ModDiff: Modularity Similarity-Based Malware Homologation Detection

Article Computer Science, Theory & Methods

A Survey of Binary Code Similarity

Irfan Ul Haq et al.

Summary: This article presents the first survey of binary code similarity, analyzing 70 approaches and discussing the scope, origin, development, and challenges in this field.

ACM COMPUTING SURVEYS (2022)

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Article Computer Science, Information Systems

An Automated Vision-Based Deep Learning Model for Efficient Detection of Android Malware Attacks

Iman Almomani et al.

Summary: This paper introduces an efficient and automated vision-based Android malware detection model that utilizes optimized CNN algorithms to accurately and quickly recognize malware attacks. Through experiments with color and grayscale images, the model achieved detection accuracy of 99.40% and 98.05%.

IEEE ACCESS (2022)

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Article Computer Science, Information Systems

DeMal: Module decomposition of malware based on community discovery

Yuyao Huang et al.

Summary: This paper proposes a solution called DeMal that automates the analysis of malware program components using community discovery methods. By recovering program call relationships, extracting structure-related attributes, and applying a combination model of multiple community discovery algorithms, DeMal can predict the code composition structure of malicious software. In experiments, DeMal performs well with an average F1 score of 71.3% and 14.5% of samples achieving an average precision of 90%. The analysis time for each sample is about 19.79 seconds. DeMal also demonstrates scalability on common programs and large-scale performance, and the visualization of results showcases its module decomposition capabilities.

COMPUTERS & SECURITY (2022)

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Article Computer Science, Software Engineering

Software Module Clustering: An In-Depth Literature Analysis

Qusay Sarhan et al.

Summary: This study reviews 143 research papers on software module clustering to investigate various aspects of clustering methods, applications, processes, algorithms, and evaluation methods. Researchers discuss research gaps and challenges in this field, providing a useful reference for future studies.

IEEE TRANSACTIONS ON SOFTWARE ENGINEERING (2022)

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Article Multidisciplinary Sciences

Android malware analysis in a nutshell

Iman Almomani et al.

Summary: This paper presents a comprehensive analysis model for android malware, highlighting the essential factors influencing the analysis results of vision-based android malware. The study conducted a deep empirical research, utilizing 22 CNN algorithms and two benchmark android malware datasets. The results emphasize the significant impact of these factors on the performance of malware analysis solutions.

PLOS ONE (2022)

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Proceedings Paper Computer Science, Software Engineering

ModX: Binary Level Partially Imported Third-Party Library Detection via Program Modularization and Semantic Matching

Can Yang et al.

Summary: With the rapid growth of software, the use of third-party libraries has become increasingly popular, presenting both convenience and management challenges. To detect and understand these libraries in software, a framework called ModX has been proposed to achieve semantic-level detection. Experimental results show that ModX outperforms other tools in terms of module quality and TPL detection.

2022 ACM/IEEE 44TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING (ICSE 2022) (2022)

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Article Chemistry, Multidisciplinary

Visualized Malware Multi-Classification Framework Using Fine-Tuned CNN-Based Transfer Learning Models

Walid El-Shafai et al.

Summary: The continuous growth in malicious software development poses significant security threats, prompting researchers to explore efficient malware detection methods using Transfer Learning and Fine-Tuning. By utilizing fine-tuned Convolutional Neural Network models, a visualized multi-classification framework is proposed to address challenges with false positives and imbalanced datasets, achieving high accuracy in detecting malware types.

APPLIED SCIENCES-BASEL (2021)

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Proceedings Paper Computer Science, Information Systems

PALMTREE: Learning an Assembly Language Model for Instruction Embedding

Xuezixiang Li et al.

Summary: The paper introduces an assembly language model PALMTREE for generating general-purpose instruction embeddings through self-supervised training on large-scale unlabeled binary corpora, overcoming existing issues and producing high-quality representations. It outperforms other instruction embedding schemes for all downstream tasks.

CCS '21: PROCEEDINGS OF THE 2021 ACM SIGSAC CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY (2021)

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Article Engineering, Electrical & Electronic

Software Vulnerability Detection Using Deep Neural Networks: A Survey

Guanjun Lin et al.

PROCEEDINGS OF THE IEEE (2020)

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Proceedings Paper Computer Science, Information Systems

DEEPBINDIFF: Learning Program-Wide Code Representations for Binary Diffing

Yue Duan et al.

27TH ANNUAL NETWORK AND DISTRIBUTED SYSTEM SECURITY SYMPOSIUM (NDSS 2020) (2020)

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Proceedings Paper Computer Science, Software Engineering

Revisiting the Challenges and Opportunities in Software Plagiarism Detection

Xi Xu et al.

PROCEEDINGS OF THE 2020 IEEE 27TH INTERNATIONAL CONFERENCE ON SOFTWARE ANALYSIS, EVOLUTION, AND REENGINEERING (SANER '20) (2020)

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Article Computer Science, Software Engineering

Accurate and Scalable Cross-Architecture Cross-OS Binary Code Search with Emulation

Yinxing Xue et al.

IEEE TRANSACTIONS ON SOFTWARE ENGINEERING (2019)

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Proceedings Paper Computer Science, Software Engineering

Software Clusterings with Vector Semantics and the Call Graph

Marios Papachristou

ESEC/FSE'2019: PROCEEDINGS OF THE 2019 27TH ACM JOINT MEETING ON EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING (2019)

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Proceedings Paper Computer Science, Theory & Methods