MalSee

MalSee: Multidimensional Malware Signal Fingerprinting

Mayachitra has pioneered a novel approach to malware analysis following the seminal work by the co-founder Dr. Manjunath and Dr. Nataraj (“Malware Images: Visualization and Automatic Classification”). This work introduced the idea of converting malware binaries into grayscale images to exploit visual patterns for malware family classification. The paper demonstrated that malware from the same family produces remarkably similar image textures and layouts, enabling high-accuracy classification without any code disassembly or execution.

Core Idea – Signal-Based Fingerprinting

The key innovation: Treating a computer binary as a raw digital signal that can be transformed into alternative domains (such as images or audio) for pattern recognition. For example, a binary file’s bytes can be interpreted as pixel intensity values to form a 2D image (often called a “byteplot” image). Similarly, the binary can be read as a 1D waveform (8-bit audio signal) and analyzed using audio processing techniques. In both cases, distinguishing features – whether visual textures or audio-frequency patterns – act as a “malware fingerprint.” These fingerprints enable detection of malware and classification into families via standard signal processing and machine learning methods. Notably, this approach requires no platform-specific disassembly or sandbox execution, bypassing many limitations of traditional static and dynamic analysis.

Platform-Agnostic Pattern Recognition

A key strength of this method is its platform and format agnosticism. Since it relies only on the raw byte content, it can be applied to executables from any operating system or file format (Windows PE, Linux ELF, Android APK, etc.) with minimal adjustment. The technique has shown effective results across a wide range of malware types and variants, including packed or obfuscated samples. By avoiding OS-dependent features and focusing on inherent byte patterns, the signal-based approach generalizes across different environments. This means analysts can use a unified image/audio analysis pipeline for malware targeting diverse systems, greatly enhancing versatility compared to traditional signatures or heuristics tied to one platform.

Advancements – Frequency Domain Analysis

Building on the original image-based visualization, later research introduced frequency-domain signal processing to enrich malware fingerprints. One example is generating multiple representations of a binary — e.g., a spatial byteplot image and a frequency-transformed image — and fusing their features for detection. In one patented approach, the byteplot image is complemented by a second image created from the binary’s byte bi-gram frequency spectrum using a Discrete Cosine Transform (DCT), capturing structural patterns not obvious in the raw bytes. Deep neural networks then analyze these images (and even a third, bigram-count image) to improve classification accuracy, detecting malware as “malicious or benign” with higher robustness. Such innovations highlight the extensibility of Manjunath’s signal-processing paradigm for malware: by applying different transforms (image, audio, frequency) to the same binary data, one can glean orthogonal features that together strengthen malware detection.

Government and Industry Adoption

Defense R&D and Lab Usage

Mayachitra’s binary-to-signal malware fingerprinting has been embraced by U.S. government research agencies as a promising cybersecurity innovation. MalSee is noted in the FY2021 Federal Cybersecurity R&D Strategic Plan Implementation Roadmap as a key program for advanced malware detection. This roadmap highlights MalSee among ONR’s cybersecurity R&D programs, underscoring the technique as an important research thrust for federal defense laboratories. The platform-agnostic nature of the method aligns well with defense needs to tackle malware across various systems, and its success in academic trials has led to adoption in practical settings.

Industry and Broader Impact

Beyond government, the concept of malware binary visualization has inspired numerous cybersecurity companies and researchers. Security tools now commonly incorporate features to visualize binary content or to feed such images into machine learning models for malware classification. The original Malimg dataset released by Manjunath’s team (over 9,000 malware images across dozens of families) has become a benchmarking standard in the industry. Companies in antivirus and threat intelligence have also cited the 2011 “malware images” approach as a foundation for next-generation detection techniques.

Patents from this Innovation

“Malware Classification and Detection Using Audio Descriptors” (U.S. Patent 11,244,050, issued Feb 2022): Inventors: Lakshmanan Nataraj, Bangalore S. Manjunath, Shivkumar Chandrasekaran. This patent discloses treating malware bytes as a 1D audio signal and extracting acoustic features for classification. Notably, it also covers using image signal representations or a combination of audio and image features to determine if a file is malicious. The patented system reads a malware binary as a sequence of audio samples or pixels, then applies signal feature extraction (e.g., spectral analysis for audio, texture analysis for images) to compute a malware “fingerprint.” By classifying these fingerprints, the system can label unknown files as malware or benign without executing the code. This invention is significant as it broadens malware analysis beyond conventional code analysis by using auditory patterns as a new modality for detecting malicious code.

“Malware Detection Using Frequency Domain-Based Image Visualization and Deep Learning” (U.S. Patent Application 20220311782A1, filed Mar 2021): Inventors: Tajuddin Manhar Mohammed, Lakshmanan Nataraj, Bangalore S. Manjunath, Shivkumar Chandrasekaran. This patent extends image-based malware classification into the frequency domain. It describes a malware visualization system that generates two or more images from each binary: (1) a byteplot image obtained by mapping the file’s bytes to pixels, and (2) a frequency-spectrum image created by computing byte bi-gram frequencies and applying a Discrete Cosine Transform (DCT). These images capture complementary characteristics of the malware. A deep neural network then analyzes them (concatenating features from both the spatial-domain and frequency-domain representations) to decide if the file is malware, benign, or unknown. By using multiple signal transformations of the binary, this approach improves detection accuracy and robustness against adversarial evasion.

“Orthogonal Malware Detection Using Multi-Domain Signal Analysis” (related patent application): Inventors: Lakshmanan Nataraj, Michael Gene Goebel, Bangalore S. Manjunath, Shivkumar Chandrasekaran. In addition to the above, Manjunath’s team has explored an “orthogonal malware detection (OMD)” strategy combining different signal modalities. This builds on the idea that analyzing a binary as both an image and an audio signal yields independent indicators of malware, which together strengthen confidence. For example, an unknown executable can be simultaneously subjected to image classification (to observe structural code patterns) and audio-based anomaly detection (to capture entropy and opcode frequency patterns as audio frequencies). A fusion of these results leads to improved accuracy over any single view. This multi-domain analysis approach has been a focus of patent filings by Mayachitra Inc., ensuring that the intellectual property around joint image/audio malware fingerprints is protected. It underscores the novelty of transforming binaries into multiple orthogonal signals for cybersecurity applications.

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