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malware detection Using Dual Siamese Network Model

Computer Modeling in Engineering & Sciences 2024年第10期141卷 563-584页

作者： ByeongYeol An JeaHyuk Yang Seoyeon Kim Taeguen Kim Department of Software Convergence Soonchunhyang UniversityAsan31538Republic of Korea Department of Mobility Convergence Security Soonchunhyang UniversityAsan31538Republic of Korea Department of Information Security Engineering Soonchunhyang UniversityAsan31538Republic of Korea

This paper proposes a new approach to counter cyberattacks using the increasingly diverse malware in cyber *** signature detection methods that utilize static and dynamic features face limitations due to the continuous evolution and diversity of new ***,machine learning-based malware detection techniques,such as Convolutional Neural Networks(CNN)and Recurrent Neural Networks(RNN),have gained *** these methods demonstrate high performance by leveraging static and dynamic features,they are limited in detecting new malware or variants because they learn based on the characteristics of existing *** overcome these limitations,malware detection techniques employing One-Shot Learning and Few-Shot Learning have been *** on this,the Siamese Network,which can effectively learn from a small number of samples and perform predictions based on similarity rather than learning the characteristics of the input data,enables the detection of new malware or *** propose a dual Siamese network-based detection framework that utilizes byte images converted frommalware binary data to grayscale,and opcode frequency-based images generated after extracting opcodes and converting them into *** proposed framework integrates two independent Siamese network models,one learning from byte images and the other from opcode frequency-based *** detection models trained on the different kinds of images generated separately apply the L1 distancemeasure to the output vectors themodels generate,calculate the similarity,and then apply different weights to each *** proposed framework achieved a malware detection accuracy of 95.9%and 99.83%in the experimentsusingdifferentmalware *** experimental resultsdemonstrate that ourmalware detection model can effectively detect malware by utilizing two different types of features and employing the dual Siamese network-based model.

关键词： Siamese network malware detection few-shot learning

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malware detection in Android IoT Systems Using Deep Learning

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Computers, Materials & Continua 2023年第2期74卷 4399-4415页

作者： Muhammad Waqar Sabeeh Fareed Ajung Kim Saif Ur Rehman Malik Muhammad Imran Muhammad Usman Yaseen Department of Computer Science COMSATS UniversityIslamabad45550Pakistan Department of Optical Engineering Sejong UniversitySeoul143747Korea Cybernetica AS Tallinn12618Estonia

The Android Operating System(AOS)has been evolving since its inception and it has become one of the most widely used operating system for the Internet of Things(IoT).Due to the high popularity and reliability ofAOS for IoT,it is a target of many cyber-attacks which can cause compromise of privacy,financial loss,data integrity,unauthorized access,denial of services and so *** Android-based IoT(AIoT)devices are extremely vulnerable to various malwares due to the open nature and high acceptance of Android in the ***,several detection preventive malwares are developed to conceal their malicious activities from analysis ***,conventional malware detection techniques could not be applied and innovative countermeasures against such anti-detection malwares are indispensable to secure the *** this paper,we proposed the novel deep learning-based real-time multiclass malware detection techniques for the AIoT using dynamic *** results show that the proposed technique outperforms existing malware detection techniques and achieves detection accuracy up to 99.87%.

关键词： Android cyber security deep learning internet of things malware detection

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malware detection Using Decision Tree Based SVM Classifier for IoT

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Computers, Materials & Continua 2022年第7期72卷 713-726页

作者： Anwer Mustafa Hilal Siwar Ben Haj Hassine Souad Larabi-Marie-Sainte Nadhem Nemri Mohamed K.Nour Abdelwahed Motwakel Abu Sarwar Zamani Mesfer Al Duhayyim Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Computer Science College of Science and ArtsKing Khalid UniversityMahayil AsirSaudi Arabia Department of Computer Science College of Computer and Information SciencesPrince Sultan UniversitySaudi Arabia Department of Computer Science College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

The development in Information and Communication Technology has led to the evolution of new computing and communication *** revolution with Internet of Things(IoTs)has developed various applications in almost all domains from health care,education to entertainment with sensors and smart *** of the subsets of IoT is Internet of Medical things(IoMT)which connects medical devices,hardware and software applications through *** enables secure wireless communication over the Internet to allow efficient analysis of medical *** these smart advancements and exploitation of smart IoT devices in health care technology there increases threat and malware attacks during transmission of highly confidential medical *** work proposes a scheme by integrating machine learning approach and block chain technology to detect malware during data transmission in *** proposed Machine Learning based Block Chain Technology malware detection scheme(MLBCT-Mdetect)is implemented in three steps namely:feature extraction,Classification and *** extraction is performed by calculating the weight of each feature and reduces the features with less *** Vector Machine classifier is employed in the second step to classify the malware and benign ***,third step uses blockchain to store details of the selected features which eventually improves the detection of malware with significant improvement in speed and ***-BCT-Mdetect achieves higher accuracy with low false positive rate and higher True positive rate.

