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Keyword: Malware Detection
An Evaluation of some Machine Learning Algorithms for the detection of Android Applications Malware
Android Operating system (OS) has been used much more than all other mobile phone’s OS turning android OS to a major point of attack. Android Application installation serves as a major avenue through which attacks can be perpetrated. Permissions must be first granted by the users seeking to install these third-party applications. Some permissions can…
Read More iDRP Framework: An Intelligent Malware Exploration Framework for Big Data and Internet of Things (IoT) Ecosystem
The Internet of Things (IoT) is at a face paced growth in the advanced Industrial Revolution (IR) 4.0 in the modern digital world. Considering the current network security challenges and sophistication of attacks in the heavily computerized and interconnected systems, such as an IoT ecosystem, the need for an innovative, robust, intelligent and adaptive malware…
Read More Malware Classification Using XGboost-Gradient Boosted Decision Tree
In this industry 4.0 and digital era, we are more dependent on the use of communication and various transaction such as financial, exchange of information by various means. These transaction needs to be secure. Differentiation between the use of benign and malware is one way to make these transactions secure. We propose in this work…
Read More Malware Classification Based on System Call Sequences Using Deep Learning
Malware has always been a big problem for companies, government agencies, and individuals because people still use it as a primary tool to influence networks, applications, and computer operating systems to gain unilateral benefits. Until now, malware detection with heuristic and signature-based methods are still struggling to keep up with the evolution of malware. Machine…
Read More Virtual Memory Introspection Framework for Cyber Threat Detection in Virtual Environment
In today’s information based world, it is increasingly important to safeguard the data owned by any organization, be it intellectual property or personal information. With ever increasing sophistication of malware, it is imperative to come up with an automated and advanced methods of attack vector recognition and isolation. Existing methods are not dynamic enough to…
Read More Detecting Malicious Assembly using Convolutional, Recurrent Neural Networks
We present findings on classifying the class of executable code using convolutional, re- current neural networks by creating images from only the .text section of executables and dividing them into standard-size windows, using minimal preprocessing. We achieve up to 98.24% testing accuracy on classifying 9 types of malware, and 99.50% testing accuracy on classifying malicious…
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