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MMALE—A Methodology for malware analysis in Linux Environments

Computers, Materials & Continua 2021年第5期67卷 1447-1469页

作者： JoséJavier de Vicente Mohino Javier Bermejo Higuera Juan Ramón Bermejo Higuera Juan Antonio Sicilia Montalvo Manuel Sánchez Rubio JoséJavier Martínez Herraiz Escuela Superior de Ingeniería y Tecnología Universidad Internacional de La RiojaLogroño26006La RiojaSpain Departamento de Ciencias de la Computación de la Escuela Politécnica Superior Universidad de Alcaláde HenaresAlcaláde HenaresMadridSpain

In a computer environment,an operating system is prone to malware,and even the Linux operating system is not an exception.In recent years,malware has evolved,and attackers have become more qualified compared to a few years ago.Furthermore,Linux-based systems have become more attractive to cybercriminals because of the increasing use of the Linux operating system in web servers and Internet of Things(IoT)devices.Windows is the most employed OS,so most of the research efforts have been focused on its malware protection rather than on other operating systems.As a result,hundreds of research articles,documents,and methodologies dedicated to malware analysis have been reported.However,there has not been much literature concerning Linux security and protection from malware.To address all these new challenges,it is necessary to develop a methodology that can standardize the required steps to perform the malware analysis in depth.A systematic analysis process makes the difference between good and ordinary malware analyses.Additionally,a deep malware comprehension can yield a faster and much more efficient malware eradication.In order to address all mentioned challenges,this article proposed a methodology for malware analysis in the Linux operating system,which is a traditionally overlooked field compared to the other operating systems.The proposed methodology is tested by a specific Linux malware,and the obtained test results have high effectiveness in malware detection.

关键词： malware analysis methodology analysis Linux malware IoT malware

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Android malware Detection Using ResNet-50 Stacking

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Computers, Materials & Continua 2023年第2期74卷 3997-4014页

作者： Lojain Nahhas Marwan Albahar Abdullah Alammari Anca Jurcut Department of Computer Science Umm Al Qura UniversityP.O.Box 715MeccaSaudi Arabia Curriculums and Teaching Department Faculty of EducationUmm Al Qura UniversityP.O.Box 715MeccaSaudi Arabia School of Computer Science University College DublinBelfieldDublinIreland

There has been an increase in attacks on mobile devices,such as smartphones and tablets,due to their growing popularity.Mobile malware is one of the most dangerous threats,causing both security breaches and financial losses.Mobile malware is likely to continue to evolve and proliferate to carry out a variety of cybercrimes on mobile devices.Mobile malware specifically targets Android operating system as it has grown in popularity.The rapid proliferation of Android malware apps poses a significant security risk to users,making static and manual analysis of malicious files difficult.Therefore,efficient identification and classification of Androidmalicious files is crucial.Several ConvolutionalNeuralNetwork(CNN)basedmethods have been proposed in this regard;however,there is still room for performance improvement.In this work,we propose a transfer learning and stacking approach to efficiently detect the Android malware files by utilizing two wellknown machine learning models,ResNet-50 and Support Vector Machine(SVM).The proposed model is trained on the DREBIN dataset by transforming malicious APK files into grayscale images.Our model yields higher performance measures than state-of-the-art works on the DREBIN dataset,where the reported measures are accuracy,recall,precision,and F1 measures of 97.8%,95.8%,95.7%,and 95.7%,respectively.

关键词： Androidmalware convolutional neural network malware analysis malware classification image classification support vector machine

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A Novel Framework for Windows malware Detection Using a Deep Learning Approach

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Computers, Materials & Continua 2022年第7期72卷 461-479页

作者： Abdulbasit A.Darem Northern Border University Arar9280Saudi Arabia

Malicious software(malware)is one of the main cyber threats that organizations and Internet users are currently facing.malware is a software code developed by cybercriminals for damage purposes,such as corrupting the system and data as well as stealing sensitive data.The damage caused by malware is substantially increasing every day.There is a need to detect malware efficiently and automatically and remove threats quickly from the systems.Although there are various approaches to tackle malware problems,their prevalence and stealthiness necessitate an effective method for the detection and prevention of malware attacks.The deep learning-based approach is recently gaining attention as a suitable method that effectively detects malware.In this paper,a novel approach based on deep learning for detecting malware proposed.Furthermore,the proposed approach deploys novel feature selection,feature co-relation,and feature representations to significantly reduce the feature space.The proposed approach has been evaluated using a Microsoft prediction dataset with samples of 21,736 malware composed of 9 malware families.It achieved 96.01%accuracy and outperformed the existing techniques of malware detection.

