91 citations · 232 across the 5 of their papers we have counts for
6 papers
A Novel Active Learning Approach to Label One Million Unknown Malware Variants
Ahmed Bensaoud, Jugal Kalita
Active learning for classification seeks to reduce the cost of labeling samples by finding unlabeled examples about which the current model is least certain and sending them to an…
Optimized detection of cyber-attacks on IoT networks via hybrid deep learning models
Ahmed Bensaoud, Jugal Kalita
The rapid expansion of Internet of Things (IoT) devices has increased the risk of cyber-attacks, making effective detection essential for securing IoT networks. This work introduce…
A Survey of Malware Detection Using Deep Learning
Ahmed Bensaoud, Jugal Kalita, Mahmoud Bensaoud
The problem of malicious software (malware) detection and classification is a complex task, and there is no perfect approach. There is still a lot of work to be done. Unlike most o…
Deep Multi-Task Learning for Malware Image Classification
Ahmed Bensaoud, Jugal Kalita
Malicious software is a pernicious global problem. A novel multi-task learning framework is proposed in this paper for malware image classification for accurate and fast malware de…
CNN-LSTM and Transfer Learning Models for Malware Classification based on Opcodes and API Calls
Ahmed Bensaoud, Jugal Kalita
In this paper, we propose a novel model for a malware classification system based on Application Programming Interface (API) calls and opcodes, to improve classification accuracy.…
Classifying Malware Images with Convolutional Neural Network Models
Ahmed Bensaoud, Nawaf Abudawaood, Jugal Kalita
Due to increasing threats from malicious software (malware) in both number and complexity, researchers have developed approaches to automatic detection and classification of malwar…