Robust and Effective Malware Detection Through Quantitative Data Flow Graph Metrics

Microsoft Word - Control Flow-based Malware Variant Detection - Final Version.docx

Static detection of malware variants plays an important role in system security and control flow has been shown as an effective characteristic that represents polymorphic malware. In our research, we propose a similarity search of malware to detect these variants using novel distance metrics. We describe a malware signature by the set of control flow graphs the malware contains. We first experi...

Malware Detection using Classification of Variable-Length Sequences

In this paper, a novel method based on the graph is proposed to classify the sequence of variable length as feature extraction. The proposed method overcomes the problems of the traditional graph with variable length of data, without fixing length of sequences, by determining the most frequent instructions and insertion the rest of instructions on the set of “other”, save speed and memory. Acco...

Annotated Control Flow Graph for Metamorphic Malware Detection

Metamorphism is a technique that mutates the binary code using different obfuscations and never keeps the same sequence of opcodes in the memory. This stealth technique provides the capability to a malware for evading detection by simple signature-based (such as instruction sequences, byte sequences and string signatures) anti-malware programs. In this paper, we present a new scheme named Annot...

Behavior-based Malware Detection with Quantitative Data Flow Analysis

Classification of Malware Using Structured Control Flow

Malware is a pervasive problem in distributed computer and network systems. Identification of malware variants provides great benefit in early detection. Control flow has been proposed as a characteristic that can be identified across variants, resulting in flowgraph based malware classification. Static analysis is widely used for the classification but can be ineffective if malware undergoes a...