关键词： malware detection malware analysis deep learning feature extraction feature selection cyber security

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malware incident response(IR) informed by cyber threat intelligence(CTI)

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Science China(Information Sciences) 2022年第7期65卷 256-258页

作者： Ying HE Ellis INGLUT Cunjin LUO School of Computer Science and Informatics De Montfort University Key Lab of Medical Electrophysiology Ministry of Education The Health Informatics Group Institute of Cardiovascular Research Southwest Medical University School of Computer Science and Engineering Northeastern University

Dear editor, Security experts have been fighting against cybercriminals for many years and existing research shows that this battle will continue. Malicious software has no remorse when it targets different organizations, regardless of its forms [1]. Ransomware [2] has caused serious issues in different industries, especially in healthcare. The existing report shows that 34% of ransomware is targeting healthcare organizations. Nowadays, criminals prefer crypto-jacking over ransomware (which also relies on cryptocurrency for anonymous ransom payments).

关键词： malware analysis Cyber Threat Intelligence Crypto Jacking Incident Response malware Incident Response

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Privacy Petri Net and Privacy Leak Software

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Journal of Computer Science & Technology 2015年第6期30卷 1318-1343页

作者：范乐君王元卓李静远程学旗林闯 National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing 100029 China Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Department of Computer Science and Technology Tsinghua University Beijing 100083 China

Private information leak behavior has been widely discovered in malware and suspicious applications. We refer to such software as privacy leak software （PLS）. Nowadays, PLS has become a serious and challenging problem to cyber security. Previous methodologies are of two categories： one focuses on the outbound network traffic of the applications; the other dives into the inside information flow of the applications. We present an abstract model called Privacy Petri Net （PPN） which is more applicable to various applications and more intuitive and vivid to users. We apply our approach to both malware and suspicious applications in real world. The experimental result shows that our approach can effectively find categories, content, procedure, destination and severity of the private information leaks for the target software.

关键词： privacy Petri net privacy leak software privacy function private information malware analysis

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malware Detection Using Deep Learning

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Open Journal of Applied Sciences 2023年第12期13卷 2480-2491页

作者： Achi Harrisson Thiziers Koné Tiémoman N’guessan Behou Gérard Traoré Tiémoko Qouddouss Kabir Unité de Recherche et d’Expertise Numérique (UREN) Université Virtuelle de C&#244te d’Ivoire Abidjan C&#244te d’Ivoire

malware represents a real threat to information systems, because of the damage it causes. This threat is growing today, as these programs take on more complex forms. This means they escape traditional malware detection methods. Hence the need for artificial intelligence, more specifically Deep Learning, which could detect malware more effectively. In this article, we’ve proposed a model for malware detection using artificial neural networks. Our approach used data from the characteristics of machines, particularly computers, to train our Deep Learning algorithm. This model demonstrated an accuracy of around 83% in predicting the presence of malware on a machine. Thus, the use of artificial neural networks for malware detection has shown his ability to assimilate complex, non-linear patterns from data.

关键词： Neural Network ANNs Malicious Code malware analysis Artificial Intelligence

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基于污点和概率的逃逸恶意软件多路径探索

Security and Safety

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Security and Safety 2023年第3期2卷 83-106页

作者：徐钫洲张网羌卫中金海 National Engineering Research Center for Big Data Technology and System Wuhan 430074China Services Computing Technology and System Lab Cluster and Grid Computing LabWuhan 430074China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data SecurityWuhan 430074China School of Cyber Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Jinyinhu Laboratory Wuhan 430040China

Static analysis is often impeded by malware obfuscation techniques,such as encryption and packing,whereas dynamic analysis tends to be more resistant to obfuscation by leveraging concrete execution information.Unfortunately,malware can employ evasive techniques to detect the analysis environment and alter its behavior accordingly.While known evasive techniques can be explicitly dismantled,the challenge lies in generically dismantling evasions without full knowledge of their conditions or implementations,such as logic bombs that rely on uncertain conditions,let alone unsupported evasive techniques,which contain evasions without corresponding dismantling strategies and those leveraging unknown implementations.In this paper,we present Antitoxin,a prototype for automatically exploring evasive malware.Antitoxin utilizes multi-path exploration guided by taint analysis and probability calculations to effectively dismantle evasive techniques.The probabilities of branch execution are derived from dynamic coverage,while taint analysis helps identify paths associated with evasive techniques that rely on uncertain conditions.Subsequently,Antitoxin prioritizes branches with lower execution probabilities and those influenced by taint analysis for multi-path exploration.This is achieved through forced execution,which forcefully sets the outcomes of branches on selected paths.Additionally,Antitoxin employs active anti-evasion countermeasures to dismantle known evasive techniques,thereby reducing exploration overhead.Furthermore,Antitoxin provides valuable insights into sensitive behaviors,facilitating deeper manual analysis.Our experiments on a set of highly evasive samples demonstrate that Antitoxin can effectively dismantle evasive techniques in a generic manner.The probability calculations guide the multi-path exploration of evasions without requiring prior knowledge of their conditions or implementations,enabling the dismantling of unsupported techniques such as C2 and significantly improving efficiency compared to linear ex

关键词： malware analysis dynamic binary instrumentation forced execution taint analysis evasion detection

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