The rapid proliferation of Internet of Things (IoT) devices has introduced unprecedented security vulnerabilities, with botnet attacks representing persistent threats to network infrastructure. While machine learning-based intrusion detection systems (IDS) show promise in laborat...
Open access
Research Article10.9734/ajarr/2026/v20i31299
The rapid proliferation of Internet of Things (IoT) devices has introduced unprecedented security challenges, with network intrusion detection systems (NIDS) becoming critical for safeguarding IoT infrastructures. While traditional machine learning approaches have shown promise,...
Open access
Research Article10.9734/ajarr/2026/v20i21297
Cloud computing introduces significant security challenges, particularly sophisticated intrusion attempts that threaten data integrity and service availability. Traditional intrusion detection systems (IDS) prioritize detection accuracy while overlooking computational resource co...
Open access
Research Article10.9734/ajrcos/2026/v19i2827
The proliferation of Internet of Things (IoT) devices has created unprecedented security challenges, with intrusion detection systems (IDS) facing significant difficulties in accurately identifying diverse attack patterns within highly imbalanced network traffic datasets. Traditi...
Open access
Research Article10.9734/ajrcos/2026/v19i2825
The proliferation of Internet of Things (IoT) devices has introduced significant cybersecurity challenges, necessitating robust intrusion detection systems capable of identifying sophisticated attacks in resourceconstrained environments. This study presents a comprehensive compar...
Open access
Research Article10.9734/jerr/2026/v28i21812
This study addresses extreme class imbalance in network intrusion detection systems, a critical challenge where minority attack classes can constitute less than 0.001% of network traffic. Using the CICIDS2017 dataset with an initial imbalance ratio of 191,678:1, we developed an a...
Open access
Research Article10.9734/jerr/2026/v28i21810