Boosting Malware Detection with AlexNet and Optimized Neural Networks Using the Grasshopper Algorithm

That risk is compounded as more critical infrastructure and systems are being managed by computers in general, connected over the Internet. To combat such nefarious software that can steal data and do a number of other privatively outcomes, you need to be very vigilant and also train all our artificial intelligent tools not just to find the malware per se but all the countless other ways in which meddlers might find their way into your computer or set off some enormously disruptive chain reaction (or series thereof). In this research, it is offering a potent new technique by integration of innovative neural network method with conventional artificial intelligence tool known as "multilayer perceptron (MLP)". For the detection of 25 distinct categories of malwares it is obtained 99.84% accuracy rate in classification through in this hybrid system.

Authors

Institutions

Publication Details

Journal
Wasit Journal of Computer and Mathematics Science
Published
2025-06-30
DOI
https://doi.org/10.31185/wjcms.303
Primary Topic
Network Security and Intrusion Detection
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Boosting Malware Detection with AlexNet and Optimized Neural Networks Using the Grasshopper Algorithm

Mohammed Aswad
Wasit Journal of Computer and Mathematics Science
Network Security and Intrusion Detection
article

Boosting Malware Detection with AlexNet and Optimized Neural Networks Using the Grasshopper Algorithm

Mohammed Aswad
article en

Abstract

That risk is compounded as more critical infrastructure and systems are being managed by computers in general, connected over the Internet. To combat such nefarious software that can steal data and do a number of other privatively outcomes, you need to be very vigilant and also train all our artificial intelligent tools not just to find the malware per se but all the countless other ways in which meddlers might find their way into your computer or set off some enormously disruptive chain reaction (or series thereof). In this research, it is offering a potent new technique by integration of innovative neural network method with conventional artificial intelligence tool known as "multilayer perceptron (MLP)". For the detection of 25 distinct categories of malwares it is obtained 99.84% accuracy rate in classification through in this hybrid system.

Wasit Journal of Computer and Mathematics ScienceVol. 4(2)
Al-Furat Al-Awsat Technical University (IQ)
Openalex Percentile: Top 80%
Network Security and Intrusion Detection
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.