Convolutional neural network Archives - Program Studi S1 Informatika

MDC-Net: Intelligent Malware Detection and Classification using Extreme Learning Machine

by V. S. K. ReddyI. NagarajuMantri GayatriR. RDileep PRevathy P

In the progress of digital era, there is a significant rise in the number of security flaws that are the result of assaults by malicious software, sometimes known as malware. Malware detection is still a popular area of study owing to the fact that a significant number of computer users, organizations, and governments have been impacted by the exponential development in malicious software assaults. These detection methods rely on malware signatures and behavioral patterns. Recent malwares make use of metamorphic, polymorphic, and other evasive strategies to rapidly modify their behavior and produce a huge number of new malwares. These tactics allow the malware to avoid detection. Recent years have seen an increase in the use of Machine Learning Algorithms (MLA) for the purpose of effective malware analysis. The majority of these newly discovered malwares are variations of previously discovered malware. However, the conventional models failed to classify the different types of malwares and resulted in poor classification performance. So, this work focused on implementation of Malware Detection and Classification (MDC-Net). Initially, dataset preprocessing is performed to remove the missing symbols, unknown data from MALIMG dataset. Then, convolutional neural network (CNN) model is used for extraction malware class dependent, and malware class specific features. Finally, Extreme Learning Machine (ELM) model trained with the CNN features. So, the ELM model can capable of classifying the various malware classes from every new data. The MDC-Net resulted in superior performance than existing approaches in terms various metrics.

Convolutional neural networks: an overview and application in radiology

by R Yamashita, M Nishio, RKG Do, K Togashi

Insights into imaging

Convolutional neural network (CNN), a class of artificial neural networks that has become dominant in various computer vision tasks, is attracting interest across a variety of domains, including radiology. CNN is designed to automatically and adaptively learn spatial hierarchies of features through backpropagation by using multiple building blocks, such as convolution layers, pooling layers, and fully connected layers. This review article offers a perspective on the basic concepts of CNN and its application to various radiological tasks, and discusses its challenges and future directions in the field of radiology. Two challenges in applying CNN to radiological tasks, small dataset and overfitting, will also be covered in this article, as well as techniques to minimize them. Being familiar with the concepts and advantages, as well as limitations, of CNN is essential to leverage its potential in diagnostic radiology, with the goal of augmenting the performance of radiologists and improving patient care.

Key Points

• Convolutional neural network is a class of deep learning methods which has become dominant in various computer vision tasks and is attracting interest across a variety of domains, including radiology.

• Convolutional neural network is composed of multiple building blocks, such as convolution layers, pooling layers, and fully connected layers, and is designed to automatically and adaptively learn spatial hierarchies of features through a backpropagation algorithm.

• Familiarity with the concepts and advantages, as well as limitations, of convolutional neural network is essential to leverage its potential to improve radiologist performance and, eventually, patient care.

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