477 citations · 565 across the 17 of their papers we have counts for
6 papers · 1 filter
MalGrid: Visualization Of Binary Features In Large Malware Corpora
Tajuddin Manhar Mohammed, Lakshmanan Nataraj, Satish Chikkagoudar +2
The number of malware is constantly on the rise. Though most new malware are modifications of existing ones, their sheer number is quite overwhelming. In this paper, we present a n…
OMD: Orthogonal Malware Detection Using Audio, Image, and Static Features
Lakshmanan Nataraj, Tajuddin Manhar Mohammed, Tejaswi Nanjundaswamy +3
With the growing number of malware and cyber attacks, there is a need for "orthogonal" cyber defense approaches, which are complementary to existing methods by detecting unique mal…
HAPSSA: Holistic Approach to PDF Malware Detection Using Signal and Statistical Analysis
Tajuddin Manhar Mohammed, Lakshmanan Nataraj, Satish Chikkagoudar +2
Malicious PDF documents present a serious threat to various security organizations that require modern threat intelligence platforms to effectively analyze and characterize the ide…
Attribution of Gradient Based Adversarial Attacks for Reverse Engineering of Deceptions
Michael Goebel, Jason Bunk, Srinjoy Chattopadhyay +3
Machine Learning (ML) algorithms are susceptible to adversarial attacks and deception both during training and deployment. Automatic reverse engineering of the toolchains behind th…
Malware Detection Using Frequency Domain-Based Image Visualization and Deep Learning
Tajuddin Manhar Mohammed, Lakshmanan Nataraj, Satish Chikkagoudar +2
We propose a novel method to detect and visualize malware through image classification. The executable binaries are represented as grayscale images obtained from the count of N-gra…
SPAM: Signal Processing to Analyze Malware
Lakshmanan Nataraj, B. S. Manjunath
In this article, we explored orthogonal methods to analyze malware motivated by signal and image processing. Malware samples are represented as images or signals. Image and signal-…