques tend to neglect the topological structures of …
[HTML][HTML] Dependable intrusion detection system using deep convolutional neural network: A novel framework and performance evaluation approach
Intrusion detection systems (IDS) play a critical role in safeguarding computer networks
against unauthorized access and malicious activities. However, traditional IDS approaches …
against unauthorized access and malicious activities. However, traditional IDS approaches …
Dugat-LSTM: Deep learning based network intrusion detection system using chaotic optimization strategy
Network intrusion is a huge harmful activity to the privacy of the data sharing network. The
activity will result in a cyber-attack, which causes damage to the system as well as the user's …
activity will result in a cyber-attack, which causes damage to the system as well as the user's …
Information gain ratio-based subfeature grou** empowers particle swarm optimization for feature selection
Feature selection is a critical preprocessing step in machine learning with significant real-
world applications. Despite the widespread use of particle swarm optimization (PSO) for …
world applications. Despite the widespread use of particle swarm optimization (PSO) for …
BrainNet: a fusion assisted novel optimal framework of residual blocks and stacked autoencoders for multimodal brain tumor classification
A significant issue in computer-aided diagnosis (CAD) for medical applications is brain
tumor classification. Radiologists could reliably detect tumors using machine learning …
tumor classification. Radiologists could reliably detect tumors using machine learning …
Real-time fusion multi-tier DNN-based collaborative IDPS with complementary features for secure UAV-enabled 6G networks
UAV-enabled Integrated Sensing and Communication (ISAC) in sixth-generation (6G)
wireless networks has sparked significant research interest. UAVs are positioned as aerial …
wireless networks has sparked significant research interest. UAVs are positioned as aerial …