6 research outputs found
Malware Detection using Artificial Bee Colony Algorithm
Malware detection has become a challenging task due to the increase in the
number of malware families. Universal malware detection algorithms that can
detect all the malware families are needed to make the whole process feasible.
However, the more universal an algorithm is, the higher number of feature
dimensions it needs to work with, and that inevitably causes the emerging
problem of Curse of Dimensionality (CoD). Besides, it is also difficult to make
this solution work due to the real-time behavior of malware analysis. In this
paper, we address this problem and aim to propose a feature selection based
malware detection algorithm using an evolutionary algorithm that is referred to
as Artificial Bee Colony (ABC). The proposed algorithm enables researchers to
decrease the feature dimension and as a result, boost the process of malware
detection. The experimental results reveal that the proposed method outperforms
the state-of-the-art
3D-model ShapeNet Core Classification using Meta-Semantic Learning
Understanding 3D point cloud models for learning purposes has become an
imperative challenge for real-world identification such as autonomous driving
systems. A wide variety of solutions using deep learning have been proposed for
point cloud segmentation, object detection, and classification. These methods,
however, often require a considerable number of model parameters and are
computationally expensive. We study a semantic dimension of given 3D data
points and propose an efficient method called Meta-Semantic Learning
(Meta-SeL). Meta-SeL is an integrated framework that leverages two input 3D
local points (input 3D models and part-segmentation labels), providing a time
and cost-efficient, and precise projection model for a number of 3D recognition
tasks. The results indicate that Meta-SeL yields competitive performance in
comparison with other complex state-of-the-art work. Moreover, being random
shuffle invariant, Meta-SeL is resilient to translation as well as jittering
noise.Comment: The 6th International Conference on Applied Cognitive Computin