This chapter focuses on online malware detection techniques in cloud IaaS using machine learning and discuss comparative analysis on the performance metrics of various deep learning...

It is crucial to detect and classify malware accurately to prevent potential security breaches. This project focuses on leveraging the power of CNNs, a deep learning technique commonly used in computer vision tasks, to classify malware samples into different categories.

In this chapter, we consider malware classification using deep learning techniques and image-based features. We employ a wide variety of deep learning techniques, including multilayer perceptrons (MLP), convolutional neural networks (CNN), long short-term memory (LSTM), and gated recurrent units (GRU).

Dec 16, 2020 · Automatic behaviour-based malware detection using machine learning algorithms is thus considered a game-changing innovation. Threats are automatically evaluated based on their behaviours...

Jan 1, 2024 · Our dataset included 29,298 samples encompassing various malware families and benign instances. The proposed framework uses six different types of machine learning algorithms, namely Logistic Regression, Support Vector Machine, K-Nearest Neighbor, Random Forest, Naive Bayes, and Decision Tree for the classification of malware.

Aug 31, 2023 · Detecting malware is done by analysing malware signatures and behaviour patterns statically and dynamically. It has been proven that these methods are ineffective and time-consuming when it...

4 days ago · The traditional malware detection methods fail to operate properly and yield high false positive rates with low accuracy of the protection system. This study explores the ways in which malware can be detected using these machine learning (ML) and deep learning (DL) approaches to address those shortcomings.

We can define malware as piece of code or malicious code that harm the data or device. here, we made a alternative of virus detection by using machine learning techniques and created a dataset and used machine learning algorithms for categorizing the file into malicious or not and compared their results to determine the best algorithm suiting ...

Apr 24, 2025 · Digital systems find it challenging to keep up with cybersecurity threats. The daily emergence of more than 560,000 new malware strains poses significant hazards to the digital ecosystem. The traditional malware detection methods fail to operate properly and yield high false positive rates with low accuracy of the protection system. This study explores the ways in which malware can be detected ...

Our method for malware detection uses different machine learning algorithms such as decision tree, random forest etc. The algorithm which has the maximum accuracy gets selected which provides a great detection ratio for the system.