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Malware Behavior Analysis Applying Machine Learning

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A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Security and Privacy".

Deadline for manuscript submissions: closed (20 December 2023) | Viewed by 5655

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Special Issue Editors

Dr. Alexander Perez-Pons

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Guest Editor

Electrical and Computer Engineering, Florida International University, Miami, FL, USA
Interests: cybersecurity; machine learning; embedded systems; malware behavioral analysis; network analysis; virtualization
Special Issues, Collections and Topics in MDPI journals

Dr. Upadhyay Himanshu

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Guest Editor

Department of Electrical and Computer Engineering, Florida International University, Miami, FL 33174, USA
Interests: quantum computing; artificial intelligence; machine learning; deep learning; big data; visualization; cybersecurity; advanced cyber analytics; memory forensics
Special Issues, Collections and Topics in MDPI journals

Dr. Tushar Bhardwaj

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Guest Editor

Postdoctoral Associate, Applied Research Center, Florida International University, Miami, FL, USA
Interests: cyber security; cloud computing; machine learning; deep learning; memory forensic; virtualization

Special Issue Information

Dear Colleagues,

When it comes to the proliferation of malware, targeting every expanding number of interconnected devices and systems, the figures are daunting, having spread exponentially considering that 560,000 new malware are detected daily, with about a billion malware in existence. These infected systems can range from various types of endpoint devices and network appliances, such as the IoT and routers. In an effort to combat these cybersecurity threats, various detection techniques have been employed, from signature-based and heuristics to now more behavioral approaches such as sandboxing and intelligent solutions at the system or network domain. Traditional static and dynamic analyses support these protection techniques to identify and classify different malwares, their capabilities and intentions. However, the next generation of malware behavioral analysis aims at leveraging the capabilities of machine learning to more rapidly and effectively gain greater insight to overcome many of the challenges encountered with current approaches.

This Special Issue seeks contributions reporting on recent advancements concerning malware behavior analyses using machine learning capabilities, automation approaches, knowledge extraction, behavioral information sources and introspection techniques. This includes novel technologies deploying behavioral analyses using machine learning and a discussion regarding the deployment of novel applications and frameworks. Moreover, the Special Issue also considers AI-based applications to deploy and manage next-generation malware behavioral analyses in virtualized and cloud computing environments. The basic idea is to propose new approaches and ideas and present applications of innovative approaches employing machine learning. Topics of interest include, but are not limited to, the following:

Trends and challenges for malware analysis;
Virtualization environments;
Data analysis techniques;
Behavioral data extraction, type and manner;
Analysis frameworks;
Machine learning and deep learning performance;
Model efficiency and accuracy;
Algorithms and data encoders;
Malware and anomaly detection;
Virtual memory forensic;
Cloud security;
Trustworthy AI in cybersecurity;
Machine learning applications in the cyber security domain;
Image classification and anomaly detection;
Threat hunting.

Dr. Alexander Perez-Pons
Dr. Upadhyay Himanshu
Dr. Tushar Bhardwaj
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

malware behavioral analysis
machine learning
deep learning
anomaly detection
cybersecurity

Published Papers (2 papers)

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Research

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19 pages, 3074 KiB

Open AccessArticle

An Efficient Malware Classification Method Based on the AIFS-IDL and Multi-Feature Fusion

by Xuan Wu and Yafei Song

Information 2022, 13(12), 571; https://doi.org/10.3390/info13120571 - 9 Dec 2022

Viewed by 1558

Abstract

In recent years, the presence of malware has been growing exponentially, resulting in enormous demand for efficient malware classification methods. However, the existing machine learning-based classifiers have high false positive rates and cannot effectively classify malware variants, packers, and obfuscation. To address this shortcoming, this paper proposes an efficient deep learning-based method named AIFS-IDL (Atanassov Intuitionistic Fuzzy Sets-Integrated Deep Learning), which uses static features to classify malware. The proposed method first extracts six types of features from the disassembly and byte files and then fuses them to solve the single-feature problem in traditional classification methods. Next, Atanassov’s intuitionistic fuzzy set-based method is used to integrate the result of the three deep learning models, namely, GRU (Temporal Convolutional Network), TCN (Temporal Convolutional Network), and CNN (Convolutional Neural Networks), which improves the classification accuracy and generalizability of the classification model. The proposed method is verified by experiments and the results show that the proposed method can effectively improve the accuracy of malware classification compared to the existing methods. Experiments were carried out on the six types of features of malicious code and compared with traditional classification algorithms and ensemble learning algorithms. A variety of comparative experiments show that the classification accuracy rate of integrating multi-feature, multi-model aspects can reach 99.92%. The results show that, compared with other static classification methods, this method has better malware identification and classification ability. Full article

(This article belongs to the Special Issue Malware Behavior Analysis Applying Machine Learning)

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Review

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38 pages, 585 KiB

Open AccessReview

A Survey and Evaluation of Android-Based Malware Evasion Techniques and Detection Frameworks

by Parvez Faruki, Rati Bhan, Vinesh Jain, Sajal Bhatia, Nour El Madhoun and Rajendra Pamula

Information 2023, 14(7), 374; https://doi.org/10.3390/info14070374 - 30 Jun 2023

Cited by 7 | Viewed by 2616

Abstract

Android platform security is an active area of research where malware detection techniques continuously evolve to identify novel malware and improve the timely and accurate detection of existing malware. Adversaries are constantly in charge of employing innovative techniques to avoid or prolong malware detection effectively. Past studies have shown that malware detection systems are susceptible to evasion attacks where adversaries can successfully bypass the existing security defenses and deliver the malware to the target system without being detected. The evolution of escape-resistant systems is an open research problem. This paper presents a detailed taxonomy and evaluation of Android-based malware evasion techniques deployed to circumvent malware detection. The study characterizes such evasion techniques into two broad categories, polymorphism and metamorphism, and analyses techniques used for stealth malware detection based on the malware’s unique characteristics. Furthermore, the article also presents a qualitative and systematic comparison of evasion detection frameworks and their detection methodologies for Android-based malware. Finally, the survey discusses open-ended questions and potential future directions for continued research in mobile malware detection. Full article

(This article belongs to the Special Issue Malware Behavior Analysis Applying Machine Learning)

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