Revolutionizing Malware Detection: A Paradigm Shift Through Optimized Convolutional Neural Networks

View Sample PDF

Author(s): Marwan Omar (Capitol Technology University, USA & Illinois Institute of Technology, USA)
Copyright: 2024
Pages: 25
Source title: Innovations, Securities, and Case Studies Across Healthcare, Business, and Technology
Source Author(s)/Editor(s): Darrell Norman Burrell (Marymount University, USA)
DOI: 10.4018/979-8-3693-1906-2.ch011

Keywords: Medical Information Science Reference / Medical Technologies / Medicine & Healthcare

Purchase

View Revolutionizing Malware Detection: A Paradigm Shift Through Optimized Convolutional Neural Networks on the publisher's website for pricing and purchasing information.

Abstract

Cybercrime has grown into a multi-billion dollar industry in recent years. Malware deployment is one of these cybercrimes' most common aspects. This malicious software has shown its ability to cripple large commercial organizations and collect significant financial tolls up to billions of dollars yearly. It targets a variety of industries, sectors, corporations, and even individual entities without discrimination. Malware writers continuously hone their techniques and raise the bar on their sophistication, creating difficult-to-detect malware that may be left unnoticed in the background for extended periods of time to get around security measures. The first accuracy rate of the baseline model is a phenomenal 98%. The accuracy of the CNN model increases to an astonishing 99.183% by increasing its complexity, outperforming the performance of the bulk of CNN models reported in the literature. This CNN model is used to forecast the appearance of new malware samples in our dataset, further demonstrating its effectiveness.

The IRMA Community

Research IRM

Revolutionizing Malware Detection: A Paradigm Shift Through Optimized Convolutional Neural Networks

Purchase

Abstract

Related Content

IRMA Sponsors