关键词： Blockchain malware detection classification feature selection internet of medical things

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An Android malware detection System Based on Feature Fusion

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Chinese Journal of Electronics 2018年第6期27卷 1206-1213页

作者： LI Jian WANG Zheng WANG Tao TANG Jinghao YANG Yuguang ZHOU Yihua School of Computer Science Beijing University of Posts and Telecommunications School of Computer Science Beijing University of Technology

In order to improve the detection efficiency of Android malicious application, an Android malware detection system based on feature fusion is proposed on three levels. Feature fusion especially emphasizes on ten categories, which combines static and dynamic features and includes 377 features for classification. In order to improve the accuracy of malware detection, attribute subset selection and principle component analysis are used to reduce the dimensionality of fusion features. Random forest is used for classification. In the experiment, the dataset includes 43,822 benign applications and 8,454 malicious applications. The method can achieve 99.4% detection accuracy and 0.6% false positive rate. The experimental results show that the detection method can improve the malware detection efficiency in Android platform.

关键词： Android security malware detection Feature fusion Machine learning Information security

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DroidEnemy: Battling adversarial example attacks for Android malware detection

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Digital Communications and Networks 2022年第6期8卷 1040-1047页

作者： Neha Bala Aemun Ahmar Wenjia Li Fernanda Tovar Arpit Battu Prachi Bambarkar Department of Computer Science New York Institute of TechnologyNew YorkNY10023USA

In recent years,we have witnessed a surge in mobile devices such as smartphones,tablets,smart watches,etc.,most of which are based on the Android operating ***,because these Android-based mobile devices are becoming increasingly popular,they are now the primary target of mobile malware,which could lead to both privacy leakage and property *** address the rapidly deteriorating security issues caused by mobile malware,various research efforts have been made to develop novel and effective detection mechanisms to identify and combat ***,in order to avoid being caught by these malware detection mechanisms,malware authors are inclined to initiate adversarial example attacks by tampering with mobile *** this paper,several types of adversarial example attacks are investigated and a feasible approach is proposed to fight against ***,we look at adversarial example attacks on the Android system and prior solutions that have been proposed to address these ***,we specifically focus on the data poisoning attack and evasion attack models,which may mutate various application features,such as API calls,permissions and the class label,to produce adversarial ***,we propose and design a malware detection approach that is resistant to adversarial *** observe and investigate how the malware detection system is influenced by the adversarial example attacks,we conduct experiments on some real Android application datasets which are composed of both malware and benign *** results clearly indicate that the performance of Android malware detection is severely degraded when facing adversarial example attacks.

关键词： Security malware detection Adversarial example attack Data poisoning attack Evasi on attack Machine learning Android

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Android malware detection Method Based on Permission Complement and API Calls

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Chinese Journal of Electronics 2022年第4期31卷 773-785页

作者： YANG Jiyun TANG Jiang YAN Ran XIANG Tao College of Computer Science Chongqing University

The dynamic code loading mechanism of the Android system allows an application to load executable files externally at runtime. This mechanism makes the development of applications more convenient, but it also brings security issues. Applications that hide malicious behavior in the external file by dynamic code loading are becoming a new challenge for Android malware detection. To overcome this challenge, based on dynamic code loading mechanisms, three types of threat models,i.e. Model I, Model II, and Model III are defined. For the Model I type malware, its malicious behavior occurs in Dex Code, so the application programming interface(API)classes were used to characterize the behavior of the DexCode file. For the Model II type and Model III type malwares whose malicious behaviors occur in an external file,the permission complement is defined to characterize the behaviors of the external file. Based on permission complement and API calls, an Android malicious application detection method is proposed, of which feature sets are constructed by improving a feature selection method. Five datasets containing 15,581 samples are used to evaluate the performance of the proposed method. The experimental results show that our detection method achieves accuracy of 99.885% on general dataset, and performes the best on all evaluation metrics on all datasets in all comparison methods.

