most citedA Dependable Hybrid Machine Learning Model for Network Intrusion Detection

173 citations · 192 across the 5 of their papers we have counts for

collaborators

5 papers

eess.IV202413 cited

Hybridized Convolutional Neural Networks and Long Short-Term Memory for Improved Alzheimer's Disease Diagnosis from MRI Scans

Maleka Khatun, Md Manowarul Islam, Habibur Rahman Rifat +3

Brain-related diseases are more sensitive than other diseases due to several factors, including the complexity of surgical procedures, high costs, and other challenges. Alzheimer's…

q-bio.GN20242 cited

Exploring Gene Regulatory Interaction Networks and predicting therapeutic molecules for Hypopharyngeal Cancer and EGFR-mutated lung adenocarcinoma

Abanti Bhattacharjya, Md Manowarul Islam, Md Ashraf Uddin +7

With the advent of Information technology, the Bioinformatics research field is becoming increasingly attractive to researchers and academicians. The recent development of various…

cs.CR20241 cited

MLSTL-WSN: Machine Learning-based Intrusion Detection using SMOTETomek in WSNs

Md. Alamin Talukder, Selina Sharmin, Md Ashraf Uddin +2

Wireless Sensor Networks (WSNs) play a pivotal role as infrastructures, encompassing both stationary and mobile sensors. These sensors self-organize and establish multi-hop connect…

cs.CR20243 cited

Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extraction

Md. Alamin Talukder, Md. Manowarul Islam, Md Ashraf Uddin +4

Cybersecurity has emerged as a critical global concern. Intrusion Detection Systems (IDS) play a critical role in protecting interconnected networks by detecting malicious actors a…

cs.CR2023173 cited

A Dependable Hybrid Machine Learning Model for Network Intrusion Detection

Md. Alamin Talukder, Khondokar Fida Hasan, Md. Manowarul Islam +5

Network intrusion detection systems (NIDSs) play an important role in computer network security. There are several detection mechanisms where anomaly-based automated detection outp…