Malware Classification using Deep Neural Networks: Performance Evaluation and Applications in Edge Devices

2023-08-21 · Akhil M R, Adithya Krishna V Sharma, Harivardhan Swamy, Pavan A, Ashray Shetty, Anirudh B Sathyanarayana

With the increasing extent of malware attacks in the present day along with the difficulty in detecting modern malware, it is necessary to evaluate the effectiveness and performance of Deep Neural Networks (DNNs) for malware classification. Multiple DNN architectures can be designed and trained to detect and classify malware binaries. Results demonstrate the potential of DNNs in accurately classifying malware with high accuracy rates observed across different malware types. Additionally, the feasibility of deploying these DNN models on edge devices to enable real-time classification, particularly in resource-constrained scenarios proves to be integral to large IoT systems. By optimizing model architectures and leveraging edge computing capabilities, the proposed methodologies achieve efficient performance even with limited resources. This study contributes to advancing malware detection techniques and emphasizes the significance of integrating cybersecurity measures for the early detection of malware and further preventing the adverse effects caused by such attacks. Optimal considerations regarding the distribution of security tasks to edge devices are addressed to ensure that the integrity and availability of large scale IoT systems are not compromised due to malware attacks, advocating for a more resilient and secure digital ecosystem.

📄 PDF Abstract BibTeX arXiv:2310.06841

Code (0)

등록된 구현이 없습니다.

Tasks

Edge-computingMalware Classification Malware Detection

MALOnt: An Ontology for Malware Threat Intelligence

2020-06-20 · Nidhi Rastogi, Sharmishtha Dutta, Mohammed J. Zaki, Alex Gittens 외

Malware threat intelligence uncovers deep information about malware, threat actors, and their tactics, Indicators of Compromise(IoC), and vulnerabilities in different platforms from scattered threat sources. This collect…

Decision Making Graph Generation Knowledge Graphs

EMBER2024 -- A Benchmark Dataset for Holistic Evaluation of Malware Classifiers

2025-06-05 · Robert J. Joyce, Gideon Miller, Phil Roth, Richard Zak 외

A lack of accessible data has historically restricted malware analysis research, and practitioners have relied heavily on datasets provided by industry sources to advance. Existing public datasets are limited by narrow s…

Malware Analysis Malware Classification Malware Detection

MalMixer: Few-Shot Malware Classification with Retrieval-Augmented Semi-Supervised Learning

2024-09-20 · Jiliang Li, Yifan Zhang, Yu Huang, Kevin Leach

Recent growth and proliferation of malware have tested practitioners ability to promptly classify new samples according to malware families. In contrast to labor-intensive reverse engineering efforts, machine learning ap…

Data Augmentation Malware Classification Retrieval

Imbalanced malware classification: an approach based on dynamic classifier selection

2025-03-30 · J. V. S. Souza, C. B. Vieira, G. D. C. Cavalcanti, R. M. O. Cruz

In recent years, the rise of cyber threats has emphasized the need for robust malware detection systems, especially on mobile devices. Malware, which targets vulnerabilities in devices and user data, represents a substan…

Malware Classification Malware Detection

ShielDroid: A Hybrid Approach Integrating Machine and Deep Learning for Android Malware Detection

2026-08-04 · Md Faisal Ahmed, Zarin Tasnim Biash, Abu Raihan Shakil, Ahmed Ann Noor Ryen 외 arxiv

The rapid advancement of modern technology has led to a significant increase in the use of smart devices, such as smartphones and tablets, resulting in the widespread adoption of mobile applications. Although application…

Malware Detection