恶意软件
计算机科学
卷积神经网络
人工智能
深度学习
学习迁移
灰度
模式识别(心理学)
上下文图像分类
机器学习
卷积(计算机科学)
人工神经网络
数据挖掘
图像(数学)
计算机安全
作者
Dipendra Pant,Rabindra Bista
标识
DOI:10.1145/3503047.3503081
摘要
Malware classification is a major challenge as they have multiple families and its type has been ever increasing. With the involvement of deep learning and the availability of massive data, neural networks can easily address this problem. This experimental work focuses on classifying the malware that are in the form of grayscale images into their respective families with high accuracy and low loss. We used transfer learning in a pretrained VGG16 model obtaining an accuracy of 88.40% of accuracy. Additionally, upon experimenting with the ResNet-18, InceptionV3 model to classify the malware images into their families didn't give better results as compared to our custom model. The custom model based on convolution neural network achieved better accuracy and was able to classify malware with 98.7% validation accuracy. Upon comparing our custom model with VGG16, ResNet-18, InceptionV3 the custom model gave better accuracy with a better f1 score of 0.99. But improper tuning of VGG16 yielded low accuracy and high parameter loss. In overall the approach of classifying malware by converting them into images and classifying thus obtained images makes the malware classification problem easier.
科研通智能强力驱动
Strongly Powered by AbleSci AI