Open AccessReview Paper on A Solution to Detecting Botnets using Convolutional Neural Networks and Support Vector Machine Algorithms
Author(s) -
Vipul Jha,
Omkar Katule,
Tanvi Bajad,
Shreyas Agadi,
Priyanka Bendale
Publication year - 2022
Publication title -
international journal of advanced research in science, communication and technology
Language(s) - English
Resource type - Journals
ISSN - 2581-9429
DOI - 10.48175/ijarsct-3287
Subject(s) - botnet , malware , computer science , support vector machine , convolutional neural network , artificial intelligence , trojan , machine learning , variety (cybernetics) , the internet , software , blocking (statistics) , computer security , data mining , algorithm , computer network , world wide web , operating system
A botnet is a network of Internet-connected devices and nodes that transmit malware software, typically installed by Trojan horses, viruses and worms. Many methods for detecting or blocking mobile malware have recently been developed. However, our model differs from ones that have been developed earlier. We are working with a dataset we found on the Kaggle site. Machine learning techniques such as CNN and SVM have been used to derive the results we have achieved. We have a variety of cases that are labelled as attack or not attack, as well as any subtypes that may exist. The suggested system is a web-based tool that provides reliable App/URL botnet prediction.