关键词： Android malware detection Dynamic code loading Permission complement

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Enhancing PDF malware detection through Logistic Model Trees

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Computers, Materials & Continua 2024年第3期78卷 3645-3663页

作者： Muhammad Binsawad Department of Information Systems King Abdulaziz UniversityP.O.Box 80217Jeddah21589Saudi Arabia

malware is an ever-present and dynamic threat to networks and computer systems in cybersecurity,and because of its complexity and evasiveness,it is challenging to identify using traditional signature-based detection *** study article discusses the growing danger to cybersecurity that malware hidden in PDF files poses,highlighting the shortcomings of conventional detection techniques and the difficulties presented by adversarial *** article presents a new method that improves PDF virus detection by using document analysis and a Logistic Model *** a dataset from the Canadian Institute for Cybersecurity,a comparative analysis is carried out with well-known machine learning models,such as Credal Decision Tree,Naïve Bayes,Average One Dependency Estimator,Locally Weighted Learning,and Stochastic Gradient *** traditional structural and JavaScript-centric PDF analysis,the research makes a substantial contribution to the area by boosting precision and resilience in malware *** use of Logistic Model Tree,a thorough feature selection approach,and increased focus on PDF file attributes all contribute to the efficiency of PDF virus *** paper emphasizes Logistic Model Tree’s critical role in tackling increasing cybersecurity threats and proposes a viable answer to practical issues in the *** results reveal that the Logistic Model Tree is superior,with improved accuracy of 97.46%when compared to benchmark models,demonstrating its usefulness in addressing the ever-changing threat landscape.

关键词： malware detection PDF files logistic model tree feature selection cybersecurity

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MobileNet-Based IoT malware detection with Opcode Features

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Journal of Communications and Information Networks 2023年第3期8卷 221-230页

作者： Changren Mai Riqing Liao Jing Ren Yuanxiang Gong Kaibo Zhang Chiya Zhang Harbin Institute of Technology Shenzhen 518055China University of Electronic Science and Technology of China Chengdu 611731China

In recent years,with the rapid development of Internet and hardware technologies,the number of Internet of things(IoT)devices has grown ***,IoT devices are constrained by power consumption,making the security of IoT *** such as Botnets and Worms poses significant security threats to users and enterprises *** learning models have demonstrated strong performance in various tasks across different domains,leading to their application in malicious software ***,due to the power constraints of IoT devices,the well-performanced large models are not suitable for IoT malware *** this paper we propose a malware detection method based on Markov images and MobileNet,offering a cost-effective,efficient,and high-performing solution for malware ***,this paper innovatively analyzes the robustness of opcode sequences.

关键词： deep learning feature fusion malware detection opcode lightweight model

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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 *** is a software code developed by cybercriminals for damage purposes,such as corrupting the system and data as well as stealing sensitive *** damage caused by malware is substantially increasing every *** is a need to detect malware efficiently and automatically and remove threats quickly from the *** there are various approaches to tackle malware problems,their prevalence and stealthiness necessitate an effective method for the detection and prevention of malware *** deep learning-based approach is recently gaining attention as a suitable method that effectively detects *** this paper,a novel approach based on deep learning for detecting malware ***,the proposed approach deploys novel feature selection,feature co-relation,and feature representations to significantly reduce the feature *** proposed approach has been evaluated using a Microsoft prediction dataset with samples of 21,736 malware composed of 9 malware *** 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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Clustering-Aided Supervised malware detection with Specialized Classifiers and Early Consensus

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Computers, Materials & Continua 2023年第4期75卷 1235-1251页

作者： Murat Dener Sercan Gulburun Information Security Engineering Graduate School of Natural and Applied SciencesGazi UniversityAnkara06560Turkey

One of the most common types of threats to the digital world is malicious *** is of great importance to detect and prevent existing and new malware before it damages information *** learning approaches are used effectively for this *** this study,we present a model in which supervised and unsupervised learning algorithms are used *** is used to enhance the prediction performance of the supervised *** aim of the proposed model is to make predictions in the shortest possible time with high accuracy and f1 *** the first stage of the model,the data are clustered with the k-means *** the second stage,the prediction is made with the combination of the classifier with the best prediction performance for the related *** choosing the best classifiers for the given clusters,triple combinations of ten machine learning algorithms(kernel support vector machine,k-nearest neighbor,naive Bayes,decision tree,random forest,extra gradient boosting,categorical boosting,adaptive boosting,extra trees,and gradient boosting)are *** selected triple classifier combination is positioned in two *** prediction time of the model is improved by positioning the classifier with the slowest prediction time in the second *** selected triple classifier combination is positioned in two *** prediction time of the model is improved by positioning the classifier with the highest prediction time in the second *** is seen that clustering before classification improves prediction performance,which is presented using Blue Hexagon Open Dataset for malware Analysis(BODMAS),Elastic malware Benchmark for Empowering Researchers(EMBER)2018 and Kaggle malware detection *** model has 99.74%accuracy and 99.77%f1 score for the BODMAS dataset,99.04%accuracy and 98.63%f1 score for the Kaggle malware detection dataset,and 96.77%accuracy and 96.77%f1 score for the EMBER 2018 *** addition,the tiered posi

关键词： malware detection ensemble learning classification clustering specialized classifier early consensus

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